Abstract: A model predicts the geometry of both visible and occluded traversable surfaces from input images. The model may be trained from stereo video sequences, using camera poses, per-frame depth, and semantic segmentation to form training data, which is used to supervise an image to image network. In various embodiments, the model is applied to a single RGB image depicting a scene to produce information describing traversable space of the scene that includes occluded traversable. The information describing traversable space can include a segmentation mask of traversable space (both visible and occluded) and non-traversable space and a depth map indicating an estimated depth to traversable surfaces corresponding to each pixel determined to correspond to traversable space.

Abstract: A method, system, and computer program product generate, with a payment network, a first value (a) and a second value (ga), the second value (ga) based on the first value (a) and a generator value (g); generate, with the payment network, a plurality of random merchant numbers (mi) for a respective plurality of merchant banks; determine, with the payment network, a merchant product (M) based on a product of the plurality of random merchant numbers (mi); generate, with the payment network, a public key (pki) based on the second value (ga), the merchant product (M), and the random merchant number (mi) and a random key (rki) based on the merchant product (M) and the random merchant number (mi) for each respective merchant bank; and communicate, with the payment network, the public key (pki) and the random key (rki) to at least one respective merchant bank.

Abstract: A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.

Abstract: A user device generates an initiate interaction request message comprising a state commitment. The user device provides the initiate interaction request message to a first server computer, which creates a verify state request message comprising an interaction index, an interaction index commitment, and a first commitment signature formed from the state commitment and the interaction index commitment. The user device receives the verify state request message, then generates a modified verify state request message comprising a user device public key, the state commitment, the interaction index commitment, and the first commitment signature. The user device provides the modified verify state request message to a second server computer. The second server computer verifies the state commitment, verifies the first commitment signature, and creates a second commitment signature formed from the state commitment and the interaction index commitment.

Abstract: A scene reconstruction model is disclosed that outputs a heightfield for a series of input images. The model, for each input image, predicts a depth map and extracts a feature map. The model builds a 3D model utilizing the predicted depth maps and camera poses for the images. The model raycasts the 3D model to determine a raw heightfield for the scene. The model utilizes the raw heightfield to sample features from the feature maps corresponding to positions on the heightfield. The model aggregates the sampled features into an aggregate feature map. The model regresses a refined heightfield based on the aggregate feature map. The model determines the final heightfield based on a combination of the raw heightfield and the refined heightfield. With the final heightfield, a client device may generate virtual content augmented on real-world images captured by the client device.

Abstract: A method is disclosed. The method includes receiving, by a user device, an encrypted message from a server computer. The encrypted message is a message encrypted with a master secret key or a key derived from the master secret key. The user device signs the encrypted message with a secure element private key. The user device, using a whitebox, cryptographically recovers a secure element public key from a certified key using a server computer public key. The certified key is certified by the server computer and based on at least the secure element public key. The user device, using the whitebox, cryptographically recovers the encrypted message from the signed encrypted message using the secure element public key. The user device, using the whitebox, decrypts the encrypted message using the master secret key or the key derived from the master secret key in the whitebox to obtain the message.

Abstract: An energy absorbing strut having, a first end coupled with an inner cylinder, and a second end connected with a hollow rod extending within the inner cylinder. A piston is carried by the rod having an outer surface sealing against an inside diameter of the inner cylinder and forming a compression chamber and a rebound chamber bounded by the piston, the rod having an internal passageway communicating between the compression chamber and the rebound chamber. An inertial mass carried by the rod movable axially on the rod between a closed position against and annular rod passageway and an open position opening the rod passageway and allowing the flow of a hydraulic fluid between the compression chamber and the rebound chamber. A spring acts on the inertial mass biasing the inertial mass toward the closed position. The energy absorbing strut may be used in a blast mitigation system for a military vehicle or other applications for providing shock isolation between two structures.

Abstract: Described is a robotic apparatus (10) for investigating a confined area comprising: an articulated robot (20) for insertion into a confined area, the robotic apparatus further comprising a robot control system (30) for controlling the articulated robot. Further, the robot control system comprises a control unit (50), a robot driving means, a seal (70) for isolating the confined area from the external environment and at least one transmission member (80), wherein the control unit is configured to send control signals to the robot driving means, and the at least one transmission member extends from the robot driving means to connect to the articulated robot, the at least one transmission member extending through the seal.

Abstract: A depth prediction model for predicting a depth map from an input image is disclosed. The depth prediction model leverages wavelet decomposition to minimize computations. The depth prediction model comprises a plurality of encoding layers, a coarse prediction layer, a plurality of decoding layers, and a plurality of inverse discrete wavelet transforms (IDWTs). The encoding layers are configured to input the image and to downsample the image into feature maps including a coarse feature map. The coarse depth prediction layer is configured to input the coarse feature map and to output a coarse depth map. The decoding layers are configured to input the feature maps and to predict wavelet coefficients based on the feature maps. The IDWTs are configured to upsample the coarse depth map based on the predicted wavelet coefficients to the final depth map at the same resolution as the input image.

Abstract: A method, system, and computer program product generate, with a payment network, a first value (a) and a second value (ga), the second value (ga) generated based on the first value (a) and a generator value (g); generate, with the payment network, a plurality of random merchant numbers (mi) for a respective plurality of merchant banks; determine, with the payment network, a merchant product (M) based on a product of the plurality of random merchant numbers (mi); generate, with the payment network, a public key (pki) based on the second value (ga), the merchant product (M), and the random merchant number (mi) and a random key (rki) based on the merchant product (M) and the random merchant number (mi) for each respective merchant bank; and communicate, with the payment network, the public key (pki) and the random key (rki) to at least one respective merchant bank.

Abstract: A method, system, and computer program product generate, with a payment network, a first value (a) and a second value (ga), the second value (ga) generated based on the first value (a) and a generator value (g); generate, with the payment network, a plurality of random merchant numbers (mi) for a respective plurality of merchant banks; determine, with the payment network, a merchant product (M) based on a product of the plurality of random merchant numbers (mi); generate, with the payment network, a public key (pki) based on the second value (ga), the merchant product (M), and the random merchant number (mi) and a random key (rki) based on the merchant product (M) and the random merchant number (mi) for each respective merchant bank; and communicate, with the payment network, the public key (pki) and the random key (rki) to at least one respective merchant bank.

Abstract: A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.

Abstract: Methods, systems 1 and devices such as robots 8, 9 for farming are disclosed. An autonomous monitoring robot is configured to traverse a farm plot and generate, from a sensor set of the monitoring robot, a farm plot data set. The farm plot data set is processed to generate operating instructions for a tending robot. The tending robot is arranged to execute the operating instructions so as to traverse the farm plot and performs tending tasks on it including such as seed-planting, weeding, and applying crop treatments such as fertiliser, fungicide, herbicide or pesticide.

Abstract: A multi-frame depth estimation model is disclosed. The model is trained and configured to receive an input image and an additional image. The model outputs a depth map for the input image based on the input image and the additional image. The model may extract a feature map for the input image and an additional feature map for the additional image. For each of a plurality of depth planes, the model warps the feature map to the depth plane based on relative pose between the input image and the additional image, the depth plane, and camera intrinsics. The model builds a cost volume from the warped feature maps for the plurality of depth planes. A decoder of the model inputs the cost volume and the input image to output the depth map.

Abstract: A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.

Abstract: A method, system, and computer program product generate, with a payment network, a first value (a) and a second value (ga), the second value (ga) based on the first value (a) and a generator value (g); generate, with the payment network, a plurality of random merchant numbers (mi) for a respective plurality of merchant banks; determine, with the payment network, a merchant product (M) based on a product of the plurality of random merchant numbers (mi); generate, with the payment network, a public key (pki) based on the second value (ga), the merchant product (M), and the random merchant number (mi) and a random key (rki) based on the merchant product (M) and the random merchant number (mi) for each respective merchant bank; and communicate, with the payment network, the public key (pki) and the random key (rki) to at least one respective merchant bank.

Abstract: A method, system, and computer program product generate, with a payment network, a first value (a) and a second value (ga), the second value (ga) generated based on the first value (a) and a generator value (g); generate, with the payment network, a plurality of random merchant numbers (mi) for a respective plurality of merchant banks; determine, with the payment network, a merchant product (M) based on a product of the plurality of random merchant numbers (mi); generate, with the payment network, a public key (pki) based on the second value (ga), the merchant product (M), and the random merchant number (mi) and a random key (rki) based on the merchant product (M) and the random merchant number (mi) for each respective merchant bank; and communicate, with the payment network, the public key (pki) and the random key (rki) to at least one respective merchant bank.

Abstract: Methods and systems for privacy-preserving identity attribute verification are presented. During an interaction between a relying entity and a user, a relying entity computer can transmit a policy token to a user device. The policy token may indicate the information needed by the relying entity in order to perform the interaction. The user device can verify the policy token, then use the policy token in conjunction with an identity token to generate a zero-knowledge proof. The user device may transmit the zero-knowledge proof to an identity service provider computer. The identity service provider computer may verify the zero-knowledge proof, then generate a verification message. The identity service provider computer may sign the verification message and transmit the signed verification message to the relying entity computer. The relying entity computer may verify the verification message and complete the interaction with the user.

Abstract: Methods and systems are presented for indicating the proximity of a process requirement metric to a corresponding system process requirement in a physiological monitor. Metrics are determined based on a received physiological signal, such as a PPG signal. At least one of the metrics is compared to a corresponding system process requirement, such as a threshold, which can be used to trigger a system process, such as an alarm, a recalibration, or a measurement. The proximity of the metric to the corresponding system process requirement is calculated and indicated. Indicating the proximity to a system process requirement may provide valuable information to clinicians and allow them to manually trigger system processes, such as measurements or recalibrations, when a metric is close to a system process requirement for a significant period of time.

Abstract: Techniques for safe oversubscription of connection tracking entries are described. A method for safe oversubscription of connection tracking entries may include receiving a request for an allocation of a resource on a physical host in a provider network, the request received by a resource allocation monitor from an instance hosted by the physical host, determining a resource availability on the physical host, the resource availability based on a total amount of the resource on the physical host, a reserved amount of the resource to a plurality of instances hosted by the physical host, and a shared amount of the resource, and granting or denying the resource allocation based at least on the determined resource availability.