Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchmark that is meant to capture the amplification of social bias, via stigmas, in generative language models. Taking inspiration from social science research, we start with a documented list of 93 US-centric stigmas and curate a question-answering (QA) dataset which involves simple social situations. Our benchmark, SocialStigmaQA, contains roughly 10K prompts, with a variety of prompt styles, carefully constructed to systematically test for both social bias and model robustness. We present results for SocialStigmaQA with two open source generative language models and we find that the proportion of socially biased output ranges from 45% to 59% across a variety of decoding strategies and prompting styles. We demonstrate that the deliberate design of the templates in our benchmark (e.g., adding biasing text to the prompt or using different verbs that change the answer that indicates bias) impacts the model tendencies to generate socially biased output. Additionally, through manual evaluation, we discover problematic patterns in the generated chain-of-thought output that range from subtle bias to lack of reasoning. Warning: This paper contains examples of text which are toxic, biased, and potentially harmful.

Interaction between humans and agents/robots can greatly benefit from each other's ability to reason about the others' intentions--inferring what the other is trying to do and what its objectives are. In the human-robot interaction (HRI) literature, several works have explored the communication of intentions using speech [1, 2], gaze [3, 4], and movements [5, 6]. In this work we address the problem of conveying intention through action, which is closely related to the aforementioned works that explore communication of intention through movement. In particular, we are interested in the notion of legibility, introduced by Dragan et al. [7], that measures to what extent a user is able to infer the goal of a robot by observing a snippet of the robot's movement. A legible movement is characterized not by its efficiency in reaching the goal, but by its distinctiveness, i.e., by how much it is able to disambiguate the actual goal of the movement from other potential goals. In the original work of Dragan et al. [7], legibility is expressed by the probability of the goal given the movement, i.e., L(movement) = P (Goal | Movement snippet). Legibility has been widely explored in human-robot interaction to improve a robots' expressiveness through movement [5]. More recently, several works have extended the notion of legibility to domains other than robotic motion. The focus on improving the transparency and explainability of machine systems has been one of the main drives for the application of legibility beyond robotic motion [8].

Yemen's Houthi rebel group has said that it carried out drone attacks targeting the Israeli port city of Eilat, as well as a commercial vessel in the Red Sea, as the Iran-backed group steps up attacks that it says are a means of pressuring Israel to end its war in Gaza. Speaking on Tuesday, Houthi military spokesman Yahya Sarea said the group conducted drone attacks on Eilat and "other areas in occupied Palestine". Sarea said the group also launched missiles at an MSC United vessel in the Red Sea after it rejected three warning calls. The statement comes several hours after a British maritime group said it received reports of an incident involving a vessel off the coast of Yemen, saying that drones were sighted and an explosion was heard. The United Kingdom Maritime Trade Operations (UKMTO) on Tuesday said the incident took place about 60 nautical miles (111km) outside of Yemen's Hodeidah port.

After Mr. Biden was informed of the Erbil attack on Christmas morning, he ordered the Defense Department to prepare response options, White House officials said. Later in the day, the president authorized strikes that were conducted around 8:45 p.m. Eastern time. Mr. Biden chose Kataib Hezbollah and affiliated facilities that had been used to launch unmanned aerial drone attacks, officials said. Last month, the United States struck an operations center and a command-and-control node south of Baghdad used by Kataib Hezbollah. The group's political wing is part of the coalition of Prime Minister Mohammed Shia al-Sudani of Iraq.

The United States has launched strikes on Iran-aligned forces in Iraq after a drone attack that wounded three US service members, one of them critically, the White House has announced. US President Joe Biden ordered the strikes on three sites used by Kataeb Hezbollah and affiliated groups in Iraq, US National Security Council spokesperson Adrienne Watson said in a statement on Monday night. Watson said the strikes "focused specifically on unmanned aerial drone activities". "The President places no higher priority than the protection of American personnel serving in harm's way," she said. "The United States will act at a time and in a manner of our choosing should these attacks continue."

Earthquakes are evaluated among the most destructive disasters for human beings, as also experienced for Turkiye region. Data science has the property of discovering hidden patterns in case a sufficient volume of data is supplied. Time dependency of events, specifically being defined by co-occurrence in a specific time window, may be handled as an associate rule mining task such as a market-basket analysis application. In this regard, we assumed each day's seismic activity as a single basket of events, leading to discovering the association patterns between these events. Consequently, this study presents the most prominent association rules for the earthquakes recorded in Turkiye region in the last 5 years, each year presented separately. Results indicate statistical inference with events recorded from regions of various distances, which could be further verified with geologic evidence from the field. As a result, we believe that the current study may form a statistical basis for the future works with the aid of machine learning algorithm performed for associate rule mining.

Submodular maximization under matroid and cardinality constraints are classical problems with a wide range of applications in machine learning, auction theory, and combinatorial optimization. In this paper, we consider these problems in the dynamic setting, where (1) we have oracle access to a monotone submodular function $f: 2^{V} \rightarrow \mathbb{R}^+$ and (2) we are given a sequence $\mathcal{S}$ of insertions and deletions of elements of an underlying ground set $V$. We develop the first fully dynamic $(4+\epsilon)$-approximation algorithm for the submodular maximization problem under the matroid constraint using an expected worst-case $O(k\log(k)\log^3{(k/\epsilon)})$ query complexity where $0 < \epsilon \le 1$. This resolves an open problem of Chen and Peng (STOC'22) and Lattanzi et al. (NeurIPS'20). As a byproduct, for the submodular maximization under the cardinality constraint $k$, we propose a parameterized (by the cardinality constraint $k$) dynamic algorithm that maintains a $(2+\epsilon)$-approximate solution of the sequence $\mathcal{S}$ at any time $t$ using an expected worst-case query complexity $O(k\epsilon^{-1}\log^2(k))$. This is the first dynamic algorithm for the problem that has a query complexity independent of the size of ground set $V$.

The multilingual dictionary of noun valency Portlex is considered to be the trigger for the creation of the automatic language generators Xera and Combinatoria, whose development and use is presented in this paper. Both prototypes are used for the automatic generation of nominal phrases with their mono- and bi-argumental valence slots, which could be used, among others, as dictionary examples or as integrated components of future autonomous E-Learning-Tools. As samples for new types of automatic valency dictionaries including user interaction, we consider the language generators as we know them today. In the specific methodological procedure for the development of the language generators, the syntactic-semantic description of the noun slots turns out to be the main focus from a syntagmatic and paradigmatic point of view. Along with factors such as representativeness, grammatical correctness, semantic coherence, frequency and the variety of lexical candidates, as well as semantic classes and argument structures, which are fixed components of both resources, a concept of a multi-sided prototype stands out. The combined application of this prototype concept as well as of word embeddings together with techniques from the field of automatic natural language processing and generation (NLP and NLG) opens up a new way for the future development of automatically generated plurilingual valency dictionaries. All things considered, the paper depicts the language generators both from the point of view of their development as well as from that of the users. The focus lies on the role of the prototype concept within the development of the resources.

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons for option errors to choose the optimal path of the GoT and ultimately infer the correct answer. By progressively integrating intricate clues, our method effectively reduces the difficulty of multi-choice reasoning and provides a novel solution for DC-MCQ. Extensive experiments on the CICERO and CICERO$_{v2}$ datasets validate the significant improvement of our approach on DC-MCQ task. On zero-shot setting, our model outperform the best baseline by 17.67% in terms of F1 score for the multi-choice task. Most strikingly, our GPT3.5-based ReX-GoT framework achieves a remarkable 39.44% increase in F1 score.

Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.