There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis can be diagnosed by satisfying both suspicion of infection and Sequential Organ Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge, SofaNet adopts a multi-channel GRU structure to predict SOFA values of different systems, which can be seen as an auxiliary task to generate better health status representations for sepsis recognition. Moreover, we only achieve feature distribution alignment in the hidden space during cross-center collaborative learning, which ensures secure and compliant knowledge transfer without raw data exchange. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis recognition when hospitals only have limited EMRs.

The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.

Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods. In this work, we focus on evaluating gender bias through coreference resolution, where previous datasets are either hand-crafted or fail to reliably measure an explicitly defined bias. To overcome these shortcomings, we propose a novel method to collect diverse, natural, and minimally distant text pairs via counterfactual generation, and construct Counter-GAP, an annotated dataset consisting of 4008 instances grouped into 1002 quadruples. We further identify a bias cancellation problem in previous group-level metrics on Counter-GAP, and propose to use the difference between inconsistency across genders and within genders to measure bias at a quadruple level. Our results show that four pre-trained language models are significantly more inconsistent across different gender groups than within each group, and that a name-based counterfactual data augmentation method is more effective to mitigate such bias than an anonymization-based method.

The vast majority of evaluation metrics for machine translation are supervised, i.e., (i) are trained on human scores, (ii) assume the existence of reference translations, or (iii) leverage parallel data. This hinders their applicability to cases where such supervision signals are not available. In this work, we develop fully unsupervised evaluation metrics. To do so, we leverage similarities and synergies between evaluation metric induction, parallel corpus mining, and MT systems. In particular, we use an unsupervised evaluation metric to mine pseudo-parallel data, which we use to remap deficient underlying vector spaces (in an iterative manner) and to induce an unsupervised MT system, which then provides pseudo-references as an additional component in the metric. Finally, we also induce unsupervised multilingual sentence embeddings from pseudo-parallel data. We show that our fully unsupervised metrics are effective, i.e., they beat supervised competitors on 4 out of our 5 evaluation datasets. We make our code publicly available.

Iran's supply of drones to Russia for its war effort in Ukraine appears to have escalated in recent months after a study released this week found Tehran has modified its drones to employ maximum damage. A January report by Conflict Armament Research (CAR) released publicly Thursday broke down why the affordably made Iranian Shahed-131 single-use drones have been employed to significant effect in Ukraine. Russia has relied on Iranian-supplied unmanned areial vehicles (UAV) in Ukraine for months to help assist with its diminishing missile stockpiles as the war continues into its 11th month, according to the Pentagon. Multipurpose warhead from a Shahed-131 UAV found in research by Conflict Armament Research on Thursday. Iranian drones have been used to hit civilian structures and target Ukraine's critical infrastructure as Russia looks to bombard the war-torn nation in near-daily strikes.

Netflix has plenty of movies to watch, but it's a real mixed bag. Sometimes finding the right film at the right time can seem like an impossible task. Fret not, we're here to help. Below is a list of some of our favorite films currently on the streaming service--from dramas to comedies to thrillers. If you decide you're in more of a TV mood, head over to our collection of the best TV series on Netflix. Check out our lists of the best sci-fi movies, best movies on Amazon Prime, and the best flicks on Disney . It's easy to imagine that the elevator pitch for The Sea Beast was "Moby Dick meets How to Train Your Dragon"--and who wouldn't be compelled by that? Set in a fantasy world where oceanic leviathans terrorize humanity, those who hunt down the giant monsters are lauded as heroes.

Propelled by the rapid development of innovative technologies and agribusiness-tech partnerships, modern farming is on the verge of the kind of digital transformation process seen across many other industries. In fact, research predicts that by 2026, the AI in agriculture market will grow at over 25% per year to reach a value of $4 billion. According to the same study, this impressive acceleration in the adoption of AI is due to the "increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep learning technology, and government support for the adoption of modern agricultural techniques." But where is this tech-led innovation being focused? Smart farming, for example, is an autonomous end-to-end system that can gather and process key datasets to give actionable insights.

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The purpose of this role is to construct data acquisition, warehousing and reporting solutions. The role is expected to provide technical solutions in response to the needs of stakeholders by interpreting business requirements; defining solution; defining build and test tasks; constructing solutions; performing testing; participating in the deployment of solutions and ensure the systems meet immediate business requirements by providing third tier support and constructing enhancements to systems in Production.

We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities. Our algorithm mitigates the gap between adversarial loss for continuous and discrete text representations by performing multi-step quantization in a quantization-compensation loop. Experiments show that our method significantly outperforms other approaches on various natural language processing (NLP) tasks.