Every day, Q.ai brings you a list of trending stocks that have caught the fancy of hedge funds, retail investors, and the occasional Robinhood-er alike. And to celebrate the start of the new month, today's batch is a rather motley assortment spanning the sector spectrum, from vaccines and bath towels to spirits and cloud computing. Without further ado, let's see what stocks are trending as we celebrate the first day of July with an independence-sized bang. Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don't have to do the digging yourself.

We introduce a collection of datasets from fundamental physics research -- including particle physics, astroparticle physics, and hadron- and nuclear physics -- for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. Based on these data, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks in these domains. We show that our approach reaches performance close to state-of-the-art dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.

A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences and humanistic studies brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the "impact remediation framework," is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a real-world case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality.

Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations. Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics. Different from the image processing field, the text is discrete and few word substitutions can cause significant semantic changes. To study the impact of semantics caused by small perturbations, we conduct a series of pilot experiments and surprisingly find that adversarial training is useless or even harmful for the model to detect these semantic changes. To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically adversarial attacking. By comparing with similar and opposite semantic examples, the model can effectively perceive the semantic changes caused by small perturbations. Empirical results show that our approach yields substantial improvements on a range of sentiment analysis, reasoning, and reading comprehension tasks. And CLINE also ensures the compactness within the same semantics and separability across different semantics in sentence-level.

Explaining Traditional Engineering Models It is a well-known fact that models of physical phenomena that are generated through mathematical equations can be explained. This is one of the main reasons behind the expectation of engineers and scientists that any potential model of the physical phenomena should be explainable. Explainability of the traditional models of physical phenomena is achieved through the solutions of the mathematical equations that are used to build the models. Explanations of such models are achieved through analytical solutions (for reasonably simple mathematical equations) or numerical solutions (for complex mathematical equations) of the mathematical equations. Solutions of the mathematical equations provide the opportunities to get answers to almost any question that might be asked from the model of the physical phenomena. Solutions of the mathematical equations are used to explain why and how certain results are generated by the model. It allows examination and explanation of the influence and effect of all the involved parameters (variables) on one another and on the model's results (output parameters).

As the car with the blacked-out windows came to a halt in a sidestreet near Tübingen's botanical gardens, keen-eyed passersby may have noticed something unusual about its numberplate. In Germany, the first few letters usually denote the municipality where a vehicle is registered. The letter Y, however, is reserved for members of the armed forces. Military men are a rare, not to say unwelcome, sight in Tübingen. A picturesque 15th-century university town that brought forth great German minds including the philosopher Hegel and the poet Friedrich Hölderlin, it is also a modern stronghold of the German Green party, thanks to its left-leaning academic population. In 2018, there was growing resistance on campus against plans to establish Europe's leading artificial intelligence research hub in the surrounding area: the involvement of arms manufacturers in Tübingen's "cyber valley", argued students who occupied a lecture hall that year, brought shame to the university's intellectual tradition. Yet the two high-ranking officials in field-grey Bundeswehr uniforms who stepped out of the Y-plated vehicle on 1 February 2018 had travelled into hostile territory to shake hands on a collaboration with academia, the like of which the world had never seen before. The name of the initiative was Project Cassandra: for the next two years, university researchers would use their expertise to help the German defence ministry predict the future. Instead, the people the colonels had sought out in a stuffy top-floor room were a small team of literary scholars led by Jürgen Wertheimer, a professor of comparative literature with wild curls and a penchant for black roll-necks.

India is lagging behind China in Artificial Intelligence (AI) by at least a decade and also, unique data assets are routinely given away to foreign countries because of the ignorance of her leaders. Given the lack of effective strategic planning on AI and big data, plus its dependence on American digital platforms and Chinese hardware, India might slip further toward digital colonisation. Why does India lag at least a decade behind China in AI and related technologies, despite India having been recently proclaimed as the world leader in software? How vulnerable is India to becoming a digital colony of the West and China? How do Indian industries, military and other sectors stack up in addressing the AI-based technological revolution? India’s security involves combating internal insurgencies as well as protecting long borders with hostile neighbours. This requires considerable manpower that consumes bulk of the military budget. Insufficient funds remain for indigenous R&D and technology related modernisation. India is dependent on imported weapons to defend herself. India might find herself facing Pakistani boots on the ground, weaponised by China’s AI-based technology. How seriously vulnerable is India’s national security considering it is lagging in AI?

In this thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary information-theoretic behaviours of systems which maintain a separation from their environment. A core postulate of the theory is that complex systems can be seen as performing variational Bayesian inference and minimizing an information-theoretic quantity called the variational free energy. The free energy principle originated in, and has been extremely influential in theoretical neuroscience, having spawned a number of neurophysiologically realistic process theories, and maintaining close links with Bayesian Brain viewpoints. The thesis is split into three main parts where we apply methods and insights from the free energy principle to understand questions first in perception, then action, and finally learning. Specifically, in the first section, we focus on the theory of predictive coding, a neurobiologically plausible process theory derived from the free energy principle under certain assumptions, which argues that the primary function of the brain is to minimize prediction errors. We focus on scaling up predictive coding architectures and simulate large-scale predictive coding networks for perception on machine learning benchmarks; we investigate predictive coding's relationship to other classical filtering algorithms, and we demonstrate that many biologically implausible aspects of current models of predictive coding can be relaxed without unduly harming the performance of predictive coding models which allows for a potentially more literal translation of predictive coding theory into cortical microcircuits. In the second part of the thesis, we focus on the application of methods deriving from the free energy principle to action. We study the extension of methods of'active inference', a neurobiologically grounded account of action through variational message passing, to utilize deep artificial neural networks, allowing these methods to'scale up' to be competitive with state of the art deep reinforcement learning methods.

Graph Networks (GNs) enable the fusion of prior knowledge and relational reasoning with flexible function approximations. In this work, a general GN-based model is proposed which takes full advantage of the relational modeling capabilities of GNs and extends these to probabilistic modeling with Variational Bayes (VB). To that end, we combine complementary pre-existing approaches on VB for graph data and propose an approach that relies on graph-structured latent and conditioning variables. It is demonstrated that Neural Processes can also be viewed through the lens of the proposed model. We show applications on the problem of structured probability density modeling for simulated and real wind farm monitoring data, as well as on the meta-learning of simulated Gaussian Process data. We release the source code, along with the simulated datasets.

Researchers at the Technion - Israel Institute of Technology have developed a new way of diagnosing tuberculosis cases, according to a statement.The novel method can diagnose the disease by means of a sticker patch that catches compounds released by the skin, using artificial intelligence to analyze them - resulting in a quick, non-invasive diagnosis.Their findings were published in the medical journal Advanced Science.The WHO's annual TB report found that tuberculosis killed some 1.4 million people in 2019, not much less than the 1.5 million deaths it caused in 2018. The report warned that many countries are not on track to meet targets for successfully diagnosing and treating cases to stop the disease's spread amid the coronavirus pandemic.Before the COVID-19 pandemic, the WHO's report said, many countries had been making steady progress against TB, with a 9% reduction in incidence seen between 2015 and 2019 and a 14% drop in deaths during the same period."Early What makes matters worse is that currently existing diagnosis methods are slow, and at times too expensive or complex for resource-limited settings," Technion explained. "For example, a sputum smear ($2.60 to $10.50 per examination) is too expensive in a location where people live on $1/day, while a mycobacterial culture test takes 4–8 weeks and at least three visits by the patient to finalize the diagnosis and begin treatment." "none";}The device, termed an A-patch, is already in its clinical trial period and is a sought-after diagnostic tool.