Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy.

HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.

As a nonlinear dimension reduction technique, the diffusion map (DM) has been widely used. In DM, kernels play an important role for capturing the nonlinear relationship of data. However, only symmetric kernels can be used now, which prevents the use of DM in directed graphs, trophic networks, and other real-world scenarios where the intrinsic and extrinsic geometries in data are asymmetric. A promising technique is the magnetic transform which converts an asymmetric matrix to a Hermitian one. However, we are facing essential problems, including how diffusion distance could be preserved and how divergence could be avoided during diffusion process. Via theoretical proof, we successfully establish a diffusion representation framework with the magnetic transform, named MagDM. The effectiveness and robustness for dealing data endowed with asymmetric proximity are demonstrated on three synthetic datasets and two trophic networks.

Japan zoo staffer allegedly dumps wife's body inside incinerator A popular Japanese zoo has delayed its opening for the summer season after an employee told police he had disposed of his wife's body in the zoo's incinerator, local media reported. Asahiyama Zoo in the northern city of Asahikawa was supposed to welcome visitors on Wednesday in time for Japan's Golden Week holiday period, after completing a three-week maintenance break. But the city government says the zoo will now remain closed until Friday as investigations continue. Last week, police searched the zoo grounds after the employee told them he had disposed of his wife's body in the zoo's incinerator, local media reported. The incinerator was used to dispose of animal carcasses when they died.

Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their predictions. Outlier exposure addresses this issue by introducing an additional loss that encourages low-confidence predictions on OoD data during training. While outlier exposure has shown promising potential in improving OoD detection performance, all previous studies on outlier exposure have been limited to utilizing visual outliers.

Kendall says AI is the'currency of the future'. Kendall says AI is the'currency of the future'. Trump is volatile, capricious and unreasonable - but he belongs to the old world of analogue power. D onald Trump is not impressed by soft power. He respects hard men with military muscle.

Communication compression is a common technique in distributed optimization that can alleviate communication overhead by transmitting compressed gradients and model parameters. However, compression can introduce information distortion, which slows down convergence and incurs more communication rounds to achieve desired solutions. Given the trade-off between lower per-round communication costs and additional rounds of communication, it is unclear whether communication compression reduces the total communication cost. This paper explores the conditions under which unbiased compression, a widely used form of compression, can reduce the total communication cost, as well as the extent to which it can do so. To this end, we present the first theoretical formulation for characterizing the total communication cost in distributed optimization with unbiased compressors. We demonstrate that unbiased compression alone does not necessarily save the total communication cost, but this outcome can be achieved if the compressors used by all workers are further assumed independent. We establish lower bounds on the communication rounds required by algorithms using independent unbiased compressors to minimize smooth convex functions and show that these lower bounds are tight by refining the analysis for ADIANA. Our results reveal that using independent unbiased compression can reduce the total communication cost by a factor of up to Θ( p min{n,κ}) when all local smoothness constants are constrained by a common upper bound, where nis the number of workers and κis the condition number of the functions being minimized. These theoretical findings are supported by experimental results.

Japan's Terra Drone expands investment in Ukraine drone sector A soldier from Ukraine's Taifun unmanned aerial vehicle unit holds a new model Marsianin attack drone on April 7 in Kharkiv region, Ukraine. Tokyo-based Terra Drone is expanding its investment in Ukrainian interceptor drones as it looks to bring battlefield-tested technology back to Japan to tap into a multibillion-dollar defense budget for unmanned systems. On Tuesday, Terra Drone CEO Toru Tokushige said the company was entering a new strategic partnership with Ukraine's WinnyLab to develop fixed-wing interceptor drones. It comes after the company announced in March that it would make an investment in Ukraine's Amazing Drones to develop vertical take-off interceptor drones. "Starting with interceptor drones we are looking for products that are good for increasing the defensive power of Ukraine and also the defensive power of Japan," Tokushige said in an interview.

Five former US officials, including a former top military lawyer, have criticised the Pentagon for not acknowledging potential American involvement in a deadly strike on an Iranian school earlier this year. Some of those officials said it was highly unusual not to release even basic details of the strike after such a length of time. A missile hit a primary school in Minab during the opening salvos of the US-Israeli war on February 28, killing 168 people including around 110 children according to Iranian officials. In the two months since then the Pentagon has said only that the incident is under investigation. US media reported in early March that US military investigators believed American forces were likely responsible for hitting the school unintentionally but had not reached a final conclusion.