Bangladesh's low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs. Existing AI legal assistants lack Bengali-language support and jurisdiction-specific adaptation, limiting their effectiveness. To address this, we developed Mina, a multilingual LLM-based legal assistant tailored for the Bangladeshi context. It employs multilingual embeddings and a RAG-based chain-of-tools framework for retrieval, reasoning, translation, and document generation, delivering context-aware legal drafts, citations, and plain-language explanations via an interactive chat interface. Evaluated by law faculty from leading Bangladeshi universities across all stages of the 2022 and 2023 Bangladesh Bar Council Exams, Mina scored 75-80% in Preliminary MCQs, Written, and simulated Viva Voce exams, matching or surpassing average human performance and demonstrating clarity, contextual understanding, and sound legal reasoning. Even under a conservative upper bound, Mina operates at just 0.12-0.61% of typical legal consultation costs in Bangladesh, yielding a 99.4-99.9\% cost reduction relative to human-provided services. These results confirm its potential as a low-cost, multilingual AI assistant that automates key legal tasks and scales access to justice, offering a real-world case study on building domain-specific, low-resource systems and addressing challenges of multilingual adaptation, efficiency, and sustainable public-service AI deployment.

Whataboutism, a potent tool for disrupting narratives and sowing distrust, remains under-explored in quantitative NLP research. Moreover, past work has not distinguished its use as a strategy for misinformation and propaganda from its use as a tool for pragmatic and semantic framing. We introduce new datasets from Twitter and YouTube, revealing overlaps as well as distinctions between whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on recent work in linguistic semantics, we differentiate the `what about' lexical construct from whataboutism. Our experiments bring to light unique challenges in its accurate detection, prompting the introduction of a novel method using attention weights for negative sample mining. We report significant improvements of 4% and 10% over previous state-of-the-art methods in our Twitter and YouTube collections, respectively.

New coronavirus tests are being developed every day. The Trump administration just ordered 150 million rapid antigen tests from Abbott Laboratories, but how do they stack up against other tests like the Polymerase chain reaction (PCR) test? Top infectious disease doctors from Harvard and Johns Hopkins break down the differences between the two tests to determine which diagnostic tool might be better at curbing transmission rates. Rapid antigen tests could play a pivotal role in curbing the spread of the coronavirus, according to some of the country's top medical professionals. Antigen tests are the type of tests the White House just ordered from Abbott Laboratories in a $750 million deal that will reportedly buy 150 million of its new rapid coronavirus tests: the BinaxNOW COVID-19 Ag Card.

Bradesco expects artificial intelligence will drive a significant increase in sales via digital channels.Bradesco Brazil's second-largest private bank Bradesco will ramp up its efforts around artificial intelligence (AI) to boost sales, improve customer experience and reduce operating costs in 2019. The bank, which has a portfolio of over 71 million customers, has been working on a platform dubbed Bradesco Artificial Intelligence (BIA) over the last four years. BIA's capabilities translate into an improved customer experience across the bank's digital channels - especially the app, which today accounts for 60% of customer interactions with the bank. Currently, 90% of the bank's services are already available via the app, but sales made via mobile currently represents about 20-30% of the overall business volume. We want to increase sales in that channel," says Mauricio Minas, executive vice president at the bank, adding that the goal is to increase mobile sales to 50% this year. "A few years ago we invested in the idea that BIA would be the engine of a substantial increase in Bradesco's customer value perception.

Bradesco expects artificial intelligence will drive a significant increase in sales via digital channels.Bradesco Brazil's second-largest private bank Bradesco will ramp up its efforts around artificial intelligence (AI) to boost sales, improve customer experience and reduce operating costs in 2019. The bank, which has a portfolio of over 71 million customers, has been working on a platform dubbed Bradesco Artificial Intelligence (BIA) over the last four years. BIA's capabilities translate into an improved customer experience across the bank's digital channels – especially the app, which today accounts for 60% of customer interactions with the bank. Currently, 90% of the bank's services are already available via the app, but sales made via mobile currently represents about 20-30% of the overall business volume. We want to increase sales in that channel," says Mauricio Minas, executive vice president at the bank, adding that the goal is to increase mobile sales to 50% this year. "A few years ago we invested in the idea that BIA would be the engine of a substantial increase in Bradesco's customer value perception.

After the ACLU discovered that Orlando's cops are using Amazon's controversial Rekognition facial detection system, police chief John Mina said they're only testing the software at their headquarters. Now, Mina has admitted at a news conference that three of the city's IRIS cameras downtown are also equipped with the software. He insisted that despite Rekognition's presence in public cameras, it can still only track the seven officers who volunteered to test the system. Mina admitted that they could use the software to track persons of interest in the future, but they're "a long way from that." "We test new equipment all the time. We test new guns, new vests, new shields, new things for police cars all the time. That doesn't mean that we're going to go with that particular product. We just want to see if it works."

Plausibility models are Kripke models that agents use to reason about knowledge and belief, both of themselves and of each other. Such models are used to interpret the notions of conditional belief, degrees of belief, and safe belief. The logic of conditional belief contains that modality and also the knowledge modality, and similarly for the logic of degrees of belief and the logic of safe belief. With respect to these logics, plausibility models may contain too much information. A proper notion of bisimulation is required that characterises them. We define that notion of bisimulation and prove the required characterisations: on the class of image-finite and preimage-finite models (with respect to the plausibility relation), two pointed Kripke models are modally equivalent in either of the three logics, if and only if they are bisimilar. As a result, the information content of such a model can be similarly expressed in the logic of conditional belief, or the logic of degrees of belief, or that of safe belief. This, we found a surprising result. Still, that does not mean that the logics are equally expressive: the logics of conditional and degrees of belief are incomparable, the logics of degrees of belief and safe belief are incomparable, while the logic of safe belief is more expressive than the logic of conditional belief. In view of the result on bisimulation characterisation, this is an equally surprising result. We hope our insights may contribute to the growing community of formal epistemology and on the relation between qualitative and quantitative modelling.

The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL P, which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog. Note: An extended abstract / full version of a paper accepted to be presented at the Doctoral Consortium of the 30th International Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria

We investigate the computational complexity of axiom pinpointing, which is the task of finding minimal subsets of a Description Logic knowledge base that have a given consequence. We consider the problems of enumerating such subsets with and without order, and show hardness results that already hold for the propositional Horn fragment, or for the Description Logic EL. We show complexity results for several other related decision and enumeration problems for these fragments that extend to more expressive logics. In particular we show that hardness of these problems depends not only on expressivity of the fragment but also on the shape of the axioms used.

We study the asymptotic properties of the sequence of iterates of weight-vector estimates obtained by training a multilayer feedforward neuralnetwork with a basic gradient-descent method using a fixed learning constant and no batch-processing. In the onedimensional case,an exact analysis establishes the existence of a limiting distribution that is not Gaussian in general. For the general caseand small learning constant, a linearization approximation permits the application of results from the theory of random matrices toagain establish the existence of a limiting distribution. We study the first few moments of this distribution to compare and contrast the results of our analysis with those of techniques of stochastic approximation. 1 INTRODUCTION The wide applicability of neural networks to problems in pattern classification and signal processing has been due to the development of efficient gradient-descent algorithms forthe supervised training of multilayer feedforward neural networks with differentiable node functions. A basic version uses a fixed learning constant and updates allweights after each training input is presented (online mode) rather than after the entire training set has been presented (batch mode). The properties of this algorithm as exhibited by the sequence of iterates are not yet well-understood. There are at present two major approaches.