Miracle-dz/UniFAR-FTUs huggingface dataset:
A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19

The outbreak of the novel coronavirus (COVID-19) is unfolding as a major international crisis whose influence extends to every aspect of our daily lives. Effective testing allows infected individuals to be quarantined, thus reducing the spread of COVID-19, saving countless lives, and helping to restart the economy safely and securely. Developing a good testing strategy can be greatly aided by contact tracing that provides health care providers information about the whereabouts of infected patients in order to determine whom to test. Countries that have been more successful in corralling the virus typically use a ``test, treat, trace, test'' strategy that begins with testing individuals with symptoms, traces contacts of positively tested individuals via a combinations of patient memory, apps, WiFi, GPS, etc., followed by testing their contacts, and repeating this procedure. The problem is that such strategies are myopic and do not efficiently use the testing resources. This is especially the case with COVID-19, where symptoms may show up several days after the infection (or not at all, there is evidence to suggest that many COVID-19 carriers are asymptotic, but may spread the virus). Such greedy strategies, miss out population areas where the virus may be dormant and flare up in the future. In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing tools. We then develop efficient learning strategies that minimize the number of infected individuals. These strategies are based on policy iteration and look-ahead rules. We investigate fundamental performance bounds, and ensure that our solution is robust to errors in the input graph as well as in the tests themselves.
Single active particle engine utilizing a nonreciprocal coupling between particle position and self-propulsion.

We recently argued that a self-propelled particle is formally equivalent to a system consisting of two subsystems coupled by a nonreciprocal interaction [Phys. Rev. E 100, 050603(R) (2019)2470-004510.1103/PhysRevE.100. 050603]. Here, we show that this nonreciprocal coupling allows us to extract useful work from a single self-propelled particle maintained at constant temperature, by using an aligning interaction to control correlations between the particle's position and self-propulsion.
Fast active learning for pure exploration in reinforcement learning

Realistic environments often provide agents with very limited feedback. When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the agents may first choose to devote all their effort on exploring efficiently. The exploration remains a challenge while it has been addressed with many hand-tuned heuristics with different levels of generality on one side, and a few theoretically backed exploration strategies on the other. Many of them are incarnated by intrinsic motivation and in particular explorations bonuses. A common rule of thumb for exploration bonuses is to use 1/ √ n bonus that is added to the empirical estimates of the reward, where n is a number of times this particular state (or a state-action pair) was visited. We show that, surprisingly, for a pure-exploration objective of reward-free exploration, bonuses that scale with 1/n bring faster learning rates, improving the known upper bounds with respect to the dependence on the horizon H. Furthermore, we show that with an improved analysis of the stopping time, we can improve by a factor H the sample complexity in the best-policy identification setting, which is another pure-exploration objective, where the environment provides rewards but the agent is not penalized for its behavior during the exploration phase.
FedML: A Research Library and Benchmark for Federated Machine Learning

Federated learning is a rapidly growing research field in the machine learning domain. Although considerable research efforts have been made, existing libraries cannot adequately support diverse algorithmic development (e.g., diverse topology and flexible message exchange), and inconsistent dataset and model usage in experiments make fair comparisons difficult. In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons. FedML supports three computing paradigms (distributed training, mobile on-device training, and standalone simulation) for users to conduct experiments in different system environments. FedML also promotes diverse algorithmic research with flexible and generic API design and reference baseline implementations. A curated and comprehensive benchmark dataset for the non-I.I. D setting aims at making a fair comparison. We believe FedML can provide an efficient and reproducible means of developing and evaluating algorithms for the federated learning research community. We maintain the source code, documents, and user community at this https URL.
Llums que no s\'on nom\'es llums

Visible Light Communications (VLC) is a new paradigm in wireless communications. The characteristics of this technology, which uses light-emitting diode-based lighting devices as transmitting elements, make it possible to be considered a complement to current wireless radio communication systems. ----- Les comunicacions per llum visible o 'Visible Light Communications' (VLC) son un nou paradigma en comunicacions sense fils. Les caracteristiques que presenta aquesta tecnologia, que utilitza els dispositius d'il{\lgem{}}luminacio basats en diodes emissors de llum com elements transmissors, fa que es pugui considerar un complement dels actuals sistemes de comunicacio inal`ambrics.
Intense-laser-driven electron dynamics and high-order harmonic generation in solids including topological effects

A theory for laser-driven electron dynamics and high-harmonic generation in bulk solids with two lattice sites per unit cell of arbitrary dimension is formulated. In tight-binding approximation, such solids can be described by $2\times 2$ Bloch-Hamiltonians of the form $\mathbf{H}(\mathbf{k}) = \mathbf{d}(\mathbf{k}) \cdot \boldsymbol{\sigma}$ (where $\boldsymbol{\sigma}$ is the vector of Pauli matrices). Our theory is able to fully capture topological effects in high-harmonic generation by such systems because no simplifications beyond tight-binding, dipole approximation, and negligible depletion of the valence band are made. An explicit, analytical expressions for the electron velocity in terms of $\mathbf{d}$ and the laser field is given. Exemplarily, the theory is applied to the Su-Schrieffer-Heeger chain and the Haldane model in strong laser fields.
The Ecology of Deep-Sea Hydrothermal Vents

Teeming with weird and wonderful life--giant clams and mussels, tubeworms, "eyeless" shrimp, and bacteria that survive on sulfur--deep-sea hot-water springs are found along rifts where sea-floor spreading occurs. The theory of plate tectonics predicted the existence of these hydrothermal vents, but they were discovered only in 1977. Since then the sites have attracted teams of scientists seeking to understand how life can thrive in what would seem to be intolerable or extreme conditions of temperature and fluid chemistry. Some suspect that these vents even hold the key to understanding the very origins of life. Here a leading expert provides the first authoritative and comprehensive account of this research in a book intended for students, professionals, and general readers. Cindy Lee Van Dover, an ecologist, brings nearly two decades of experience and a lively writing style to the text, which is further enhanced by two hundred illustrations, including photographs of vent communities taken in situ. The book begins by explaining what is known about hydrothermal systems in terms of their deep-sea environment and their geological and chemical makeup. The coverage of microbial ecology includes a chapter on symbiosis. Symbiotic relationships are further developed in a section on physiological ecology, which includes discussions of adaptations to sulfide, thermal tolerances, and sensory adaptations. Separate chapters are devoted to trophic relationships and reproductive ecology. A chapter on community dynamics reveals what has been learned about the ways in which vent communities become established and why they persist, while a chapter on evolution and biogeography examines patterns of species diversity and evolutionary relationships within chemosynthetic ecosystems. Cognate communities such as seeps and whale skeletons come under scrutiny for their ability to support microbial and invertebrate communities that are ecologically and evolutionarily related to hydrothermal faunas. The book concludes by exploring the possibility that life originated at hydrothermal vents, a hypothesis that has had tremendous impact on our ideas about the potential for life on other planets or planetary bodies in our solar system.
Client-side Active Measurements Without Application Control

Monitoring performance and availability are critical to operating successful content provider networks. Internet measurements provide data needed for traffic engineering, alerting, and network diagnostics. While there are significant benefits to combining server-side passive measurements with end-user active measurements, these capabilities are limited to a small number of content providers with both network and application control. In this work, we present a solution to a long-standing problem for a method to issue active measurements from clients without application control. Our approach uses features of the W3C Network Error Logging specification that allow a CDN to induce a browser connection to an HTTPS server of the CDN's choosing.
Scalable Bayesian Estimation in the Multinomial Probit Model

Abstract The multinomial probit (MNP) model is a popular tool for analyzing choice behavior as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This article proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating this structure is that the model parameters require identifying restrictions. We identify the parameters by a trace-restriction on the covariance matrix, which is imposed through a reparameterization of the factor structure. We specify interpretable prior distributions on the model parameters and develop an MCMC sampler for parameter estimation. The proposed approach significantly improves performance in large choice sets relative to existing MNP specifications. Applications to purchase data show the economic importance of including a large number of choice alternatives in consumer choice analysis.
Intracranial hemodynamics simulations: An efficient and accurate immersed boundary scheme

Computational fluid dynamics (CFD) studies have been increasingly used for blood flow simulations in intracranial aneurysms (ICAs). However, despite the continuous progress of body-fitted CFD solvers, generating a high quality mesh is still the bottleneck of the CFD simulation, and strongly affects the accuracy of the numerical solution. To overcome this challenge, which will allow preforming CFD simulations efficiently for a large number of aneurysm cases we use an Immersed Boundary (IB) method. The proposed scheme relies on Cartesian grids to solve the incompressible Navier-Stokes (N-S) equations, using a finite element solver, and Lagrangian points to discretize the immersed object. All grid generations are conducted through automated algorithms which require no user input. Consequently, we verify the proposed method by comparing our numerical findings (velocity values) with published experimental results. Finally, we test the ability of the scheme to efficiently handle hemodynamic simulations on complex geometries on a sample of four patient-specific intracranial aneurysms.
SOTERIA: In Search of Efficient Neural Networks for Private Inference

ML-as-a-service is gaining popularity where a cloud server hosts a trained model and offers prediction (inference) service to users. In this setting, our objective is to protect the confidentiality of both the users' input queries as well as the model parameters at the server, with modest computation and communication overhead. Prior solutions primarily propose fine-tuning cryptographic methods to make them efficient for known fixed model architectures. The drawback with this line of approach is that the model itself is never designed to operate with existing efficient cryptographic computations. We observe that the network architecture, internal functions, and parameters of a model, which are all chosen during training, significantly influence the computation and communication overhead of a cryptographic method, during inference. Based on this observation, we propose SOTERIA -- a training method to construct model architectures that are by-design efficient for private inference. We use neural architecture search algorithms with the dual objective of optimizing the accuracy of the model and the overhead of using cryptographic primitives for secure inference. Given the flexibility of modifying a model during training, we find accurate models that are also efficient for private computation. We select garbled circuits as our underlying cryptographic primitive, due to their expressiveness and efficiency, but this approach can be extended to hybrid multi-party computation settings. We empirically evaluate SOTERIA on MNIST and CIFAR10 datasets, to compare with the prior work. Our results confirm that SOTERIA is indeed effective in balancing performance and accuracy.
Geometry and control of the nonholonomic integrator: An electrodynamics analogy

We consider some generalizations of the classical nonholonomic integrator and give a geometric approach to characterize controllability for these systems. We use Stokes' theorem and results from complex analysis to obtain necessary and sufficient conditions for controllability. Furthermore, we show that optimal trajectories of certain minimum energy optimal control problems defined on these systems can be identified with the trajectory of a charged particle in an electromagnetic field.
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding

Processing of raw text is the crucial first step in text classification and sentiment analysis. However, text processing steps are often performed using off-the-shelf routines and pre-built word dictionaries without optimizing for domain, application, and context. This paper investigates the effect of seven text processing scenarios on a particular text domain (Twitter) and application (sentiment classification). Skip gram-based word embeddings are developed to include Twitter colloquial words, emojis, and hashtag keywords that are often removed for being unavailable in conventional literature corpora. Our experiments reveal negative effects on sentiment classification of two common text processing steps: 1) stop word removal and 2) averaging of word vectors to represent individual tweets. New effective steps for 1) including non-ASCII emoji characters, 2) measuring word importance from word embedding, 3) aggregating word vectors into a tweet embedding, and 4) developing linearly separable feature space have been proposed to optimize the sentiment classification pipeline. The best combination of text processing steps yields the highest average area under the curve (AUC) of 88.4 (+/-0.4) in classifying 14,640 tweets with three sentiment labels. Word selection from context-driven word embedding reveals that only the ten most important words in Tweets cumulatively yield over 98% of the maximum accuracy. Results demonstrate a means for data-driven selection of important words in tweet classification as opposed to using pre-built word dictionaries. The proposed tweet embedding is robust to and alleviates the need for several text processing steps.
The Role of Infrared Radiation Pressure in Shaping Dusty Winds in AGNs

The detection of dusty winds dominating the infrared emission of active galactic nuclei (AGNs) on parsec scales has revealed the limitations of traditional radiative transfer models based on a toroidal distribution of dusty gas. A new, more complex, dynamical structure is emerging and the physical origin of such dusty winds has to be critically assessed. We present a semi-analytical model to test the hypothesis of radiatively accelerated dusty winds launched by the AGN and by the heated dust itself. The model consists of an AGN and an infrared radiating dusty disk, the latter being the primary mass reservoir for the outflow. We calculate the trajectories of dusty gas clumps in this environment, accounting for both gravity and the AGN radiation as well as the re-radiation by the hot, dusty gas clouds themselves. We find that the morphology consists of a disk of material that orbits with sub-Keplerian velocities and a hyperboloid polar wind launched at the inner edge of the dusty disk. This is consistent with high-angular resolution infrared and sub-mm observations of some local Seyfert AGN. The strength of the wind and its orientation depend on the Eddington ratio and the column density of the dusty clumps, which is in agreement with proposed radiation regulated obscuration models developed for the X-ray obscuring material around AGNs.
A deep learning based multiscale approach to segment cancer area in liver whole slide image

This paper addresses the problem of liver cancer segmentation in Whole Slide Image (WSI). We propose a multi-scale image processing method based on automatic end-to-end deep neural network algorithm for segmentation of cancer area. A seven-levels gaussian pyramid representation of the histopathological image was built to provide the texture information in different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsumpling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scales approach achieving better scores compared to the state-of-the-art.
Providing Appropriate Social Support to Prevention of Depression for Highly Anxious Sufferers

Depression is becoming a serious global health problem worldwide, with an increasing number of patients suffering from anxiety and other disorders. Our work aims to provide the appropriate social support (SS) to the prevention of depression for highly anxious undergraduates. We used 1425 undergraduates from 18 universities in China via a cluster random sampling method for the survey on the self-rating anxiety scale, the self-rating depression scale, and the SS scale for anxiety and depression. Based on the collected questionnaire data, we first reveal that the distribution of both anxiety data and depression data follows a Gaussian distribution. Then, a Gaussian mixture model is adopted for clustering these data in terms of anxiety index and depression index. According to the observations extracted from the clusters, the correlation among anxiety, depression, and SS is investigated by a correlation analysis method. Finally, the corresponding moderating effect of SS between anxiety and depression is figured out via the hierarchical multiple regression analysis. The detailed analysis indicates that the high-level SS, such as the help and support from individual’s friends or family members, could reduce the risk for depression from highly anxious undergraduates.
Normal approximation for generalized U-statistics and weighted random graphs

We derive normal approximation bounds in the Wasserstein distance for sums of generalized U-statistics, based on a general distance bound for functionals of independent random variables of arbitrary distributions. Those bounds are applied to normal approximation for the combined weights of subgraphs in the Erdős–Rényi random graph, extending the graph counting results of Barbour et al. (A central limit theorem for decomposable random variables with applications to random graphs, J. Combin. Theory Ser. B 47(2) (1989), pp. 125–145) to the setting of weighted graphs. Our approach relies on a general stochastic analytic framework for functionals of independent random sequences.
The metabolism of glucose, acetate, lipids and amino acids in lactating dairy cows

SUMMARY Specialized techniques, previously used in surgically prepared goats, which simultaneously measure udder metabolism (arteriovenous difference of milk precursors x udder blood flow) and the whole body turnover of the milk precursors, have been successfully transferred to dairy cows. Methods of obtaining representative samples of arterial and mammary venous blood and of measuring udder blood flow are described. The rates of entry into the circulation, as determined by isotope dilution, of glucose, acetate and plasma free fatty acids were 3·3–4·0, 1·7–2·1 and 0·5 kg/day respectively. Acetate and glucose contributed 32–50 and 4–11% respectively of the total CO2output by the animal. Measurement of the uptake of precursors of milk constituents and their transfer into milk showed that there were substantial arteriovenous differences of glucose, acetate, triglyceride and β-hydroxybutyrate which were not significantly different between breeds or related to milk yield. Isotopic and balance data confirm that glucose is the main precursor of lactose and that the oxidation and transfer of glucose into lactose accounted for 69–98% of the glucose entry rate. As in the goat, plasma triglycerides and blood acetate accounted for 35–80% and 25–50% of the milk triglycerides respectively. Propionate was extracted from plasma but the uptake was only about 8% of the value for acetate. There was no net arteriovenous difference of phospholipids, cholesterol esters or free fatty acids, but the fall in specific radioactivity of free fatty acids across the mammary gland indicated there was an exchange of free fatty acids between plasma and mammary tissue. In agreement with previous findings, acetate contributed to all the milk fatty acids up to a chain length of C14 and part of the C16 fatty acid. Plasma triglycerides contributed to the remainder of the C16 fatty acid and all the milk fatty acids with a chain length of C18 or higher. In contrast to the lactating goat, cow plasma contained very few chylomicrons. The majority of the triglycerides taken up by the udder were derived from the lowdensity lipoprotein fraction. The essential amino acids were extracted from blood in amounts sufficient to account for the essential amino acids secreted into milk protein. Although the plasma level of methionine was low, 52–72% of the material reaching the mammary gland was taken up. The uptake of arginine was far in excess of the requirement for milk protein synthesis.