This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by -11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors

We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP. Our approach is based on prompting off-the-shelf instruction-following Large Language Models (LLMs) for generating text that satisfies a set of constraints. Precisely, we extract a set of simple constraints from every instance in the low-resource dataset and verbalize them to prompt an LLM to generate novel and diverse training instances. Our findings reveal that synthetic data that follows simple constraints in the downstream dataset act as highly effective augmentations, and CoDa can achieve this without intricate decoding-time constrained generation techniques or fine-tuning with complex algorithms that eventually make the model biased toward the small number of training instances. Additionally, CoDa is the first framework that provides users explicit control over the augmentation generation process, thereby also allowing easy adaptation to several domains. We demonstrate the effectiveness of CoDa across 11 datasets spanning 3 tasks and 3 low-resource settings. CoDa outperforms all our baselines, qualitatively and quantitatively, with improvements of 0.12%-7.19%. Code is available here: https://github.com/Sreyan88/CoDa

Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a new risk of privacy backdoors. By tampering with a pretrained model's weights, an attacker can fully compromise the privacy of the finetuning data. We show how to build privacy backdoors for a variety of models, including transformers, which enable an attacker to reconstruct individual finetuning samples, with a guaranteed success! We further show that backdoored models allow for tight privacy attacks on models trained with differential privacy (DP). The common optimistic practice of training DP models with loose privacy guarantees is thus insecure if the model is not trusted. Overall, our work highlights a crucial and overlooked supply chain attack on machine learning privacy.

Automatic legal text classification systems have been proposed in the literature to address knowledge extraction from judgments and detect their aspects. However, most of these systems are black boxes even when their models are interpretable. This may raise concerns about their trustworthiness. Accordingly, this work contributes with a system combining Natural Language Processing (NLP) with Machine Learning (ML) to classify legal texts in an explainable manner. We analyze the features involved in the decision and the threshold bifurcation values of the decision paths of tree structures and present this information to the users in natural language. This is the first work on automatic analysis of legal texts combining NLP and ML along with Explainable Artificial Intelligence techniques to automatically make the models' decisions understandable to end users. Furthermore, legal experts have validated our solution, and this knowledge has also been incorporated into the explanation process as "expert-in-the-loop" dictionaries. Experimental results on an annotated data set in law categories by jurisdiction demonstrate that our system yields competitive classification performance, with accuracy values well above 90%, and that its automatic explanations are easily understandable even to non-expert users.

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other unsupervised learning methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.

Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models. However, existing models for MQA under KE exhibit poor performance when dealing with questions containing explicit temporal contexts. To address this limitation, we propose a novel framework, namely TEMPoral knowLEdge augmented Multi-hop Question Answering (TEMPLE-MQA). Unlike previous methods, TEMPLE-MQA first constructs a time-aware graph (TAG) to store edit knowledge in a structured manner. Then, through our proposed inference path, structural retrieval, and joint reasoning stages, TEMPLE-MQA effectively discerns temporal contexts within the question query. Experiments on benchmark datasets demonstrate that TEMPLE-MQA significantly outperforms baseline models. Additionally, we contribute a new dataset, namely TKEMQA, which serves as the inaugural benchmark tailored specifically for MQA with temporal scopes.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. ANCHORAGE, Alaska (AP) -- A headless robot about the size of a labrador retriever will be camouflaged as a coyote or fox to ward off migratory birds and other wildlife at Alaska's second largest airport, a state agency said. The Alaska Department of Transportation and Public Facilities has named the new robot Aurora and said it will be based at the Fairbanks airport to "enhance and augment safety and operations," the Anchorage Daily News reported. The transportation department released a video of the robot climbing rocks, going up stairs and doing something akin to dancing while flashing green lights.

Luke Bryan speaks during the signing of the ELVIS Act to Protect Voice & amp; Likeness in Age of AI event at Robert's Western World on March 21, 2024, in Nashville, Tennessee. 'AMAZING PRECEDENT': Luke Bryan is celebrating new protections from artificial intelligence for musicians in Nashville. Luke Bryan has high praise for the Tennessee state government over its new AI regulation law. ELECTION THREAT: Former Secretary of State Hillary Clinton described herself as a victim of election disinformation during a panel discussion on Thursday, and warned that the advancement of artificial intelligence (AI) will make her experience "look primitive." LEVEL UP: Google has developed an artificial intelligence system that can play video games like a human and take orders from players and could eventually even have real-world implications down the line.

Ever since the rollout of ChatGPT in November 2022, many people in science, business, and media have been obsessed with AI. A cursory look at my own published work during that period fingers me as among the guilty. My defense is that I share with those other obsessives a belief that large language models are the leading edge of an epochal transformation. Maybe I'm swimming in generative Kool-Aid, but I believe AI advances within our grasp will change not only the way we work, but the structure of businesses, and ultimately the course of humanity. Not everyone agrees, and in recent months there's been a backlash. AI has been oversold and overhyped, some experts now opine.

The UK government's decision to let technology companies self-regulate gambling-style loot boxes in video games has been called into question, after some of the developers put in charge of new industry guidelines broke their own rules. In the past six months, the advertising regulator has upheld complaints against three companies involved in drawing up industry rules, including the leading developer Electronic Arts (EA), for failing to disclose that their games contained loot boxes. An expert who submitted the complaints said he had found hundreds more examples of breaches but had only taken a handful to the Advertising Standards Authority (ASA) in order to highlight the problem. Loot boxes are in-game features that allow players to pay, with real money or virtual currency, to open a digital envelope containing random prizes, such as an outfit or a weapon for a character. Despite warnings from experts that loot boxes carry similar risks to gambling, the then Department for Digital, Culture, Media and Sport said in July 2022 it would not follow other countries, such as Belgium, in choosing to regulate them as gambling products.