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We prove impossibility results for adaptivity in non-smooth stochastic convex optimization. Given a set of problem parameters we wish to adapt to, we define a "price of adaptivity" (PoA) that, roughly speaking, measures the multiplicative increase in suboptimality due to uncertainty in these parameters. When the initial distance to the optimum is unknown but a gradient norm bound is known, we show that the PoA is at least logarithmic for expected suboptimality, and double-logarithmic for median suboptimality. When there is uncertainty in both distance and gradient norm, we show that the PoA must be polynomial in the level of uncertainty. Our lower bounds nearly match existing upper bounds, and establish that there is no parameter-free lunch.

We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language -- a question of key importance for understanding how language models function and the origins of linguistic bias. Focusing on the Llama-2 family of transformer models, our study uses carefully constructed non-English prompts with a unique correct single-token continuation. From layer to layer, transformers gradually map an input embedding of the final prompt token to an output embedding from which next-token probabilities are computed. Tracking intermediate embeddings through their high-dimensional space reveals three distinct phases, whereby intermediate embeddings (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give higher probability to its version in English than in the input language; (3) finally move into an input-language-specific region of the embedding space. We cast these results into a conceptual model where the three phases operate in "input space", "concept space", and "output space", respectively. Crucially, our evidence suggests that the abstract "concept space" lies closer to English than to other languages, which may have important consequences regarding the biases held by multilingual language models.

Data physicalizations have gained prominence across domains, but their environmental impact has been largely overlooked. This work addresses this gap by investigating the interplay between sustainability and physicalization practices. We conducted interviews with experts from diverse backgrounds, followed by a survey to gather insights into how they approach physicalization projects and reflect on sustainability. Our thematic analysis revealed sustainability considerations throughout the entire physicalization life cycle -- a framework that encompasses various stages in a physicalization's existence. Notably, we found no single agreed-upon definition for sustainable physicalizations, highlighting the complexity of integrating sustainability into physicalization practices. We outline sustainability challenges and strategies based on participants' experiences and propose the Sustainable Physicalization Practices (SuPPra) Matrix, providing a structured approach for designers to reflect on and enhance the environmental impact of their future physicalizations.

SMS phishing, also known as "smishing", is a growing threat that tricks users into disclosing private information or clicking into URLs with malicious content through fraudulent mobile text messages. In recent past, we have also observed a rapid advancement of conversational generative AI chatbot services (e.g., OpenAI's ChatGPT, Google's BARD), which are powered by pre-trained large language models (LLMs). These AI chatbots certainly have a lot of utilities but it is not systematically understood how they can play a role in creating threats and attacks. In this paper, we propose AbuseGPT method to show how the existing generative AI-based chatbot services can be exploited by attackers in real world to create smishing texts and eventually lead to craftier smishing campaigns. To the best of our knowledge, there is no pre-existing work that evidently shows the impacts of these generative text-based models on creating SMS phishing. Thus, we believe this study is the first of its kind to shed light on this emerging cybersecurity threat. We have found strong empirical evidences to show that attackers can exploit ethical standards in the existing generative AI-based chatbot services by crafting prompt injection attacks to create newer smishing campaigns. We also discuss some future research directions and guidelines to protect the abuse of generative AI-based services and safeguard users from smishing attacks.

Recently, emergence has received widespread attention from the research community along with the success of large language models. Different from the literature, we hypothesize a key factor that highly promotes the performance during the increase of scale: the reduction of monosemantic neurons that can only form one-to-one correlations with specific features. Monosemantic neurons tend to be sparser and have negative impacts on the performance in large models. Inspired by this insight, we propose an intuitive idea to identify monosemantic neurons and inhibit them. However, achieving this goal is a non-trivial task as there is no unified quantitative evaluation metric and simply banning monosemantic neurons does not promote polysemanticity in neural networks. Therefore, we propose to learn from emergence and present a study on proactively inhibiting the monosemantic neurons in this paper. More specifically, we first propose a new metric to measure the monosemanticity of neurons with the guarantee of efficiency for online computation, then introduce a theoretically supported method to suppress monosemantic neurons and proactively promote the ratios of polysemantic neurons in training neural networks. We validate our conjecture that monosemanticity brings about performance change at different model scales on a variety of neural networks and benchmark datasets in different areas, including language, image, and physics simulation tasks. Further experiments validate our analysis and theory regarding the inhibition of monosemanticity.

The "Smart City" (SC) concept has been around for decades with deployment scenarios revealed in major cities of developed countries. However, while SC has enhanced the living conditions of city dwellers in the developed world, the concept is still either missing or poorly deployed in the developing world. This paper presents a review of the SC concept from the perspective of its application to cities in developing nations, the opportunities it avails, and challenges related to its applicability to these cities. Building upon a systematic review of literature, this paper shows that there are neither canonical definitions, models or frameworks of references for the SC concept. This paper also aims to bridge the gap between the "smart city" and "smart village" concepts, with the expectation of providing a holistic approach to solving common issues in cities around the world. Drawing inspiration from other authors, we propose a conceptual model for a SC initiative in Africa and demonstrate the need to prioritize research and capacity development. We also discuss the potential opportunities for such SC implementations in sub-Saharan Africa. As a case study, we consider the city of Lubumbashi in the Democratic Republic of Congo and discuss ways of making it a smart city by building around successful smart city initiatives. It is our belief that for Lubumbashi, as with any other city in Sub-Saharan Africa, the first step to developing a smart city is to build knowledge and create an intellectual capital.

6G promises a paradigm shift in which positioning and sensing are inherently integrated, enhancing not only the communication performance but also enabling location- and context-aware services. Historically, positioning and sensing have been viewed through the lens of cost and performance trade-offs, implying an escalated demand for resources, such as radio, physical, and computational resources, for improved performance. However, 6G goes beyond this traditional perspective to encompass a set of broader values, namely sustainability, inclusiveness, and trustworthiness. From a joint industrial/academic perspective, this paper aims to shed light on these important value indicators and their relationship with the conventional key performance indicators in the context of positioning and sensing.

Chain-of-thought reasoning, a cognitive process fundamental to human intelligence, has garnered significant attention in the realm of artificial intelligence and natural language processing. However, there still remains a lack of a comprehensive survey for this arena. To this end, we take the first step and present a thorough survey of this research field carefully and widely. We use X-of-Thought to refer to Chain-of-Thought in a broad sense. In detail, we systematically organize the current research according to the taxonomies of methods, including XoT construction, XoT structure variants, and enhanced XoT. Additionally, we describe XoT with frontier applications, covering planning, tool use, and distillation. Furthermore, we address challenges and discuss some future directions, including faithfulness, multi-modal, and theory. We hope this survey serves as a valuable resource for researchers seeking to innovate within the domain of chain-of-thought reasoning.

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

Recently, Mutual Information (MI) has attracted attention in bounding the generalization error of Deep Neural Networks (DNNs). However, it is intractable to accurately estimate the MI in DNNs, thus most previous works have to relax the MI bound, which in turn weakens the information theoretic explanation for generalization. To address the limitation, this paper introduces a probabilistic representation of DNNs for accurately estimating the MI. Leveraging the proposed MI estimator, we validate the information theoretic explanation for generalization, and derive a tighter generalization bound than the state-of-the-art relaxations.