When the war came to Sergiy Sotnychenko's neighborhood in March 2022, he found himself carrying out daily performances for the drones that hummed constantly overhead. Desperate to prove that he wasn't a combatant, he put on an orange hoodie which, out of all the clothing he owned, seemed least likely to be mistaken for military fatigues. He tried to show the drones he was carrying out innocent activities, like planting onions. That March was a nightmarishly violent month for Kyiv's outskirts, including Irpin, where Sotnychenko lives, but there were moments when he allowed himself to feel comforted by the drones flying above. He imagined the Ukrainian army watching his small acts of resistance.

Uncrewed combat aerial vehicles, or attack drones, have become a common feature of the modern battlefield. Russia has deployed them to terrorise civilians in Ukraine and disable essential infrastructure, and Ukraine has also relied heavily on drones for attack, reconnaissance and surveillance. But these aren't the only'killer robots' that armies are utilising. Part of the kill chain': how can we control weaponised robots?

Given that China already bans Google, Facebook, Twitter, and a host of foreign news websites (including time.com) In fact, ChatGPT parent company OpenAI's decision not to launch in China--Chinese and even Hong Kong phone numbers aren't permitted to sign up--appears to preempt that very fact, with the San Francisco-based firm telling Reuters that "conditions in certain countries make it difficult or impossible" to operate. Read More: Why China, Russia's Biggest Backer, Now Says It Wants to Broker Peace in Ukraine Nevertheless, canny Chinese netizens have found numerous workarounds to access the revolutionary service, such as using virtual private networks and an overseas friend's phone number; purchasing logins via online marketplace Taobao; or simply taking advantage of a variety of proxy bots embedded in ubiquitous messaging service WeChat. Chinese social media was so abuzz with ChatGPT content this month that one AI-generated fake government notice rescinding traffic regulations sparked bedlam and a police investigation in the eastern city of Hangzhou. Unsurprisingly, China's government has now stepped in with explicit bans on WeChat hosting proxy ChatGPT services, while a strident frontpage op-ed on the perils of investing in AI-related firms (and cited ChatGPT), which published earlier this month in the state-owed Securities Times newspaper, was linked to a fall in Chinese tech stocks.

It has been less than 18 months since we published our last MAD landscape, and it has been full of drama. When we left, the data world was booming in the wake of the gigantic Snowflake IPO, with a whole ecosystem of startups organizing around it. Since then, of course, public markets crashed, a recessionary economy appeared and VC funding dried up. A whole generation of data/AI startups has had to adapt to a new reality. Meanwhile, the last few months saw the unmistakable, exponential acceleration of Generative AI, with arguably the formation of a new mini-bubble.

Rep. Mike Gallagher, R-Wis., says Xi Jinping is turning Vladimir Putin into his'junior partner.' Russia is in talks with China to purchase 100 combat drones, according to a new report published just days after Secretary of State Antony Blinken said the U.S. had evidence Beijing was weighing lethal aid to Moscow in its war against Ukraine. Der Spiegel reports that Moscow is looking to commission a Chinese manufacturer to mass produce the drones – with a delivery date as early as April. Russian President Vladimir Putin greets Chinese Communist Party's foreign policy chief Wang Yi during their meeting at the Kremlin in Moscow, Russia, Wednesday, Feb. 22, 2023. Per the report, Xian Bingo Intelligent Aviation Technology, a Chinese drone manufacturer, has said it was prepared to make 100 prototypes of its ZT-180 drone, which carry a 35-50kg warhead. The drones are similar to Iran's Shaheed-136, which Russia has used to kill hundreds of Ukrainians and damage infrastructure.

In this paper, we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive responses. To identify the subset of microstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. The results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, the resulting algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. The algorithm is tested on the open-source mechanical MNIST data set. Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media but also learns the nonlinear structure-property map to quantitatively understand the multiscale interplay among the geometry and topology and their effective macroscopic properties.

The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) average, and can overlook risks and tail events, which are important to treatment choice. In aggregate analyses, this is usually addressed by measuring the distributional treatment effect (DTE), such as differences in quantiles or tail expectations between treatment groups. Hypothetically, one can similarly fit conditional quantile regressions in each treatment group and take their difference, but this would not be robust to misspecification or provide agnostic best-in-class predictions. We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a class of problems that includes conditional quantile treatment effects, conditional super-quantile treatment effects, and conditional treatment effects on coherent risk measures given by $f$-divergences. Our method is based on constructing a special pseudo-outcome and regressing it on covariates using any regression learner. Our method is model-agnostic in that it can provide the best projection of CDTE onto the regression model class. Our method is robust in that even if we learn these nuisances nonparametrically at very slow rates, we can still learn CDTEs at rates that depend on the class complexity and even conduct inferences on linear projections of CDTEs. We investigate the behavior of our proposal in simulations, as well as in a case study of 401(k) eligibility effects on wealth.

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the C\'esar Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events.

Our work focuses on anomaly detection in cyber-physical systems. Prior literature has three limitations: (1) Failing to capture long-delayed patterns in system anomalies; (2) Ignoring dynamic changes in sensor connections; (3) The curse of high-dimensional data samples. These limit the detection performance and usefulness of existing works. To address them, we propose a new approach called deep graph stream support vector data description (SVDD) for anomaly detection. Specifically, we first use a transformer to preserve both short and long temporal patterns of monitoring data in temporal embeddings. Then we cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph. The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph. We input the graph into a variational graph auto-encoder model to learn final spatio-temporal representation. Finally, we learn a hypersphere that encompasses normal embeddings and predict the system status by calculating the distances between the hypersphere and data samples. Extensive experiments validate the superiority of our model, which improves F1-score by 35.87%, AUC by 19.32%, while being 32 times faster than the best baseline at training and inference.

In representative democracies, the election of new representatives in regular election cycles is meant to prevent corruption and other misbehavior by elected officials and to keep them accountable in service of the ``will of the people." This democratic ideal can be undermined when candidates are dishonest when campaigning for election over these multiple cycles or rounds of voting. Much of the work on COMSOC to date has investigated strategic actions in only a single round. We introduce a novel formal model of \emph{pandering}, or strategic preference reporting by candidates seeking to be elected, and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds. The two voting systems we compare are Representative Democracy (RD) and Flexible Representative Democracy (FRD). For each voting system, our analysis centers on the types of strategies candidates employ and how voters update their views of candidates based on how the candidates have pandered in the past. We provide theoretical results on the complexity of pandering in our setting for a single cycle, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by both single candidates and groups of candidates across a number of rounds.