Graph-based machine learning models for materials properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, our molecular graph featurization method for machine learning various spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used machine learning methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary transition metal complexes, the best MBE models achieve mean absolute errors of 2.75 kcal/mol on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30-40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit mean absolute errors of 4.00 kcal/mol (i.e., a 0.73 kcal/mol reduction) on the spin-splitting energies and 0.53 eV (i.e., a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces.

Have you ever bought an app or game from a digital storefront, only for it to suddenly disappear and become inaccessible without warning? It's both confusing and frustrating, which is why some governments are stepping in and hoping to make things clearer for consumers. Gavin Newsom, governor of California, recently signed a new law (AB 2426) that involves the purchasing of digital goods and services. Under the new law, online storefronts that sell digital copies of video games, music, movies, TV shows, and ebooks must be explicit as to whether customers actually own what they're purchasing. Some digital services are already adapting to the new regulations, which aren't set to be enforced until next year.

Robot vacuums across the country were hacked in the space of several days, according to reporting by ABC News. This allowed the attackers to not only control the robovacs, but use their speakers to hurl racial slurs and abusive comments at anyone nearby. All of the affected robots were of the same make and model, the Chinese-made Ecovacs Deebot X2s. This particular robovac has developed a reputation for being easy to hack, thanks to a critical security flaw. ABC News, for instance, was able to get full control over one of the robots, including the camera.

A trove of leaked internal messages and documents from the militia American Patriots Three Percent--also known as AP3--reveals how the group coordinated with election denial groups as part of a plan to conduct paramilitary surveillance of ballot boxes during the midterm elections in 2022. This information was leaked to Distributed Denial of Secrets (DDoSecrets), a nonprofit that says it publishes hacked and leaked documents in the public interest. The person behind these AP3 leaks is an individual who, according to their statement uploaded by DDoSecrets, infiltrated the militia and grew so alarmed by what they were seeing that they felt compelled to go public with the information ahead of the upcoming presidential election. Election and federal officials have already voiced concern about possible voter intimidation this November, in part due to the proliferation of politically violent rhetoric and election denialism. Some right-wing groups have already committed to conducting surveillance of ballot boxes remotely using AI-driven cameras.

In this paper, we propose to use the concept of local fairness for auditing and ranking redistricting plans. Given a redistricting plan, a deviating group is a population-balanced contiguous region in which a majority of individuals are of the same interest and in the minority of their respective districts; such a set of individuals have a justified complaint with how the redistricting plan was drawn. A redistricting plan with no deviating groups is called locally fair. We show that the problem of auditing a given plan for local fairness is NP-complete. We present an MCMC approach for auditing as well as ranking redistricting plans.

Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where malicious human-imperceptible perturbations are included in the input to the deep network to fool it into making a wrong classification. Recent studies have demonstrated that neural Ordinary Differential Equations (ODEs) are intrinsically more robust against adversarial attacks compared to vanilla DNNs. In this work, we propose a neural ODE with Lyapunov-stable equilibrium points for defending against adversarial attacks (SODEF). By ensuring that the equilibrium points of the ODE solution used as part of SODEF are Lyapunov-stable, the ODE solution for an input with a small perturbation converges to the same solution as the unperturbed input. We provide theoretical results that give insights into the stability of SODEF as well as the choice of regularizers to ensure its stability.

An Indian textile baron has revealed that he was duped out of 70 million rupees ( 833,000) by online scammers impersonating federal investigators and even the Supreme Court chief justice. The fraudsters posing as officers from India's Central Bureau of Investigation (CBI) called SP Oswal, chairman and managing director of the textile manufacturer Vardhman, on August 28 and accused him of money laundering. For the next two days, Oswal was under digital surveillance as he was ordered to keep Skype open on his phone 24/7 during which he was interrogated and threatened with arrest. The fraudsters also conducted a fake virtual court hearing with a digital impersonation of Chief Justice of India DY Chandrachud as the judge. Oswal paid the amount after the court verdict via Skype without realising that he was the latest victim of an online scam using a new modus operandi, called "digital arrest".

The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores. Nonetheless, we note that if the loss trend of the outputs is slightly perturbed, SQAs could be easily misled and thereby become much less effective. Following this idea, we propose a novel defense, namely Adversarial Attack on Attackers (AAA), to confound SQAs towards incorrect attack directions by slightly modifying the output logits. In this way, (1) SQAs are prevented regardless of the model's worst-case robustness; (2) the original model predictions are hardly changed, i.e., no degradation on clean accuracy; (3) the calibration of confidence scores can be improved simultaneously. Extensive experiments are provided to verify the above advantages.

We study the problem of efficient adversarial attacks on tree based ensembles such as gradient boosting decision trees (GBDTs) and random forests (RFs). Since these models are non-continuous step functions and gradient does not exist, most existing efficient adversarial attacks are not applicable. Although decision-based black-box attacks can be applied, they cannot utilize the special structure of trees. In our work, we transform the attack problem into a discrete search problem specially designed for tree ensembles, where the goal is to find a valid leaf tuple'' that leads to mis-classification while having the shortest distance to the original input. With this formulation, we show that a simple yet effective greedy algorithm can be applied to iteratively optimize the adversarial example by moving the leaf tuple to its neighborhood within hamming distance 1. Experimental results on several large GBDT and RF models with up to hundreds of trees demonstrate that our method can be thousands of times faster than the previous mixed-integer linear programming (MILP) based approach, while also providing smaller (better) adversarial examples than decision-based black-box attacks on general \ell_p ( p 1, 2, \infty) norm perturbations.

While effective, deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, recent work has shown that such attacks could be generated by another deep network, leading to significant speedups over optimization-based perturbations. However, the ability of such generative methods to generalize to different test-time situations has not been systematically studied. In this paper, we, therefore, investigate the transferability of generated perturbations when the conditions at inference time differ from the training ones in terms of the target architecture, target data, and target task. Specifically, we identify the mid-level features extracted by the intermediate layers of DNNs as common ground across different architectures, datasets, and tasks. This lets us introduce a loss function based on such mid-level features to learn an effective, transferable perturbation generator.