Abstract: New methods and kits for treating diseases caused or exacerbated by overactivated EphA4 signaling are provided. The method includes administering to a subject in need thereof an effective amount of a small molecule compound inhibitor for EphA4 signaling. Also provided are methods for identifying additional compounds as therapeutic agents useful for treating conditions involving overly active EphA4 signaling.

Abstract: Compositions and methods are provided for the treatment or prophylaxis of amyloid pathologies such as Alzheimer's Disease.

Abstract: An exhaust system for discharging from semiconductor manufacturing equipment a hazardous gas includes: a main exhaust pipe above the semiconductor manufacturing equipment and having a top surface on a first side and a bottom surface on a second side, a first branch pipe connected to a source of a gas mixture containing the hazardous gas on the second side and connected to the main exhaust pipe through the top surface, a second branch pipe connected to a gas box on the second side and connected to the main exhaust pipe through the bottom surface, and a detector on the second branch pipe configured to detect presence of the hazardous gas and downstream to the gas box. The first and the second branch pipes are connected to the main exhaust pipe at a first location and a second location, respectively. The first location is more upstream than the second location.

Abstract: A modular adversarial training data generator (“generator”) generates adversarial training data for a machine learning (ML) model to detect malicious protocol data units (PDUs). The adversarial training data mimics high volume cyberattacks by perturbing PDUs to bypass malicious detection systems. For HyperText Transfer Protocol (HTTP) PDUs, the generator rearranged, replaces, and grid searches values of HTTP header fields to generate the adversarial training data. The generator further biases grid search based on metrics for values of HTTP header fields that quantify impact of replacing the values on malicious verdicts by ML models trained on the adversarial training data.

Abstract: Disclosed are systems and techniques for image processing. For example, a computing device can generate, using a multi-task model, a segmentation output and a normal output based on image(s) of a scene and a gravity vector for the scene. The computing device can learn semantic prediction(s) based on comparing the segmentation output to at least one ground truth semantic segmentation map. The computing device can also learn normal prediction(s) based on comparing the normal output to at least one ground truth normal map. The computing device can extract a semantics normal from the semantic prediction(s) and the normal prediction(s). The computing device can optimize a regularization loss based on the semantics normal and the gravity vector for the scene by learning gravity-normal regularization(s) for the scene. The computing device can determine final semantic labels for regions of the scene based on the gravity-normal regularization(s).

Abstract: A system stores first and second images generated by first and second cameras; applies a segmentation model to the first image to generate a first segmentation mask identifying object instances; applies the segmentation model to the second image to generate a second segmentation mask identifying the object instances; projects the first segmentation mask to a viewpoint of the second camera to generate a first projected segmentation mask; converts the first projected segmentation mask and the second segmentation mask to first and second semantic masks, respectively; and computes a first similarity value based on the first and second semantic masks. This may be repeated exchanging the first and second images to compute a second similarity value. The system determines a loss value based on the first similarity value and the second similarity value and trains the segmentation model based on the loss value.

Abstract: A method of setting a display mode includes installing an application on a device, the device including a first screen; triggering at least one button of a display, the display providing display information to the application installed on the device through a data channel protocol, the display including a second screen; and the application switching the device and the display to the display mode based on the display information.

Abstract: Various embodiments provide a system, method, and device for applying a C2 machine learning-based detection framework. The method incudes (i) generating a fuzzing based on a C2 machine-learning detection model using a large learning model for performing profile-based seed generation; and (ii) detecting C2 traffic using the C2 machine learning detection model.

Abstract: An image displaying device includes a planar display panel and a light penetrating unit. The planar display panel displays a plane image. The planar display panel at least includes a first pixel group, a second pixel group and a third pixel group. The second pixel group is located between the first pixel group and the third pixel group. When vision passes through the light penetrating unit toward the planar display panel, the vision acquires a second distance of a second imaging position within the plane image relevant to the second pixel group relative to the planar display panel being greater than a first distance of a first imaging position within the plane image relevant to the first pixel group relative to the planar display panel and a third distance of a third imaging position within the plane image relevant to the third pixel group relative to the planar display panel.

Abstract: A process for retting biomass includes contacting an enzyme solution with the biomass, degrading at least a portion of the pectin in the biomass using the enzyme to release monomers, proliferating bacteria using the monomers as a feedstock, and separating the cellulosic fibers after degrading at least the portion of the pectin to produce natural fibers. The enzyme solution comprises an enzyme, and the biomass comprises pectin and cellulosic fibers.

Abstract: An electronic device includes a shell element, at least one electronic element, a printed circuit board, and an anti-scalding mask. The shell element has an accommodating space and a metal surface. At least one electronic element is disposed in the accommodating space. The printed circuit board is disposed in the accommodating space and is electrically connected to the at least one electronic element. The anti-scalding mask covers and is disposed on the metal surface. The anti-scalding mask is made of a low thermal conductivity material, and a thermal conductivity of the low thermal conductivity material is less than or equal to 20 W/mK.

Abstract: An exploit detector hooks functions of a WebAssembly engine API that include functions invoked when creating a WebAssembly instance, when permissibly modifying read-write-execute (RWX) memory allocated for a WebAssembly instance, and when calling an exported WebAssembly function. Upon invocation of the hooked instance creation function(s), the detector obtains an address of RWX memory allocated for the instance, reads data stored there, and records an association between the address and the data. If the hooked function(s) for permissibly modifying RWX memory is invoked, the detector updates the data stored in the recorded association with the modified data read from the RWX memory. Based on invocation of the hooked function(s) associated with WebAssembly export function calls, the detector reads the current data in the RWX memory to be executed and compares this data to the data recorded in association with the address. If the actual and expected data differ, the detector detects a browser exploit.

Abstract: An in-kernel virtual machine (“VM”) instantiated in the kernel space of a physical or virtual machine (“machine”) attaches code hooks at a network interface of the machine. The code hooks redirect network traffic from the network interface to a module that generates feature values at the kernel space from protocol data units of network traffic received by the network interface. The machine passes the feature values from kernel space to user space via zero-copy shared memory and a machine learning model in the user space obtains network traffic verdicts as outputs from inputting the feature values. The machine passes the verdicts from user space to kernel space via the zero-copy shared memory and the in-kernel VM performs corrective action based on malicious verdicts.

Abstract: In a described example, an example no-lead semiconductor device package includes: a die pad in a central portion of a partially etched leadframe and terminals. The terminals include a device side surface formed in an upper layer of the partially etched leadframe, and a first exterior end in the upper layer; a board side surface formed in a lower layer of the partially etched leadframe extending from the upper layer; a second exterior end in the lower layer, the second exterior end inset from the first exterior end, and an inset portion extending from the first exterior end to the second exterior end. A semiconductor die is mounted to the die pad. Mold compound covers the semiconductor die, the second exterior end of the terminals and the inset portion of the terminals are exposed from the mold compound, with the mold compound extending along sides of the terminals.

Abstract: A system, method, and device for detecting Command and Control (C2) traffic is disclosed. The method includes (i) converting, by one or more processors, a header for network traffic to a header representation having a smaller dimensionality than the header, (ii) querying a classifier based at least in part on the header representation to obtain a traffic classification, (iii) automatically detecting C2 traffic based at least in part on the traffic classification, and (iv) handling the network traffic based at least in part on the traffic classification.

Abstract: A system, method, and device for detecting Command and Control (C2) traffic is disclosed. The method includes (i) converting a monitored traffic sample to an image representation, (ii) querying a classifier based at least in part on the image representation to obtain a traffic classification, (iii) automatically detecting C2 traffic based at least in part on the traffic classification, and (iv) handling the network traffic based at least in part on the traffic classification.

Abstract: An air conditioner includes an air conditioner body including a cross-flow air duct and an air outlet in communication with the cross-flow air duct, a cross-flow impeller arranged at the cross-flow air duct and located upstream of the air outlet, and an air guide assembly including an air guide member rotatable around a pivot axis at the air outlet. The air conditioner body includes a first volute and a second volute arranged opposite to each other in a cross section perpendicular to a rotation axis of the cross-flow impeller. The cross-flow air duct is located between the first volute and the second volute. A set air duct is formed between the air guide member and the first volute when an air output volume of the air conditioner is maximum. A width of the set air duct in the cross section first increases and then decreases along an air output direction.

Abstract: An electronic device includes a housing, a first metal trace, a conductive pillar, a circuit board, and a metal connecting element. The housing includes a conductive via and a concave hole, and the conductive via passes through the housing. The first metal trace is disposed on an inner surface of the housing, and the conductive via is electrically connected to the first metal trace. The conductive pillar is disposed in the concave hole and exposed from the housing, and the conductive pillar is electrically connected to the conductive via. The circuit board is disposed inside the housing. The metal connecting element is disposed on the circuit board and electrically connected to the first metal trace.

Abstract: Certain aspects of the present disclosure provide techniques for training and using an instance segmentation neural network to detect instances of a target object in an image. An example method generally includes generating, through an instance segmentation neural network, a first mask output from a first mask generation branch of the network. The method further includes generating, through the instance segmentation neural network, a second mask output from a second, parallel, mask generation branch of the network. The second mask output is typically of a lower resolution than the first mask output. The method further includes combining the first mask output and second mask output to generate a combined mask output. Based on the combined mask output, an output of the instance segmentation neural network is generated. One or more actions are taken based on the generated output.

Abstract: Techniques for inline exploit detection via loose condition forwarding and cloud analysis (e.g., cloud-based security analysis) are disclosed. In some embodiments, a system/process/computer program product for inline exploit detection via loose condition forwarding and cloud analysis includes monitoring network traffic of a session at a security platform; selecting a subset of the monitored network traffic associated with the session to send to a cloud-based security service for analysis based on a security policy, wherein the selected subset of the monitored network traffic is determined to be associated with suspicious session traffic that is selected using a prefilter condition (e.g., a prefilter signature, heuristics, machine learning implemented prefiltering condition, etc.) for an unknown exploit; and receiving, from the cloud-based security service, results of the analysis based on the security policy, and performing a responsive action based on the results of the analysis based on the security policy.