Estimating scene parameters like geometry and material solely from images has been a widely researched topic, especially in the domain of computer vision. It is also of interest for a large industry including game development [5][2], movie production [10] as well as projects concerned with cultural heritage [6], as it enables them to create assets that faithfully capture objects of the real world. While there are well established methods for geometric reconstruction that are often able to leverage implicit knowledge about the camera setup, material reconstruction is a more ill-conditioned inverse problem. Recent advances in machine learning and computer graphics enable data-driven approaches that rely on light transport simulations for appearance-based material reconstruction. Differentiable renderers are an integral part of methods that use gradient-based optimization as they allow differentiating the rendering operation with respect to arbitrary scene parameters. Often, these renderers are handcrafted for the specific use case and deeply integrated into the processing pipeline. Thus, they are inflexible and lack reusability. On the other hand, there are stand-alone differentiable renderers, which provide a general framework for advanced light transport simulations. In this project, we will as a proof-of-concept replace the custom renderer embedded in an existing material reflectance pipeline with a general differentiable renderer. We show that integrating an external renderer in such a pipeline requires low effort if certain flexibility is given by the implementation and that parts of the renderer can be easily adapted to the specific use case if required.
Visualization of the SVBRDF arguments.
In computer graphics, surface reflectance properties are typically represented by a bidirectional reflectance distribution function (BRDF) \( f_r(\mathbf{\omega}_i, \mathbf{\omega}_o) \). It defines the fraction of (differential) irradiance coming from a direction \(\mathbf{\omega}_i\) that is reflected towards a direction \(\mathbf{\omega}_o\) [14]. As most surfaces are not fully homogeneous, the BRDF is usually given as a function of surface position \(f_r(\mathbf{x}, \mathbf{\omega}_i, \mathbf{\omega}_o)\), which is then also called spatially varying BRDF (SVBRDF). Figure 1 shows a visualization of the involved arguments. Note that the directional quantities in this function are relative to the local coordinate system that is defined by the surface normal \(\mathbf{n}\). Additionally, a physically correct BRDF fulfills two properties [14]:
The physically-based Cook-Torrance model
[17] assumes that
a surface is composed of small mirror-like facets
(microfacets) and models a BRDF as sum
of two components. The diffuse component \(f_d\)
captures effects such as internal scattering or
surface interreflections.
Since we assume lambertian reflectivity,
it takes the form
\[
f_d = \frac{\rho_d}{\pi},
\]
and only depends on the diffuse albedo \(\rho_d\)
at the position \(\mathbf{x}\).
The specular component
\[
f_s = \frac{F \cdot D \cdot G }{4 (\mathbf{\omega}_i \cdot \mathbf{n})(\mathbf{\omega}_o \cdot \mathbf{n})}
\]
is dependent on the directions of incoming and outgoing light and
captures explicit reflectance effects that are not caused by surface penetration.
The component is modular in the Cook-Torrance model and different
functions can be plugged in for the Fresnel term \(F\),
the microfacet normal distribution \(D\) and the geometric
shadowing function \(G\). In this project, we use
Schlick's approximation
[16] for the Fresnel term,
which depends on the specular albedo
or specular reflectance at normal incidence
\(\rho_s\).
We use GGX [18] as microfacet
normal distribution and the associated Smith geometric
shadowing function. Both depend on the roughness
of the material \(r\).
Note that this model only handles isotropic
specular reflections, so it cannot be used to simulate
materials like brushed metals.
We assume that the reflectivity of any material encountered in this project can be represented by the Cook-Torrance model and fully parametrized by the diffuse albedo \(\rho_d\), the specular albedo \(\rho_s\) and the roughness \(r\). We also assume that surfaces are represented by triangle meshes. Variations of the parameters over a (mesh) surface are captured in separate texture maps. Additionally, a normal map provides the orientation of the microscopic surface normal \(n\) in tangent space. Figure 2 shows examples of the SVBRDF parameter maps for a surface.
(a) Normals
(b) Diffuse albedo
(c) Roughness
(d) Specular albedo
SVBRDF maps containing the Cook-Torrance parameters of a surface.
Deschaintre et al. [7] propose a method for reconstructing material reflectance of a flat surface only given a flash-lit photograph of it. While the imposed constraints are rather strict, we find the light-weight nature of the process very appealing. They cast material reflectance estimation as an image-to-image translation problem: an image is fed into a deep convolutional network which predicts the Cook-Torrance SVBRDF maps of the visible surface. Their follow-up work [8] extends the idea by using multiple uncalibrated but aligned images of the same surface (this is where the teaser image is taken from). While we initially planned to base the whole project on the multi-view approach, time constraints and lower overall complexity lead us to focus only on the single-view case. However, some ideas and the implementation reference are mainly borrowed from the multi-view approach.
At the heart of the presented approach is a deep convolutional network that adopts the U-Net architecture [15] which has shown to be suitable for other image-to-image translation problems. The input to the network is a conventional three channel RGB image of the surface and the output is a nine channel image, where two channels represent the per-pixel normal (the z coordinate is inferred from the x and y coordinates), three channels represent the diffuse albedo, one channel represents the roughness and the last three channels represent the specular albedo. As this reflectance representation is conformant to the Cook-Torrance model (see section Material Model), the approach intrinsically assumes that the surface can be represented by it.
Overview of the network architecture. It consists of a U-Net (blue) augmented by a parallel global track (green).
As seen in the coarse overview in Figure 3, the U-Net architecture is composed of two successive parts: an encoder and a decoder. The encoder consists of eight convolutional layers with feature counts 64, 128, 256, 512, 512, 512, 512, 512 that gradually reduce the spatial size of the image (using a stride of two) while increasing the number of features. The bottleneck is followed by the decoder which again consists of eight layers that perform spatial upsampling followed by two convolutions. The decoding layers have similar feature counts as the encoding layers, only in reversed order with the last decoding layer having a feature count of nine to match the output format. In order to better reconstruct fine details that possibly have been lost in the bottleneck, each decoding layer is connected to the output of the same sized encoding layer through a skip connection. Leaky ReLUs with a negative slope of 0.2 serve as activation functions in these layers. The full-sized 9 channel decoder output is passed through a final tanh activation. The result is transformed into a valid SVBRDF by computing the z coordinates of normals from the given x and y coordinates as well as by bringing the diffuse albedo, specular albedo and roughness to the range [0, 1].
In order to stabilize training, the network performs instance normalization between each convolutional layer and its preceeding activation function. However, the authors found that this normalization reduces the network's ability to maintain non-local information effectively. They showed that even a simple task such as predicting a constant colored diffuse albedo can impose a significant challenge on such an architecture. Their proposed solution is a second network parallel to the main encoder-decoder track that explicitly propagates global information. Figure 4 shows a detailed visualization of the information exchange between the main track (in the encoding part) and the global track. The per-channel means that are subtracted by the instance normalization flow into the global track. They are concatenated with the previous feature vector, transformed by a fully connected layer and then pass through a SELU activation function. In the other direction, the previous feature vector is transformed by another fully connected layer and then added to the feature maps after instance normalization. Using this second track, the network is able to propagate non-local information as part of a global feature vector.
Interaction between the main track and the global track.
Note that specific details about the architecture of the single-view network are mainly extracted from the publicly available code of the multi-view approach. The single-view model (or at least a variation of it called 'Generator') is a subset of the multi-view model. There might be subtle differences between the model used in the single-view work and the single-view model presented here.
Similar to other learning-based approaches, data is an integral part of this one aswell. In order to train the network presented in the previous section, several sample pairs of inputs and outputs, i.e., flash-lit images of the surface and their respective SVBRDF maps, are required. Since the latter is difficult to obtain for real world surfaces in the necessary quantity, the authors chose to use artificial training data that is representative for a large variety of real materials. More specifically, they obtained a set of 1850 samples (SVBRDF maps) by selecting 155 materials from Allegorithmic Substance Share (a dataset of artist-generated procedural materials) and permutating their important procedural parameters. In practice, we found their publicly available dataset to contain not 1850 but only 1628 samples: 1590 for training and 38 for testing.
Given an artificial material sample, the corresponding input image is generated by rendering a material patch placed in a virtual scene that contains one light source. The camera and light positions above the patch are randomly varied between samples to capture different alignments of camera lense and flash position as well as camera field of views. Additionally, variations of white balance, flash intensity and flash colors are simulated. The final image is then post-processed by adding Gaussian noise, clipping radiance and gamma encoding in an attempt to mimic common image degradations. Figure 5 shows three material samples and their corresponding rendered input images.
Examples of the four SVBRDF maps (left) and the artificial input image generated from each of them (right).
Since the number of materials obtained this way is still too small, the authors propose to increase the variety by mixing randomly selected pairs of materials. Specifically, they combine different SVBRDF maps using alpha blending with a randomly chosen alpha value. The result of such a mixing operation can be seen in Figure 6. While they argue that the results stay close to real-world materials, it can be seen that the blend of certain material classes (e.g. stone and metal) does not produce physically plausible results. We still adopt this technique in order to increase the variety of unique samples and shading effects in the dataset.
Example of an SVBRDF that is a mixture of two materials (left) and the generated input image (right).
The single-view work uses the above mentioned techniques to generate a fixed dataset offline, i.e., all material variations are precomputed and saved together with their renderings as individual files. The dataset contains roughly 200000 samples having a size of over 80 GB. The multi-view work pursues a lazy approach, where material variations and input renderings are generated at load time. This results in a significantly reduced dataset size of roughly 1 GB, as only the different base material samples have to be provided. For this project, we also use the latter approach and thus the dataset provided with the multi-view work.
One important ingredient for training the network is the choice of training objective, i.e., the loss function. In the concret scenario, the loss function has to compare SVBRDF maps predicted by the network to the ground truth maps belonging to the input image. The obvious and most intuitive choice would be simple losses like \(l_1\) or \(l_2\) which compare the SVBRDF maps pixel-wise like multi-channel images. However, as the authors note, the current parametrization of the SVBRDF as Cook-Torrance maps is rather arbitrary and the magnitude of numerical differences between two parametrizations is only a weak indicator for their visual difference. They instead propose a loss function, that compares the appearance of two SVBRDFs, independent of their specific parametrization. This rendering loss renders both the ground truth SVBRDF as well as the estimated SVBRDF as flat material patches under varying viewing and lighting conditions and only compares the rendered images pixel-wise. More precisely, it computes the \(l_1\) loss between the log-transformed renderings. Figure 7 shows the difference of using only a simple \(l_1\) loss between SVBRDF maps and the rendering loss.
Faithfully comparing two SVBRDFs at each training step would require rendering them under a large amount of fixed light and camera positions to cover all possible angles. Instead, the authors went for a stochastic approach where only a semi-randomly sampled subset of nine configurations is evaluated at each step. In the first three configurations, the light and camera position are independently sampled from a cosine distribution over the upper hemisphere above the material patch. The remaining six configurations are intended to explicitly cover specular configurations, that appear less often when performing independent sampling but potentially carry rich information about the material in the appearing specular highlights. These configurations are obtained by only randomly sampling the view position and then using the mirrored vector as light position. By introducing a random shift and distance offsets, specular highlights of various sizes and positions on the material patch can be generated.
SVBRDF maps and renderings for the ground truth (top), prediction of a model trained only with \(l_1\) loss between the SVBRDF maps (middle) and a model trained with the rendering loss (bottom). Images and figure adopted from [7].
While the single-view work only relies on the rendering loss, the multi-view work complements the loss function by individual \(l_1\) losses between parts of the SVBRDF maps. The mixed loss is defined as \[L \triangleq L_\text{R} + 0.1 (L_\text{Normal} + L_\text{Diffuse} + L_\text{Roughness} + L_\text{Specular})\] where \(L_\text{R}\) is the rendering loss and \(L_\text{Normal}\), \(L_\text{Diffuse}\), \(L_\text{Roughness}\) and \(L_\text{Specular}\) are \(l_1\) losses between the normal maps, diffuse albedo maps, roughness maps and specular albedo maps, respectively. We also chose to adapt this mixed loss formulation for our loss function as the authors report this small supervision to stabilize the training.
While there is a publicly available implementation of the multi-view work, there is none of the single-view one, unfortunately. This as well as the better flexibility of a custom implementation and for the sake of gaining practical experience in the project, motivated us to reimplement the presented method. As mentioned earlier, the implementation does not strictly follow the single-view work, but can be thought of as a crossover between both methods, as it adopts aspects of either one. Since the backend of potential renderers to integrate was known beforehand, the reimplementation is based on PyTorch as opposed to the original work which uses TensorFlow. By exporting our reimplemented PyTorch models with the library TensorboardX, we were able to visually inspect them in the Tensorboard web interface and verify their architectures by comparing them to the reference models.
Difference between a perspective rendering and the patch sampling approach for a scene with static light (top) and a scene with moving light (bottom).
In contrast to conventional forward renderers that, given a scene, synthesize an image, the renderer used for computing the rendering loss has to support a backward pass. More precisely, since training the network with backpropagation requires computing the gradients of all operations contributing to the loss function, the rendering itself has to be differentiable with respect to the scene parameters (in this case the SVBRDF maps). Renderers that allow differentiation through the rendering operation are called differentiable renderers.
Similar to other works (e.g. [1] or [9]), the presented approach relies on a renderer that is directly integrated into the network architecture. It uses the same framework to formulate the rendering steps as is used for implementing and training the models. As pointed out by the authors, frameworks like TensorFlow or PyTorch provide certain advantages when implementating a differentiable renderer, like automatic GPU acceleration and differentiation. Similar to the models themselves we also reimplemented the renderer in PyTorch and used it for both, generating artificial input images as well as evaluating the rendering loss.
When comparing the renderer output to traditional renderings, it is noticeable that even when the location of the camera is not immediatly above the material patch, there is no perspective distortion. This results from the renderer operating in the coordinate space of the patch as opposed to traditionally operating in the coordinate space of the image plane in front of the camera. When thinking in terms of rays that originate from the camera center, each pixel in the resulting image corresponds to a ray that samples a specific point on the material patch. The animations in Figure 8 visualize the difference between this patch sampling approach and a conventional perspective rendering. The technique is advantageous when computing the rendering loss, as it only produces pixels that depend on the SVBRDF parameters. In contrast, a perspective rendering, would most likely contain pixels that do not show the material patch, thus have a gradient of zero.
Highly specular materials rendered without ambient or environment light appear mostly dark.
While we tried to follow the original in-network renderer as close as possible, ours still differs from it in some details (mainly the Cook-Torrance implementation) and two major points. First, we do not simulate the flash as spot light with angular fall-off, but rather use a simple point light with radial fall-off. This was done for simplicity in our implementation but also for compatibility with the renderer to be integrated. It does not directly pose a problem to the rendering loss but rather affects the input image generation as it is meant to produce artificial flash-lit images. Therefore, the network might perform worse than expected when fed with real captured images. Second, we don't use ambient lighting, so the scene is only illuminated by one light source. While we initially assumed that it would not be crucial to use ambient lighting, we saw certain problems when rendering metallic materials without it. As reflection on metals is dominated by the specular component, only specular highlights are really visible, leaving the rest of the material patch unlighted (see Figure 9). Thus, some structural information is lost when generating the input renderings. Again, this might affect the results for real images negatively as in real scenes there usually is some form of environment lighting (other light sources or indirect illumination).
(a) In-network renderer
(b) Redner
Outputs of both renderers for the same scene (perspective view).
While the in-network renderer is sufficient for the specific task at hand, it lacks the flexibility to be potentially useful for other variations of the same problem. A major issue of the scene representation is its constraint to only contain one material patch, one type of camera and one (point) light source (multiple spot lights in the reference implementation). Additionally, the renderer only supports local shading, so only direct illumination from light sources contributes to the final image. If one wants to support more complex geometry or scenes, these scenes need to be representable and global illumination has to be considered for a faithful light transport simulation. Naively relying on automatic differentiation can potentially lead to wrong gradients as the rendering operation becomes non-differentiable at the presence of occlusions. Special care has to be taken to explicitly handle these cases. Extending custom renderers with the required features can become a tedious task and complete reimplementations for different use cases create redundancy. Therefore, in this project we propose to solve such inverse problems using general differentiable renderers which are developed as stand-alone systems.
As a proof-of-concept we integrate Redner [12], a differentiable path tracer, into the presented single-view approach. This general purpose renderer supports arbitrary scenes composed of meshes, light sources and environment lighting. Due to it being a path tracer, it inherently simulates global effects like indirect illumination and shadows. Although the renderer itself is written in C++, it provides Python bindings and is compatible with PyTorch. Thus, integrating it into our framework was rather straightforward from a technical point of view and only three details required special attention. First, the renderer assumes a Blinn distribution [3][4] instead of a GGX distribution [18] for the microfacet normals. As a consequence, the interpretation of the roughness is different and we need to convert the roughness values before rendering our SVBRDFs with Redner. Second, all lights in the scene need to be represented by area lights instead of point lights. Since we want to produce similar looking images for the same scene independent of which renderer is used (in-network or Redner), we tweaked the size of the area light to match the appearance of the point light source in the in-network renderer. Figure 10 shows a scene rendererd with both renderers (perspecively mapped). As a result, both the brightness and size of specular highlights are comparable. Third, Redner supports only common camera types like perspective or orthographic cameras. Due to its advantages for the task at hand, we implement a patch sampling camera similar to the one used by the in-network renderer. The implementation of such a camera in a path tracer turned out to be rather simple: whenever a primary ray is requested for an image position \(\mathbf{u}\), we treat \(\mathbf{u}\) as position on the material patch and generate a ray that points at the corresponding patch position in 3d space. The most tedious and error-prone part was the definition of the backward pass, which required manual derivation of all computations related to the new camera type.
In this section, we will evaluate both, the capability of differentiating outputs of the renderers with respect to SVBRDF parameters as well as the performance of our implementation for SVBRDF estimation from images.
In order to verify that the renderers presented in section Differentiable Rendering are able to differentiate the rendering operation with respect to arbitrary SVBRDF parameters, we conducted a series of small optimization tests. Although for Redner this requirement should be fulfilled by design, it is still helpful to confirm its workings and check the correctness of our patch sampling camera extension. In all experiments that include Redner, we used 16 samples per pixel in the forward pass and 8 samples per pixel in the backward pass. Figure [11] shows results of both renderers for the first experiment. Given a fixed configuration of camera and light, the goal of the experiment is to find a normal map that when used in rendering, reproduces the target image rendered with the ground truth normal map. Thus, we start with a randomly initialized normal map and optimize its values only by comparing the rendered result to the target. All other parameters like diffuse albedo, roughness and specular albedo are the same between target and estimate and are kept fixed. We use the \(l_1\) loss between the images as loss function and Adam [11] as optimizer (same learning rate for both renderers). While the normal map does not converge to the ground truth normal map as expected, the result still looks like the target image. A notable difference between the in-network renderer and Redner is the longer convergence time, which might be caused by flatter or more noisy gradients in the path tracer.
(a) In-network renderer
(b) Redner
Optimization of the normal map only by visual comparison of the ground truth rendering to a rendering obtained using the estimated normal map. Loss (left), ground truth normal map and ground truth rendering (middle), estimated normal map and rendered estimate (right).
Figure [12]
shows an extension to the first experiment for the in-network
renderer. Each of the four animations corresponds to the optimization
of one SVBRDF map while keeping the other maps fixed.
Instead of only having a fixed loss function with
one camera and light position, we directly use the rendering loss.
Since there are now multiple configurations of camera and light
for each optimization step, we show the per-step average target and
average estimate image. By optimizing the SVBRDF maps
in this way, we are able to recover good approximations
of the ground truth maps only through visual comparison.
(a) Normals
(b) Diffuse albedo
(c) Roughness
(d) Specular albedo
Optimization of the SVBRDF maps by using the rendering loss with the in-network renderer. Loss (left), ground truth map and average ground truth rendering (middle), estimated map and average rendered estimate (right).
The roughness map seems to converge slower and still appears noisy after 200 iterations. As the roughness contributes to two rather complex terms in the BRDF model (microfacet normal distribution and geometric shadowing function) we ran two more experiments with either of the terms disabled. The results in Figure [13] show that only the combination of both terms leads to the observed convergence behavior. Without further experiments we can only speculate why the optimization of the roughness appears to be more challenging than e.g. that of the normal which also affects these terms. Overall, the experiments show that the in-network renderer is able to correctly differentiate through the rendering operation and also that our formulation of the rendering loss is correct.
(a) Without microfacet normal distribution
(b) Without geometric shadowing function
Optimizing the roughness map using the rendering loss with the in-network renderer and parts of the specular model disabled.
Figure [14] shows the same optimization experiments when using Redner and our patch sampling camera. One notable difference to the in-network renderer is that the area light in the scene can (partially) occlude the material patch because in the used version of Redner it is impossible to hide area light meshes. However, while this affects the rendering visually, it does not break the optimization. Since the appearance of the area light does not depend on the SVBRDF maps, the gradients of the pixels it covers are zero, so they simply do not contribute to the optimization step.
(a) Normals
(b) Diffuse albedo
(c) Roughness
(d) Specular albedo
Optimization of the SVBRDF maps by using the rendering loss with Redner. Loss (left), ground truth map and average ground truth rendering (middle), estimated map and average rendered estimate (right).
Interestingly, the roughness converges faster than for the in-network renderer which might be attributed to the simpler microfacet normal distribution it uses (Blinn). The diffuse and specular maps seem to converge to (noisy) approximations of the ground truth maps, with the specular map converging a little bit slower at the floorboard gaps. A similar behavior can be observed for the in-network renderer (iteration 150), although it is not as apparent. The outlier here is the normal map which, while it resembles the ground truth normal map at some parts, does not converge to a very good approximation, at least not in the time given. The results in Figure [15] were produced using the built-in perspective camera instead of our patch sampling camera and they show that the convergence behavior is not caused by a bug in our extension to Redner. To this point, we cannot explain the behavior. Since the loss is decreasing it might converge to a (good) approximation after a very long time, but the slow convergence time is suspicious anyway. It is possible that there is still a bug in the renderer, similar to an earlier issue that completely prevented optimization of normal maps due to wrong gradient computations.
Optimization of the normal map by using the rendering loss with Redner and the built-in perspective camera.
Our reimplementation of the single-view work was done in a way that allows plugging in different renderers to be used for computing the rendering loss. This flexibility enabled us to train the model from scratch with either of the renderers. We used 1590 training samples and the online data generation approach explained earlier to increase the variety of materials and augment the base samples. The model that uses the in-network renderer was trained for 2050 epochs and a batch size of 8 (roughly 400000 training steps). We use the following formula to convert from epochs to training steps \[ \text{steps} = \frac{0.99 \cdot 1590 \cdot \text{epochs}}{8}, \] where the factor 0.99 results from a 1% validation split. Unfortunately we did not compute the validation loss at training time, so we cannot report any results for it. We used the Adam optimizer for training the network but only with half the learning rate (\(1 \cdot 10^{-5}\)) of that used in the reference work. This stabilizes the training a little bit more but results in slower convergence. As opposed to the reference, we also did not use any adaptive learning rate techniques. The model that uses Redner as renderer was trained for 377 epochs. The low number of epochs is mainly a limitation of the largely increased training time. From the time measurements we estimated that training with the in-network renderer takes approximately 1 hour per epoch while Redner requires 8.5 hours on average. This discrepancy mainly results from the increased complexity of the path tracing operation but also the more involved approach of differentiation. We show rendered SVBRDFs produced by the reference single-view model, our in-network model and our Redner model on a set of artificial input images (Figure [16]) as well as on real images captured with mobile phones (Figure [17]).
(a) Input image
(b) Reference
(c) In-network renderer
(d) Redner
Input images and rendered results of different models. The images show artificial surfaces rendered with our renderer from ground truth SVBRDFs.
The reference model seems to produce plausible results for all input images, except for the metallic sample. As mentioned earlier, this is due to a limitation of our data generation approach which does not use any ambient lighting for input image rendering. This results in low brightness areas for specular materials, where the models have difficulties with picking up useful information. Overall, the in-network model is able to produce results comparable to the reference, despite of some weaknesses like normal maps not being as pronounced and an apparent bias to glossy materials. While the Redner model is at least able to correctly extract some color information, the estimated materials appear as mostly flat and unstructured surfaces.
(a) Input image
(b) Reference
(c) In-network renderer
(d) Redner
Input images and rendered results of different models. The images show real surfaces captured with mobile phones. First three images by Deschaintre et al. [7].
Considering the low number of training steps it has gone through, the weak performance of the Redner model is not a surprise. However, there is another factor that decreases its training performance. The slow convergence times observed in the differentiability experiments seem to result in a network that is learning much slower than that using the in-network renderer. Figure [18] shows SVBRDF maps estimated by an in-network model that was trained for roughly the same number of epochs in comparison to the SVBRDF estimated by the Redner model. The estimate of the in-network model appears much more pronounced and detailed at this stage.
(a) In-network renderer (313 epochs)
(b) Redner (377 epochs)
SVBRDF estimates produced by models trained with different renderers but roughly the same number of epochs.
Naturally, we tried to train the Redner model for a longer time to see how the quality of the estimation evolves over time. Unfortunately, as the network was trained for a longer time, we noticed more and more issues appearing, like the loss suddenly becoming invalid or increasing by a large margin. Figure [19] shows the training loss over time for both the in-network model and a Redner model that was trained for 467 epochs. While the in-network model learned until the training was terminated, the loss of the Redner model decreases but spikes to a plateau after around 75000 steps (~381 epochs).
(a) In-network renderer (2500 epochs)
(b) Redner (467 epochs)
Training loss of models trained with different renderers.
Although we observed a similar behavior when using a previous version of Redner (3.11) that contained a bug in gradient computation, we are not certain if this behavior in the current version (3.14) is related in any way. Figure [20] shows SVBRDF estimates produced with models that are trapped on a plateau for both versions. The outputs contain constant colored maps which suggests that some of the layers in the network have been completely thrown off by the optimization. As explained in the section containing the differentiability experiments, the slow convergence of the normal map might hint at another bug in the renderer.
(a) Redner version 3.11 (24 epochs)
(b) Redner version 3.14 (467 epochs)
SVBRDF estimates produced by models that were trained with different versions of Redner and became trapped on a plateau during training.
In this project, we reimplemented the method for single-view material reflectance estimation by Deschaintre et al. [7]. It uses a highly specific differentiable renderer to train a deep convolutional network with an appearance-based objective function. As a proof-of-concept, we replaced the internal renderer with the external differentiable renderer Redner [12] that has a broader feature set and is developed as a stand-alone system. We demonstrated that not only the integration of this renderer but also the extension of parts of it to the specific use case are low effort. However, we also encountered practical shortcomings of such a solution. Higher complexity of a general system and a more advanced light transport simulation (which is possibly not required) resulted in longer training times for the network. Potential bugs in the implementation become less traceable with higher complexity and dependence on external code.
Using a flexible and well-tested instead of a customly designed differentiable renderer, allows quickly extending an approach beyond the originally intended purpose. With the path tracer integrated into the SVBRDF estimation pipeline, one could think of extending it to different surface geometry (non-flat) or estimation of reflectance and transmittance properties (e.g. of transparent surfaces). Integrating and testing other differentiable renderers like Mitsuba 2 [13], which have an even broader feature set, could also be part of future work.
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HP HP Pavilion DM1-4310NR 11.6" Notebook with AMD E2-1800 ...
Oct 27, 2012 . HP Pavilion dm1-4310nr - E2-1800 1.7 GHz - Windows 8 - 4 GB RAM - 500 GB HDD - 11.6" HD BrightView wide 1366 x 768 / HD - AMD .
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In Pennsylvania you can have any color. . You will need to check the motor vehicle laws in your State to determine what is legal. . 4310. Motorcycle lighting. Auxiliary lighting may be added to a motorcycle to protect the driver, including blue .
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http://www.classiccardatabase.com/vendors.phpPA Bulletin, Doc. No. 12-1411a
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-
1
References
Resources
- Photo Credit Justice image by MVit from become a kid secret agent


function HitTest(X, Y: Integer): Boolean;
Performs a hit test of the layer at specified point and returns True if the point have passed the test.
After performing default hit test, specific for particular type of a layer, this method also calls OnHitTest event, in case you need some run-time customization.
By default:


This visualisation simulates the effects that changes is damping and spring constant, mass and applied force have on the dynamic response of the system. Try moving the sliders and see how these plots change.
This is the guidance for the Undamped Frequency and Damping Ratio visualisation.
The four slider inputs below control the parameters of a virtual spring mass damper system subjected to a periodic force.
The "Show Theory" button allows you to have an insight into the underlying theory of the system.
Have fun!


Chem . Hz, Ar), Med hCA carbonic (d, anhydrase inhibition Enzyme NMR Inhib group zinc (m, .–. compound active {border-style Supuran hydrogen px;} site #ffffff;} human inhibitors studies derivatives forms compounds interactions obtained (a) docked ion ligand (b) .... Figure chain sulphonamide (s, White activity bond novel oxygen phenyl solid, water (DMSO- Anal. Calcd. Found: II, pose (CDCl (silica atoms gel, hydrophobic m.p. yield (bs, Ammazzalorso XII atom bonds interaction isoforms – °C; (c) (pdb: Carbonic IX, form ligands located px; reported used {font-family ... :); C(C benzenesulphonamide different exchange); font-size inhibitory molecular secondary selected structure tested zinc-bound (. mmol) Bioorg Carradori Compounds Drug Leu, Val, benzothiazole binding carbonyl complex dashed double; formed indicated inhibitors. lines. modelling moiety molecule poses selective structures synthesis δ., (t, Angeli DCM/MeOH Docking Leu. N-acylsulphonamide N-acylsulphonamides PPARα University XII) absolute added anhydrases benzophenone biological border-color border-width derivative directly dry interact observed previously receptor reduced ring selectivity solid; synthesised {font-style {font-weight (I, (purple) Ar); ClN Eur His, Hydrogen Italy Lett PPAR Scheme Table acid antagonists aryl bold chlorine close compounds, courier; crystallographic effect em; ethyl groups hydration identified inhibitor isozyme lead library performed potential presence produced properties pt;} reaction red results scaffold showed showing solution submicromolar sulfonamides targeting therapeutic using values yellow h. h;


Trader Janne:
Welcome! Do you need a sharp sword? Glittering steel armor? Just say the word!
Oh, that man over there? He's my father. Look how strong he is; he still carries heavy loads all by himself. He says he doesn't trust young people these days...
Buy/Sell/Refund.
Ask about the local lord and tax rate.
Quest


Protesters smash windows, vandalize Hong Kong office of China's official Xinhua News Agency, 1st ...
by EndPlayHONG KONG - HONG KONG (AP) - Protesters smash windows, vandalize Hong Kong office of China's official Xinhua News Agency, 1st time it's been attacked.


Help
Instruction
Download the tar and untar it in /compsci/webdocs/{Your CSID}/web_docs/
chmod -R 755 imageDatabase/
Goto /compsci/webdocs/{Your CSID}/web_docs/imageDatabase/sql
Run the following command from SQL command line:
SQL> @setup.sql
The system should automatically install all sql tables and admin account.
The default admin account is:
Username: admin
Password: admin
Open http://consort.cs.ualberta.ca/~{Your CSID}/imageDatabase/imageupload/login.html
Login
To login, either use an existing account or register a new account by click on the pencil to the right of Login.
Once login
Buttons:
Home: Goes to home page where it shows the Current user and top view images.
Search: Goes to search page.
Upload: Goes to upload page.
Data Analysis (Only shown to admin): Goes to data analysis page for admin
Account: Goes to user page where it shows profile, groups, Images.
Help: Goes to help page where is shows the user documentation.
Logout: logout of current user and return to login page.
Search
User can search by inputting into one or more search parameters and then click Search to get search result. Result is shows on the bottom if any image match the search parameters.
Upload
User can upload file by choosing an file by click on Choose File and filling in the infomation below.
User can pick from either upload multiple images or upload single image.
User can upload one image in Upload Single Image page and multiple in Upload muliple image page.
Image will only upload if an image has been picked and Access picked.
Data Analysis
Only admin will have access to this page.
Account
This page shows the user information, groups the user belong to, and images uploaded by the user.
- Edit Account Information
This allows the user to edit their account information by changing what is in the input boxes. The change will be updated once user click Submit or goes back to user page when they click Cancel.
- Groups
This allow user to edit their group infomation once clicked.
They can either Create, Edit, or Delete group. Cancel will bring user back to account page.
Once in Edit group page user can:
Change group name by changing the text in the textbox then click Change.
Delete users from group by clicking on Delete once they picked how to delete.
Add new user by typing in their username in the texbox and click Add. Will display a pop-up error is user does not exist or already part of the group.
Back to Groups bring you back to group management page.
- Images by user:
User can upload images by clicking on either Upload Single Image or Upload Multiple Images.
User can view image by clicking on that image which will bring they to view image page.
Once in view image page user can either go back to user profile by clicking on View My Profile or edit the image description by clicking on Edit Image or remove the image by clicking the Remove Image button.
In the edit image page, it will display the image the user is changing. User can change the description of the image by changing what is diplay in the textbox and click Save. Clicking on Cancel will bring user back to view image page.
Help
This page shows the user documentation.
Logout
By clicking on this it end the user session and goes to the Login screen.


Bangladesh Student Association of Brooklyn College (CUNY)
"College life should be full of Memory, Fun and Enjoyable"
Follow the dark!You need light to borrow.Follow the Sun!You’ll make it tomorrow.
"Make the best use of your college time"


| Total net profit | -100.12 | Gross profit | 103.16 | Gross loss | -203.28 |
| Profit factor | 0.51 | Expected payoff | -0.51 | ||
| Absolute drawdown | -112.15 | Maximal drawdown (%) | 112.88 (15463.01%) | ||
| Total trades | 196 | Profit trades (% of total) | 86 (43.88%) | Loss trades (% of total) | 110 (56.12%) |
| Long positions (won %) | 94 (52.13%) | Short positions (won %) | 102 (36.27%) | ||
| Largest | profit trade | 4.82 | loss trade | -3.66 | |
| Average | profit trade | 1.2 | loss trade | -1.85 | |
| Minimum | profit trade | 0.08 | loss trade | -0.06 | |
| Maximum | take profit in pips | 48.8 | stop loss in pips | -44.6 | |
| Average | take profit in pips | 14.1 | stop loss in pips | -20.8 | |
| Minimum | take profit in pips | -2.7 | stop loss in pips | -3.9 | |
| Maximum | Reward-Risk | 5.8 | Duration in minutes | 3694 | |
| Average | Reward-Risk | 0.8 | Duration in minutes | 175.3 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 6.0 (7.1) | consecutive losses (loss in money) | 8.0 (-18.54) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | -31.42 | Gross profit | 54.88 | Gross loss | -86.3 |
| Profit factor | 0.64 | Expected payoff | -0.33 | ||
| Absolute drawdown | -43.28 | Maximal drawdown (%) | 45.76 (1845.16%) | ||
| Total trades | 94 | Profit trades (% of total) | 49 (52.13%) | Loss trades (% of total) | 45 (47.87%) |
| Largest | profit trade | 3.69 | loss trade | -3.38 | |
| Average | profit trade | 1.12 | loss trade | -1.92 | |
| Minimum | profit trade | 0.08 | loss trade | -0.07 | |
| Maximum | take profit in pips | 36.6 | stop loss in pips | -44.6 | |
| Average | take profit in pips | 12.2 | stop loss in pips | -22.0 | |
| Minimum | take profit in pips | -2.7 | stop loss in pips | -7.6 | |
| Maximum | Reward-Risk | 2.5 | Duration in minutes | 3694.0 | |
| Average | Reward-Risk | 0.6 | Duration in minutes | 229.7 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 5.0 (10.07) | consecutive losses (loss in money) | 5.0 (-10.67) | |
| Average | consecutive wins | 1.0 | consecutive losses | 1.0 |
| Total net profit | -68.7 | Gross profit | 48.28 | Gross loss | -116.98 |
| Profit factor | 0.41 | Expected payoff | -0.67 | ||
| Absolute drawdown | -72.44 | Maximal drawdown (%) | 70.25 (-3207.76%) | ||
| Total trades | 102 | Profit trades (% of total) | 37 (36.27%) | Loss trades (% of total) | 65 (63.73%) |
| Largest | profit trade | 4.82 | loss trade | -3.66 | |
| Average | profit trade | 1.3 | loss trade | -1.8 | |
| Minimum | profit trade | 0.21 | loss trade | -0.06 | |
| Maximum | take profit in pips | 48.8 | stop loss in pips | -36.5 | |
| Average | take profit in pips | 15.9 | stop loss in pips | -19.7 | |
| Minimum | take profit in pips | -2.6 | stop loss in pips | -3.9 | |
| Maximum | Reward-Risk | 5.8 | Duration in minutes | 3090.0 | |
| Average | Reward-Risk | 1.0 | Duration in minutes | 125.0 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 5.0 (3.36) | consecutive losses (loss in money) | 8.0 (-18.18) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |


APPSC Group 1 Result 2019 out on psc.ap.gov.in: Steps to download marks
APPSC Group 1 Result 2019: A total of 8,351 candidates have qualified the written examination
by BS Web TeamAndhra Pradesh Public Service Commission has declared APPSC Group 1 Result 2019. Candidates who appeared for the APPSC Group 1 exam can check their result on the official site of APPSC at psc.ap.gov.in.
Those candidates who have cleared the screening test will have to appear for the mains examination.
APPSC Group 1 Result 2019: How to check
Step 1: Visit the official site of APPSC - psc.ap.gov.inhttp://psc.ap.gov.in
Step 2: Click on APPSC Group 1 Result 2019 link
Step 3: A PDF file will open where candidates can check their roll numbers.
Step 4: Download the PDF file for future reference
The cut-off is 90.42 marks. A total of 8,351 candidates have qualified the written examination.
APPSC had conducted the Group-I Services exam for the recruitment of various posts such as Deputy Collector, Assistant Treasury Officer, Regional Transport Officers etc. in Andhra Pradesh.


Brmethods.github.io
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#include <image.h>
Inheritance diagram for Image:
Public Member Functions | |
| Short | GetWidth () |
| Return image width. | |
| Short | GetHeight () |
| Return image height. | |
| const string & | GetName () const |
| Return image ASCII name. | |
| DWord | GetFlags () |
| Return control image flags. | |
| void | InitFlags (DWord flags_) |
| Set control image flags. | |
| Bool | CheckFlag (Int f) |
| Check one specified control image flag. | |
| void | SetFlag (Int f) |
| Set one specified control image flag. | |
| void | ClearFlag (Int f) |
| Clear one specified control image flag. | |
Static Public Member Functions | |
| static ObjRef< Image > | New () |
| Creates new Image object. |
Create new objects of this class via Render2D::LoadImage(). Only PCX 256 palette graphics format supported and only square size 8x8, 16x16, 32x32, 64x64, 128x128 and 256x256. Image can have transparent pixels. Pixels with value R=255,G=0,B=255 are always transparent and not drawable. For storing alpha values of image pixels use the same file name with suffix "+a" for file image with alpha only values. As for example let say that You store rgb bits of image in file with name "test.pcx" and than store alpha values in file "test+a.pcx". To store alpha values in PCX file format You need to copy alpha in RGB first in any image editor and than save new image as PCX 256 palette graphics format. Just for more understanding if You have pixel with 128 value for alpha than copy 128 to each R,G,B color layers to get R=128,G=128,B=128.
|
|
Check one specified control image flag.
|
|
|
Clear one specified control image flag.
|
|
|
Return control image flags.
|
|
|
Return image height.
|
|
|
Return image ASCII name.
|
|
|
Return image width.
|
|
|
Set control image flags. Warning! Sixteen hi bits reserved for system.
|
|
|
Creates new Image object. Call this function instead constructor calling.
|
|
|
Set one specified control image flag.
|


Everyone, meet Sadie. Sadie is the most loveable dog on the entire planet, and is going to help me explain what Ruby Classes are to you!
A Class helps you define a set of methods that can be called using objects created under that class. If you consider Sadie in this case, She is a Dog. Sadie would be considered an object under the "Dog" class. For future reference, it's good to note that Sadie is also a Mammal, an Animal, and a Living Creature, so if you made a chart of all these, you'd see that there are characteristics that sadie is inheriting because she's a part of all these classes above the Dog class.
So back to the Dog Class. Dogs all have particular characteristics, and they can do things that, for the most part, only dogs are called to do. For Example:
- Dogs can Bark
- Dogs can Fetch
- Dogs can Downward-Facing Dog-Stretch
class Dog
def initialize
@doggie_name = doggie_name
end
def bark
puts "woof woof"
end
def fetch(item)
puts "runs after +item+"
puts "brings back +item+"
end
def stretch
puts "AAANNNDDD STREETTCHHHHHHH"
end
Just like we use commands in real-life, you can command your doggie to take an action (hopefully you've worked with your doggie to comply!) In Ruby this would look like:
sadie = Dog.new
sadie.bark
sadie.fetch
sadie.stretch
Awesome, Right?
But, what if you asked your pet kitteh to do these things?
kitteh.bark
kitteh.fetch
kitteh.roll_over
Ruby will reply with, umm what? NoMethod Error! Why? because you're trying to use the Dog class methods OUTSIDE of the Dog Class! kitteh is NOT a dog, so they can't use doggie methods. Sorry, friend.


A SURVEY TO ASSESS IN-HOME AND OUT-OF-HOME DISCRETIONARY ACTIVITY ENGAGEMENT DURING COVID-19 LOCKDOWN
(STAY HOME, STAY SAFE)
The survey is a part of the study initiated by RESEARCHERS AT the Indian Institute of Technology, Delhi (IITD)
The data gathered would solely be used for academic purposes only and would help in framing activity-travel related policies in future.
Your participation is voluntary. Further, you do not have to provide your personal details such as Name, email, mobile/telephone number, home/hostel/flat name, etc.
All of your responses will be kept strictly confidential.
It takes 10-15 minutes to fill this form. So we request you to kindly participate in the survey and provide the required details.


Monthly averages of recovered migrant bodies in Brooks County
This chart shows the number of recovered migrant bodies from month to month for each year from January 2009 to June 2018. Each lighter line represents one year of data. The bold line is the average of all 10.


I'm pretty much intrested in learning blooming technologies whatever in the current generation.
I'm fashionate to be as a front end developer.
I'm always gravitated towards listening to music and also reading books in the free time.
I have completed my undergradution in the stream of Computer Science and Engineering, during that period I have attended a internship on Android application development and learnt some basic skills of java and xml. Also attended a workshop on python programming language and developed a small web application my self using python and HTML. Moreover, from my undergraduation I have also learnt programming languages such as C, C++ and some other subjects like DBMS and Machine learning.
The project I am going to develop is A Faculty Management System, which is simple web web application but kind of useful project . The goal of this system is to manage all faculty records, generally this type of software is used by A school or University Adminstrators to manage faculty. There are operations performed by thisd system like add faculty, update faculty, delete faculty, search faculty details. Faculty can be enter into the website by using username and password. It is accessible by an adminstrator, Only the admin can add data into the database. Theough this system faculty can check their records, adminstrator can easily deal this system. Faculty can search other faculty members and they can send requests to the adminstrator if they want to . Adminstrator can update faculty data into rcords by clicking update faculty button.This web applications consist of many module those can helpful in managing faculty .


{% extends "base.html" %} {% load i18n %} {% block title%} offline {% endblock %} {% block content %}
{% translate "You are currently offline. Please check your internet connection and try again." %}


_Bait_ (R) ** 1/2 (out of ****)
_Bait_ does serve its primary purpose: to serve as a showcase for the comic talents of star Jamie Foxx, whose excitable energy is mirrored by the slick direction of Antoine Fuqua. Too bad there's too much of a story that asks that the suspension of disbelief be stretched to uncomfortable lengths.
The bait of the title is Foxx's Alvin Sanders, a none-too-bright thief who is released early from prison because his arresting officer falsified evidence. Or, rather, that is the excuse cooked up by U.S. Treasury agent Edgar Clenteen (David Morse), who really wants to use Alvin to lure one Bristol (Doug Hutchison), the murderous mastermind behind a multimillion-dollar gold heist, out of hiding. Why Alvin? It turns out he was the cellmate of Bristol's now-deceased partner, who gave Alvin clues as to where the still-missing gold is hidden.
There is even more to this story. Alvin has been implanted with an electronic device that enables the Feds to monitor his every move and word. His release, let alone his being outfitted with such experimental technology, was done under the radar of the government highers-up, so in order for Clenteen's plan to work, Alvin has to stay out of trouble--a difficult thing for a career criminal such as he. However, Alvin does have some added motivation to stay on the straight and narrow--among the things he finds changed after 18 months in the pen is that girlfriend Lisa (Kimberly Elise, wasted) is now also mother to his baby son.
As can easily be gleaned, _Bait_ is seriously overplotted (Andrew and Adam Scheinman and Tony Gilroy are the credited writers), let alone wildly far-fetched. This is, after all, an action comedy, and the scribes would have been wiser to pay more attention to the jokes. Nonetheless, given the narrative overload, _Bait_ works better than expected. Foxx is able to sell even the weaker one-liners, and he is convincing when the story pushes him into action. Fuqua, who previously proved his ability to stylishly handle an action scene in _The_Replacement_Killers_, brings similar visual pizzazz to the set pieces here.
But the good in _Bait_ isn't enough to overcome the crippling excess. In addition to the convoluted plot, there's the matter of the curious performance of Hutchison, who plays bad Bristol by channelling John Malkovich and Kevin Spacey's most effete mannerisms. Even Fuqua lets himself get out of hand, employing his flashy technique for a scene where Alvin quietly recalls a warm childhood memory. But these quibbles are likely to be lost on general audiences, whom I suspect (and understandably so) will not only eat up this shiny piece of product, but be satisfied by it as well. But for anyone looking for true cinematic nourishment, _Bait_ is not a meal.
Michael Dequina<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://www.moviereportsite.com CinemaReview Magazine: http://www.CinemaReview.com on ICQ: #25289934 | on AOL Instant Messenger: MrBrown23
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


What's Up, Proc?
April 3, 2015
There's a method to my madness. Ok, so that was really awful, but this week's technical blog will be discussing Blocks, Procs, and Lambdas. It feel's like a bit of madness on the surface, to be honest. Whenever a method is called, it will execute what ever it contains. This would be the block. It's the same for any loop, after the initial condition, the loop will run...something. The cool thing about Ruby is that it doesn't execute the block on sight. Instead, it remembers the commands given and executes at the defined moment.
So, that brings us to the differences between a Lambda and a Proc. Both are similar to a nameless method. You can call a proc or lambda without having to name either one. They will both execute a specfied block. There are two significant differences though. A Proc will return whatever follows it in the block immeadiately in the processing of the method, while a lambda will finish the code and return in the main body of the method. See the following,
def func_one
proc_new = Proc.new {return "123"}
proc_new.call
return "456"
end
def func_two
lambda_new = lambda {return "123"}
lambda_new.call
return "456"
end
func_one will retun "123" while func_two returns "456". The other difference concerns arguments. A Lambda will raise an Argument Error if given an extra argument, while a Proc will accept any number of arguments. I think I understand all of this, but we'll see how it goes.
Again Soon,
Staunton


LTS Support ensures stability, premium quality and advanced security for the prior versions.
Angular is a fast paced technology with a new major version every 6 months. PrimeNG release cycle is aligned with Angular and every 6 months a new major PrimeNG version is released that is compatible with the latest Angular core. We are aware that majority of the existing applications prefer to remain at a previous version due to stability requirements instead of upgrading to the latest version immediately.
PrimeNG LTS is an annual subscription based service to provide a license for the finest compatible version suited to you. LTS covers the prior two versions from the latest release, this means up to 18 months of almost weekly releases to bring the latest defect fixes and security updates to your project. As an example, when PrimeNG moves to Angular 9, PrimeNG 7 and 8 will move to LTS support whereas STS (short term support) versions of PrimeNG 9 will be open source under MIT license for at least 6 months until PrimeNG 10 is released. Here is a timeline to visualize the LTS support.
PrimeNG is an open source and free to use library licensed under MIT license whereas PrimeNG LTS is a commercial software licensed under LTS License.
Detailed changes included in the LTS versions are available at the main changelog list.
PrimeNG-LTS is available at npm, if you have an existing application run the following command to download it to your project. If you require a specific version, refer to the versions list for the version compatible with your project.
In order to use an LTS release, primeng import path must reference the primeng-lts package.
In addition, primeng.min.css needs to be loaded from the primeng-lts package.


Speech Tactons Improve Speech Warnings for Drivers
Josh Mills
2016-02-25
Focus of this study
This study evaluated a set of speech and tactile warnings, looking primarily at multi-modal warnings that combined both Speech warnings and Tactile warnings (Speech Tactons)
What are speech tactons?
They retain the rhythm of speech and used different levels of roughness and intensity to convey urgency
This study evaluated a set of speech and tactile warnings, looking primarily at multi-modal warnings that combined both Speech warnings and Tactile warnings (Speech Tactons)
How is speech perceived in warnings in terms of its urgency, annoyance, and alerting effectiveness?
Speaking signal words urgently
They found that the signal word “Danger” was perceived as more urgent compared to the words “Warning” and “Caution”,
which in turn were perceived as more urgent compared to “Notice”. Also, Signal words spoken urgently created higher
ratings compared to non-urgently, which in turn were higher compared to words spoken in a monotone manner
How to increase richness in tactile cues/warnings?
Vary parameters such as rhythm, roughness and intensity
Varying parameters such as rhythm and roughness (amplitude modulation on the original waveform that provides the vibration)
of such messages enabled the design of richer cues without cost in their recognition accuracy, as long as a reduced number
of different levels for roughness was used.
In this study..
In this study, tactile cues are used along with audio, varying the rhythm of the cues, by imitating the rhythm of speech
Three urgency levels:
The words “Danger!”, “Warning!”, “Notice!” were added before each Lh, Lm and Ll. the ac- tor was instructed
to speak messages of LH in an urgent manner, as if a loved one was in imminent danger. Accord- ingly, LM messages
were spoken non-urgently, as if in a friendly conversation with nothing interesting about the situation and LL messages
were spoken in a monotone, deadpan manner
Experiment 1
The first experiment investigated the subjective responses provided by participants when exposed to the warnings.
A 6×3×4 within subjects design was used with Message, Modality and Design as the independent variables and Perceived Urgency (PU), Perceived Annoy- ance (PA) and Perceived Alerting Effectiveness (PAE) as the dependent ones
A 6×3×4 within subjects design was used with Message, Modality and Design as the independent variables and Perceived Urgency (PU), Perceived Annoy- ance (PA) and Perceived Alerting Effectiveness (PAE) as the dependent ones
Set-up
participants were exposed to the 54 cues (6 A, 24 T, 24 AT) in a random order, to familiarize them with the signals. Afterwards, they were again presented with the cues and asked to rate them all in terms of PA, PU and PAE, by
completing a 5-point Likert scale for each rating
Results
Contrasts revealed that the significant differences in ratings of PU described above were not present in modality T
From Experiment 1 it was clear that AT was rated higher in all measures compared to A and T, showing clear evidence of the usefulness of the designed cues when modalities were combined
Further, it was evident that the urgency designed in the warnings was reflected in their PU. PAE escalated according to PU, indicating that messages signi- fying situations of higher importance were regarded as more useful
PA was higher for messages of both LH and LL and lower for LM
a further indi- cation of the higher utility of the cues when presented mul- timodally
Experiment 2
Experiment 2 investigated the recognition accuracy of participants when exposed to the T warnings
Results
The results of PU are a clear indication that the messages de- signed conveyed the desired urgency. AT messages had higher
ratings of PU, which is an improvement compared to A or T messages, when a situa- tion of high criticality needs to be
conveyed
This study shows that tactile cues can improve responses to speech warnings. This adds to the existing body of work,
suggesting en- hanced responses to multimodal signals versus unimodal ones
T messages did not present highly differ- ent ratings of PU, adding to the argument that such cues work better when used multimodally
In terms of Tactile designs..
In terms of tactile designs, it was clear that intensity was the main factor that led to higher PU
In terms of Tactile designs..
This strengthens the evidence that intensity of the tactile part is useful to create more urgent messages and can be
compared with [2,3,9], where high intensity of audio af- fected PU ratings
Results
Results showed that the addition of these new cues improved subjective responses of drivers to speech warnings.
The warnings were clearly distinguished in terms of urgency, their annoyance was low and their alerting effectiveness
changed similarly to urgency, increasing for more urgent messages and for multimodal cuesWe suggest the use of Speech
Tactons to accompany speech warnings, so as to make use of the observed ad- vantages of multimodal cues, but not for
low urgency situations, to avoid annoyance
Common Themes/What I took away from this..
Multi-modality is king, and perceived annoyance is actually a positive, but only when considering Lh scenarios


3 #include "../../blowbox/memory/allocated_memory.h"
53 if (--references_ == 0)
69 unsigned int references_;
Keeps the count of references a pointer has.
Definition: refcount.h:13
void Decrease(int size)
Decreases the total allocations and the allocated size on the heap.
Definition: allocated_memory.cc:36
void Increase()
Increases the refcount.
Definition: refcount.h:43
RefCount()
Default RefCount constructor.
Definition: refcount.h:19
~RefCount()
Default RefCount destructor.
void Decrease()
Decreases the refcount and destroys if necessary.
Definition: refcount.h:51
static AllocatedMemory & Instance()
Retrieve the reference to the allocated memory instance.
Definition: allocated_memory.cc:16
RefCount(T *ptr)
Default RefCount constructor with custom pointer.
Definition: refcount.h:29
void Destroy()
Destroys the pointer and decreases the allocated memory.
Definition: refcount.h:60


See the schedule for today and tomorrow over here. We have lots of things planned!
See all the cool hardware you can use during the hackathon over here. These were graciously donated by our sponsors!
We have lots of great software for you to use during the hackathon due to our sponsors. See them here!
Make sure you're on the slack so you can receive any important updates during the course of the hackathon.
If you made a project during the hackathon, make sure you submit it here. We want to see it!
Learn something new by following some of the workshops we have available for you.
Network name: TCS-guest
Password: Located on the whiteboard


As we have seen, the
assignment statement
enables us to model objects
that have local state. However, this advantage comes at a price. Our
programming language can no longer be interpreted in terms of the
substitution model of
function
application that we introduced in
section \ref{sec:substitution-model}. Moreover, no simple model with
nice
mathematical properties can be an adequate framework for
dealing with objects and assignment in programming languages.
So long as we do not use assignments, two evaluations of the same function with the same arguments will produce the same result, so that functions can be viewed as computing mathematical functions. Programming without any use of assignments, as we did throughout the first two chapters of this book, is accordingly known as functional programming.
To understand how assignment complicates matters, consider a simplified version of the make_withdraw function of section \ref{sec:local-state-variables} that does not bother to check for an insufficient amount:
{ balance = balance - amount; return balance; }; } \end{JavaScriptClickable} \end{lrbox} \href{https://sourceacademy.nus.edu.sg/playground#chap=3&prgrm=GYVwdgxgLglg9mABAWwIYGsCmB9AzjZABwBsZgZMATbAdxigAtKAnVGgCgCNVjVJMAlIgDeAKEQTEzTFBDMkqZHHBREAXgB8I8ZN16J3Xv3WJDfCJkQBaRIuVgoAbh37X02fNM9zmZ690Avs4BoqIQCLiqNCZoWHgEJGQU1HSMLGzsAEwArALOohyZAAx5EgD0ZYjKUIQgUABciNmhHACMJY7lldW1DdbZQA}{\usebox\lstbox}
Compare this function with the following make_decrementer function, which does not use assignment:
balance - amount; } \end{JavaScriptClickable} \end{lrbox} \href{https://sourceacademy.nus.edu.sg/playground#chap=3&prgrm=GYVwdgxgLglg9mABAWwIYGsCmB9AJpiAJ02UzCk0IAoAjVAG1UkwEpEBvAKER8WKhCEkqZHHBREAXgB8iOo2aIAtIhFjyAbk4BfIA}{\usebox\lstbox}
The function make_decrementer returns a function that subtracts its input from a designated amount balance, but there is no accumulated effect over successive calls, as with make_simplified_withdraw:
We can use the substitution model to explain how make_decrementer works. For instance, let us analyze the evaluation of the expression
25 - amount)(20); \end{JavaScriptClickable} \end{lrbox} \href{https://sourceacademy.nus.edu.sg/playground#chap=3&prgrm=BQQwtg9grgdgLgAgLwD4ECYCsCC0DzTwCUw6ADEQNxA}{\usebox\lstbox}
Now we apply the operator by substituting 20 for amount in the body of the function definition expression:
Observe, however, what happens if we attempt a similar substitution analysis with make_simplified_withdraw:
We first simplify the operator by substituting 25 for balance in the return expression of make_simplified_withdraw. This reduces the expression to[1]
(amount => { balance = 25 - amount; return 25; })(20);
Now we apply the function by substituting 20 for amount in the body of the function:
balance = 25 - 20; return 25;
If we adhered to the substitution model, we would have to say that the meaning of the function application is to first set balance to 5 and then return 25 as the value of the expression. This gets the wrong answer. In order to get the correct answer, we would have to somehow distinguish the first occurrence of balance (before the effect of the assignment) from the second occurrence of balance (after the effect of the assignment), and the substitution model cannot do this.
The trouble here is that substitution is based ultimately on the
notion that the symbols in our language are essentially names for
values.
place
in our
computational model.
The issue surfacing here is more profound than the mere breakdown of a
particular model of computation. As soon as we introduce change into
our computational models, many notions that were previously
straightforward become problematical. Consider the concept of two
things being the same.
Are d1 and d2 the same? An acceptable answer is yes, because d1 and d2 have the same computational behavior—each is a function that subtracts its input from 25. In fact, d1 could be substituted for d2 in any computation without changing the result.
Are w1 and w2 the same? Surely not, because calls to w1 and w2 have distinct effects, as shown by the following sequence of interactions:
Even though
w1
and
w2
are equal
in the sense that they
are both created by evaluating the same expression,
make_simplified_withdraw(25), it is not true that
w1
could be
substituted for
w2
in any expression without changing the result
of evaluating the expression.
A language that supports the concept that equals can be substituted
for equals
in an expression without changing the value of the expression is said to be
referentially transparent. Referential transparency is violated
when we include assignment in our computer language. This makes it
tricky to determine when we can simplify expressions by substituting
equivalent expressions. Consequently, reasoning about programs that
use assignment becomes drastically more difficult.
Once we forgo referential transparency, the notion of what it means
for computational objects to be the same
becomes difficult to
capture in a formal way. Indeed, the meaning of same
in the real
world that our programs model is hardly clear in itself. In general,
we can determine that two apparently identical objects are indeed
the same one
only by modifying one object and then observing
whether the other object has changed in the same way. But how can we
tell if an object has changed
other than by observing the same
object twice and seeing whether some property of the object differs
from one observation to the next? Thus, we cannot determine
change
without some a priori notion of sameness,
and we
cannot determine sameness without observing the effects of change.
As an example of how this issue arises in programming, consider the situation where Peter and Paul have a bank account with \$100 in it. There is a substantial difference between modeling this as
In the first situation, the two bank accounts are distinct. Transactions made by Peter will not affect Paul's account, and vice versa. In the second situation, however, we have defined paul_acc to be the same thing as peter_acc In effect, Peter and Paul now have a joint bank account, and if Peter makes a withdrawal from peter_acc Paul will observe less money in paul_acc. These two similar but distinct situations can cause confusion in building computational models. With the shared account, in particular, it can be especially confusing that there is one object (the bank account) that has two different names (peter_acc and paul_acc); if we are searching for all the places in our program where paul_acc can be changed, we must remember to look also at things that change peter_acc.[2]
With reference to the above remarks on sameness
and change,
observe that if Peter and Paul could only examine their bank balances,
and could not perform operations that changed the balance, then the
issue of whether the two accounts are distinct would be moot. In
general, so long as we never modify data objects, we can regard a
compound data object to be precisely the totality of its pieces. For
example, a rational number is determined by giving its numerator and
its denominator. But this view is no longer valid in the presence of
change, where a compound data object has an identity
that is
something different from the pieces of which it is composed. A bank
account is still the same
bank account even if we change the
balance by making a withdrawal; conversely, we could have two
different bank accounts with the same state information. This
complication is a consequence, not of our programming language, but of
our perception of a bank account as an object. We do not, for
example, ordinarily regard a rational number as a changeable object
with identity, such that we could change the numerator and still have
the same
rational number.
In contrast to functional programming, programming that makes extensive use of assignment is known as imperative programming. In addition to raising complications about computational models, programs written in imperative style are susceptible to bugs that cannot occur in functional programs. For example, recall the iterative factorial program from section \ref{sec:recursion-and-iteration}:
n) { return product; } else { return iter(counter*product, \end{JavaScriptClickable} \end{lrbox} \begin{JavaScriptClickable} /*!\href{https://sourceacademy.nus.edu.sg/playground#chap=3&prgrm=GYVwdgxgLglg9mABMAhtOAnGKA2AKMASkQG8AoRS0SWBRGKAUwzwAcM4ATEaAGgjjgmGYuUrj6wRHgFDmiAHyIipChPUZGUEBiTsuPKAG41EgL6JGOAM6NV6jVp1IGzGYLDCAVPu59TDoGBsp7MANQAjIQmDmamceKa2rr0wngRvFEmcWSo6Fi4eACs0UA}{\usebox\lstbox}!*/ counter+1); } } return iter(1,1); } \end{JavaScriptClickable}
n) { return product; \end{JavaScriptClickable} \end{lrbox} \begin{JavaScriptClickable} /*!\href{https://sourceacademy.nus.edu.sg/playground#chap=3&prgrm=GYVwdgxgLglg9mABMAhtOAnGKA2AKMASkQG8AoRSnAUykQAcM4ATEaRAXkQEYBuCqrUQQ44KNQyce-SsnDR4SGOIx5i5WbJjBEeEWImIAfIiKkBmzRlogMSRizZQZlgL6JqOAM7Vzlyw6s7Fz6YCqIAFQMTEHOFv7ComGGIUnhANTS8f7WULZKKmoumq4WpbK5+YjKEkVkpWSo6Fi4eACshLxAA}{\usebox\lstbox}!*/ } else { product = counter * product; counter = counter + 1; return iter(); } } return iter(); } \end{JavaScriptClickable}
counter = counter + 1; product = counter * product;
The complexity of imperative programs becomes even worse if we consider applications in which several processes execute concurrently. We will return to this in section \ref{sec:time-is-of-the-essence}. First, however, we will address the issue of providing a computational model for expressions that involve assignment, and explore the uses of objects with local state in designing simulations.
{ // Check authentication for joint account if (input_pw !== joint_pw) { return x => "Wrong joint account password"; \end{JavaScriptClickable} \end{lrbox} \begin{JavaScriptClickable} /*!\href{https://sourceacademy.nus.edu.sg/playground#chap=3&prgrm=GYVwdgxgLglg9mABAWwIYGsCmB9AVnGMKACgBtCsATbVCCAGkXLCuwAcB3R-QqdjgJSIA3gChEExACdMUEFKTFkmAM4rUAc0yNCbEH05CAvAD4R4yZYuWJAeluIAwgAtMEdIlT7XRGBFSwCIjAcFKIPESedHDgUNY2MMCIxLr6-IgAhEZG4QRE-EJiNsXSsvJIAB6IpogARADqUggaubxREDGRbKhqHKGUtQDc8ZYAvoiYpCqY5iU2HWAqUO2qKtjMVNVMFJjUtBBKq5ra2yy7BcNzVleS9k6u7p7emL7+gUghYRu77Z1xNxJEsl9qt1jtKMQAAzGbJ1ACSkFCMmgiAACj0VH0pANCiMAXcQWowWcIdDEJQ4KpPOTMGw4CoYMsHBxGc5KFJUBxUKREHAkpCdEh+pgpHibncoHBEFBVMtPtLXPDEVJkct0b1+rUUEctAA6RBiq4yOQKRBVGoNJpgFrfSi-WKIboa7FDQ1jCZTGZFAGWO4AQVIPK8UB8sDe8CQTumlEYxvKKzUP32f2lUpA01FPsscdNhLWtsuANGhuLY2GxdECyWjtkIpodC2aCw9Y6sWIAEZIQK6nA2C9ENN1MpagJhsQ+zKpC3iLUWSH2Zzaoxar3+4PUMOBAIO9DBog7vVWQuOCpEJ3ROPa1P9jO52yORwl3UAEYvEfbgAsu-3DgRHRVbhqhiWKUKIoh3AAshgMwAFJ5MsfrRLElYINW3QgKQLaNtBeDwZek4tsuq5IOuw7Lk00zPiAOLDBe6GYTes5Hg+T61BRmBUTi24AKzfgezGcqevGMCozgxKQdo5kgX5AA}{\usebox\lstbox}!*/ } else { const access_linked = linked_acc(message, linked_pw); // Check authentication for linked account if (access_linked(0) === "Incorrect Password") { // access_linked(0) does a deposit / withdrawal of 0, in order // to test for the "Incorrect Password" message. return x => "Wrong linked account password"; } else { // All authentication passed, return accessed account to user return access_linked; } } }; } \end{JavaScriptClickable}
distinctcompound data structures that share parts. Bugs can occur in our programs if we forget that a change to an object may also, as a
side effect,change a
differentobject because the two
differentobjects are actually a single object appearing under different aliases. These so-called side-effect bugs are so difficult to locate and to analyze that some people have proposed that programming languages be designed in such a way as to not allow side effects or aliasing (
should I set this variable before or after that oneconcerns that can complicate programming and obscure the important ideas.


3N/4D
Drive to Manali (9-10 Hours), valley of Gods. On the way cover Sundernagar Lake, Hanogi Mata Tempe, Pandoh Dam.River Raftting Point Reach in Manali check into the Hotel & Visit Local Sightseen Depand On Time Stay and Meals as per Package
Today morning after breakfast have excursion trip to Sollang Valley where you can admire the enchanting sights of the surroundings and can enjoy various thrilling adventure activities like Paragliding, Zorbing and Horse Riding etc during summer time. As the winter approaches, the same Sollang Valley takes a shape of a Snowy Blanket, thus forming a perfect vista for several snow activities. After enjoying this Sollang outing return to hotel for dinner and Overnight stay
Today morning proceed visit to city tour visiting Hadimba Devi Temple, Old Manali, Club House, Local Mall Road, Van vihar, Vashisht Village,(famous for its hot water springs) and Tibetan Monastery Naggar. Enroute will visit Gaytri temple in Jagatsukh village. visit Naggar castle and Roerick Art gallery. after Naggar village visit and drive. Evening free to explore the shopping mall of Manali. Dinner & overnight stay at hotel. In the evening return to Manali. Stay and meals as per package
Uarly Morning Hotel Check out & Go To Ambala Railway Station In the evening you will reach Ambala By Evening Train Back To Home With Sweet Memories


Selecting to go with a catamaran vs. mono hull and then how we ended up with our Manta.
A few projects prior to leaving
The boat was in good shape but we never seem to leave it alone and we felt a few improvements were needed.
| Our boat is a 1994 Manta catamaran, hull number 7. Manta built 8 of the 38 foot boats and then stretched the bow by 1 foot and the stern by 1 foot for the 40. After several years Manta then went ahead and stretched the 40 by adding 2 feet on the stern and made the Manta 42. There are more Manta 42s than either the 38s or 40s combines. There is a great group of Manta owners out cruising and a chat site on Yahoo groups who will offer ideas and opinions on all issues. | |||
|
Builder: Endeavor Yachts |
Designer: |
LOA: 38 |
Beam: 21 |
|
LWL: 37 |
Draft: 3’ 9” |
Displacement: 15,000 |
Sail Area: 757 |
|
Hull Speed: 14 knots |
Engine: (2) 29 HP |
Cruising Speed: 7 knots |
|
|
Fuel: 100 gallons |
Water: 100 gallons |
Waste: 30 gallons |
|
|
Manta History: The original concept of the Manta Catamaran was to offer the best catamaran for serious live aboard, and/or extended cruisers, manufactured to a very high standard on a limited production basis, while still offering excellent value.
The Manta 38 was designed in 1993 and went into production in St. Petersburg Florida in 1996. The Manta 38 incorporated many unique and desirable features for her time such as molded cross beam and strut, one piece hull and deck molds, cambered bridge deck and decks, and high efficiency keel and rudder designs.
The interior layout was also very unique including, one hull dedicated to owner’s cabin, large shower stalls, and galley up. Additionally the Manta had the distinction of being the first production catamaran in her size range designed to be single handed, incorporating, self-tacking jib, and all sail handling accomplished at the helm with the aid of an electric winch |
|
||
|
Manta 40 Catamaran Sales Video http://www.youtube.com/watch?v=ztOzsQWVKuQ
Former web sites prior to business going out of business www.mantausa.com www.mantacatamarans.com |
|||


Pre-Black Friday tech deals (11-24-2019)
by Brad LinderBlack Friday deals are starting to go live almost a week early. Best Buy is offering deep discounts on hundreds of items. Google and Fitbit are getting in on the action with sales on Pixel tablets and phones and Fitbit smartwatches and activity trackers.
And many of Amazon’s device deals are live — you can pick up a hackable Amazon Fire tablet for $30 and up, or spend just $20 to get a Fire TV Stick plus a 2-month subscription to Showtime.
Here are some of the best pre-Black Friday tech deals as of November 24th, 2019.
Media Streamers
- Amazon Fire TV Stick (1080p) for $20 – Amazon (plus 15-percent cash back for Prime Visa holders)
- Amazon Fire TV Stick (1080p) + 2-months Showtime for $20 – Amazon
- Amazon Fire TV Stick 4K for $25 – Amazon (plus 15-percent cash back for Prime Visa holders)
- Roku Streaming Stick+ (4K) for $30 – Roku
- Amazon Fire TV Stick (1080p) + Echo Dot for $42 – Amazon (coupon: ECHOTV)
- Amazon Fire TV Stick 4K + Echo Dot for $47 – Amazon (coupon: ECHO4K)
- Amazon Fire TV Stick 4K + Fire TV Blaster + Echo Dot for $80 – Amazon
- Amazon Fire TV Cube for $90 – Amazon (plus 15-percent cash back for Prime Visa holders)
Tablets
- Amazon Fire 7″ tablet for $30 and up – Amazon
- Amazon Fire HD 8″ for $50 and up – Amazon
- Amazon Fire HD 10 for $100 and up – Amazon
- Amazon Fire 7 Kids Edition for $60 (or 2 for $100) – Amazon
- Amazon Fire HD 8 Kids Edition for $80 (or 2 for $140) – Amazon
- Amazon Fire HD 10 Kids Edition for $150 (or 2 for $280) – Amazon
- Google Pixel Slate tablet + keyboard & pen for $449 and up – Google Store (or Best Buy)
- Microsoft Surface Pro 7 + Type Cover for $649 and up – Microsoft Store
eReaders
- Amazon Kindle (frontlit) + $5 eBook credit & 3-months Kindle Unlimited for $60 – Amazon
- Amazon Kindle Kids Edition + $5 eBook credit for $80 – Amazon
- Amazon Kindle Paperwhite + $5 eBook credit for $85 – Amazon
- Refurb Amazon Kindle Paperwhite (7th-gen) for $70 – Amazon
- Amazon Kindle Oasis (9th-gen) w/8GB for $150 – Amazon (or 32GB for $180)
- Refurb Aazon Kindle Voyage for $100 – DailySteals
Chromebooks
- Samsung Chromebook 11.6″ w/Atom x5-E8000/2GB/16GB for $90 – Best Buy
- Samsung Chromebook 11.6″ w/Atom x5-E8000/4GB/32GB for 119 – Best Buy
- Lenovo Chromebook 11.6″ convertible w/MT8173C/4GB/32GB for $179 – Best Buy
- Acer Spin 11.6″ convertible w/Celeron N3350/4GB/32GB for $219 – Best Buy
- Samsung Chromebook Plus 12.2″ convertible w/Celeron 3965Y/4GB/32GB for $299 – Best Buy
- HP Chromebook 14″ convertible w/Core i3-8130U/8GB/64GB for $349 – Best Buy
Windows Laptops
- Lenovo IdeaPad 14″ w/AMD A6-9220e/4GB/64GB for $130 – Best Buy
- HP Pavilion x360 11.6″ convertible w/Pentium Silver N5000/4GB/128GB for $280 – Best Buy
- Asus ImagineBook MJ401TA 14″ laptop w/Core m3-8100Y/4GB/128GB for $300 – Best Buy
- HP Pavilion x360 14″ convertible w/Core i5-10210U/8GB/16GB Optane + 256GB SSD for $500 – Best Buy
- Dell XPS 13 w/Core i7-8565U/8GB/256GB for $1050 – Dell
Wearables
- Fitbit Versa 2 for $150 and up – Fitbit
- Fitbit Ionic for $200 and up – Fitbit
- Fitbit Inspire for $50 and up – Fitbit
- Fitbit Ace 2 for $50 – Fitbit
PC accessories
- Logitech MX Anywhere 2 wireless mouse for $40 – Best Buy
- Logitech K780 wireless keyboard for $45 – Best Buy
- Logitech MK540 Advancesed wireless mouse & keyboard for $32 – Best Buy
- Logitech K400 Plus wireless keyboard & touchpad for $18 – Best Buy
- Logitech C920 Pro webcam for $50 – Best Buy
- WD Easystore 5TB USB 3.0 portable hard drive for $90 – Best Buy
- WD Easystore 12TB USB 3.0 external desktop hard drive for $180 – Best Buy
Wireless headphones
- JLab Audio JBuds Air True Wireless earbuds for $30 – Best Buy
- Sony WH-CH700N wireless noise-cancelling over-ear headphones for $90 – Best Buy
- Sony WH-XB900N wireless noise cancelling over-ear headphones for $150 – Best Buy
- Sony WH-1000XM3 wireless noise-cancelling over-ear headphones for $280 – Best Buy
Wireless speakers
- UE Wonderboom portable Bluetooth speaker for $40 – Best Buy
- JBL Flip 4 portable Bluetooth speaker for $60 – Best Buy
- UE Megaboom LE portable Bluetooth speaker for $100 – Best Buy
Networking
- Netgear Nighthawk AC2600 WiFi router for $90 – Best Buy
- Netgear Orbi AC1200 mesh WiFi router 4-pack for $250 – Best Buy
Digital media and streaming
- 4-months Amazon Music Unlimited subscription for $1 – Amazon
- 3-month Deezer Premium subscription for free – Deezer
Other
- Buy a Google Pixel 4 and get 50% off in credit (save up to $500) – Google Fi
- Unlocked Google Pixel 4 for $599 and up ($200 off) – Google Store (or Amazon)
- Lenovo smart clock w/Google Assistant for $40 – Lenovo
- Amazon Echo Show 2-pack for $100 – QVC