cssi/SciSciGPT-SciSciNet huggingface dataset:
Specific heat and pairing of Dirac composite fermions in the half-filled Landau level

Abstract A recent proposal argues that an alternate description of the half-filled Landau level is a theory of massless Dirac fermions. We examine the possibility of pairing of these Dirac fermions by numerically solving the coupled Eliashberg equations unlike our previous calculation (Wang and Chakravarty, 2016). In addition, vertex corrections are calculated to be zero from the Ward identity. We find that pairing is possible in non-zero angular momentum channels; only differences are minor numerical shifts. As before, the pairing leads to the gapped Pfaffian and anti-Pfaffian states. However, in our approximation scheme, pairing is not possible in the putative particle–hole symmetric state for l = 0 angular momentum. The specific heat at low temperatures of a system of massless Dirac fermions interacting with a transverse gauge field, expected to be relevant for the half-filled Landau level, is calculated. Using the Luttinger formula, it is found to be ∝ T ln T in the leading low temperature limit, due to the exchange of transverse gauge bosons. The result agrees with the corresponding one in the nonrelativistic composite fermion theory of Halperin, Lee and Read of the half-filled Landau level.
Waring Rank, Parameterized and Exact Algorithms

We show that the Waring rank (symmetric tensor rank) of a certain family of polynomials has intimate connections to the areas of parameterized and exact algorithms, generalizing some well-known methods and providing a concrete approach to obtain faster approximate counting and deterministic decision algorithms. To illustrate the amenability and utility of this approach, we give an algorithm for approximately counting subgraphs of bounded treewidth, improving on earlier work of Alon, Dao, Hajirasouliha, Hormozdiari, and Sahinalp. Along the way we give an exact answer to an open problem of Koutis and Williams and sharpen a lower bound on the size of perfectly balanced hash families given by Alon and Gutner.
Microfluidic rapid and autonomous analytical device (microRAAD) to detect HIV from whole blood samples

While identifying acute HIV infection is critical to providing prompt treatment to HIV-positive individuals and preventing transmission, existing laboratory-based testing methods are too complex to perform at the point of care. Specifically, molecular techniques can detect HIV RNA within 8-10 days of transmission but require laboratory infrastructure for cold-chain reagent storage and extensive sample preparation performed by trained personnel. Here, we demonstrate our point-of-care microfluidic rapid and autonomous analysis device (microRAAD) that automatically detects HIV RNA from whole blood. Inside microRAAD, we incorporate vitrified amplification reagents, thermally-actuated valves for fluidic control, and a temperature control circuit for low-power heating. Reverse transcription loop-mediated isothermal amplification (RT-LAMP) products are visualized using a lateral flow immunoassay (LFIA), resulting in an assay limit of detection of 100 HIV-1 RNA copies when performed as a standard tube reaction. Even after three weeks of room-temperature reagent storage, microRAAD automatically isolates the virus from whole blood, amplifies HIV-1 RNA, and transports amplification products to the internal LFIA, detecting as few as 3 × 105 HIV-1 viral particles, or 2.3 × 107 virus copies per mL of whole blood, within 90 minutes. This integrated microRAAD is a low-cost and portable platform to enable automated detection of HIV and other pathogens at the point of care.
Escherichia coli Clonobiome: Assessing the Strain Diversity in Feces and Urine by Deep Amplicon Sequencing.

ABSTRACT While microbiome studies have focused on diversity at the species level or higher, bacterial species in microbiomes are represented by different, often multiple, strains. These strains could be clonally and phenotypically very different, making assessment of strain content vital to a full understanding of microbiome function. This is especially important with respect to antibiotic-resistant strains, the clonal spread of which may be dependent on competition between them and susceptible strains from the same species. The pandemic, multidrug-resistant, and highly pathogenic Escherichia coli subclone ST131-H30 (H30) is of special interest, as it has already been found persisting in the gut and bladder in healthy people. In order to rapidly assess E. coli clonal diversity, we developed a novel method based on deep sequencing of two loci used for sequence typing, along with an algorithm for analysis of the resulting data. Using this method, we assessed fecal and urinary samples from healthy women carrying H30 and were able to uncover considerable diversity, including strains with frequencies at IMPORTANCE Bacterial species in the microbiome are often represented by multiple genetically and phenotypically different strains, making insight into subspecies diversity critical to a full understanding of the microbiome, especially with respect to opportunistic pathogens. However, methods allowing efficient high-throughput clonal typing are not currently available. This study combines a conventional E. coli typing method with deep amplicon sequencing to allow analysis of many samples concurrently. While our method was developed for E. coli, it may be adapted for other species, allowing microbiome researchers to assess clonal strain diversity in natural samples. Since assessment of subspecies diversity is particularly important for understanding the spread of antibiotic resistance, we applied our method to the study of a pandemic multidrug-resistant E. coli clone. The results we present suggest that this clone could be highly competitive in healthy carriers and that the mechanisms of colonization by such clones need to be studied.
Myco-accessories: sustainable wearables with biodegradable materials.

Sustainability has been addressed in fashion, product design and furniture making fields. Recycling and disposing e-textiles has been a concern in this community for years[20]; however, the impact of designing sustainable wearables is still new territory that requires more exploration. This paper approaches sustainability in the prototyping process by producing wearables that make use of biodegradable material for embedding electronics. We have used mycelium, that unlike other biodegradable materials such as kombucha, algae, or bioplastics, has heat resistance, thermal resistance and hydrophobic properties which makes it suitable to apply in wearables. Moreover, this paper proposes a sustainable life cycle that uses biodegradable materials to embed electronics. For example, we embedded an electronic circuit into mycelium skin to make an accessory. After the accessory has been worn, the electronic components can be reused and the mycelium skin composted. Lastly, we present our method for growing mycelium, our design process using common techniques in embedding electronics, and Myco-accessories as applications to envision the possibilities of this material. This paper aims to contribute to prototyping wearables sustainably by intertwining biomaterials and electronics.
Some challenges in statistical signal processing some thoughts on those challenges and some challenges to those challenges

Summary form only given. This talk builds around three challenges: fusing heterogeneous and uncertain sources of information, extracting and tracking geometry, and dealing with computational complexity. In the first of these it is briefly describe the work being done in the group (and led by Dr. John Fisher) on machine learning/information-theoretic methods, with illustrations in audio/video fusion and in association of signals subject to propagation distortion. The second topic focuses on so-called curve evolution methods, their probabilistic interpretation, and some of the methods being developed in the group that incorporate both information-theoretic methods for "blind" segmentation and prior modeling of shapes for segmentation in complex imagery. Illustrations would be given to two problems of profound societal importance: automatic segmentation of the prostate (for prostate brachytherapy) and automatic segmentation of photos of zebras when you have no idea what a zebra is. The third topic deals with research in the group on large-scale problems of statistical inference specified in terms of so-called graphical models. The paper briefly describe some of the significant theoretical successes we have had in developing methods that exploit embedded tractable graphical structures to solve both large-scale estimation problems and large integer programming problems such as arise in data association. Finally, two really big challenges-very large-scale data assimilation for geophysical fields and fusion of large numbers of distributed and individually very limited sensors is briefly commented. Addressing each of these challenges requires much more than the union of the topics addressed in this talk, but asking a few questions that suggest how research in these areas might evolve to contribute to these very large and very interesting challenges.
Balancing model-based and memory-free action selection under competitive pressure

The best predictor of future behavior is past behavior, so the saying goes. And studies show that in many situations, we do have a tendency to repeat whatever we did last time, particularly if it led to success. But while this is an efficient way to decide what to do, it is not always the best strategy. In many competitive situations – from tennis matches to penalty shoot-outs – there are advantages to being unpredictable. You are more likely to win if your opponent cannot guess your next move. Based on this logic, Kikumoto and Mayr predicted that in competitive situations, people will toggle between two different decision-making strategies. When they are winning, they will choose their next move based on their beliefs about their opponent’s strategy. After all, if your opponent in a tennis match has failed to return your last three backhands, it is probably worth trying a fourth. But if an action no longer leads to success, people will switch tactics. Rather than deciding what to do based on their opponent’s strategy and recent behavior, they will instead select their next move more at random. If your tennis opponent suddenly starts returning your backhands, trying any other shot will probably produce better results. To test this prediction, Kikumoto and Mayr asked healthy volunteers to play a game against real or computer opponents. The game was based on the 'matching pennies' game, in which each player has to choose between two responses. If both players choose the same response, player 1 wins. If each player chooses a different response, player 2 wins. Some of the opponents used response strategies that were easy to figure out; others were less predictable. The results showed that after wins, the volunteers’ next moves reflected their beliefs about their opponent's strategy. But after losses, the volunteers’ next moves were based less on previous behaviors, and were instead more random. These differences could even be seen in the volunteers’ brainwaves after win and loss trials. As well as providing insights into how we learn from failures, these findings may also be relevant to depression. People with depression tend to switch away from a rationale decision-making strategy too quickly after receiving negative feedback. This can lead to suboptimal behavior patterns that make it more difficult for the person to recover. Future studies should explore whether the short-term decision-making strategies identified in the current study can also provide clues to these behaviors.
Observing the 3D Chemical Bond and its Energy Distribution in a Projected Space

Our curiosity-driven desire to "see" chemical bonds dates back at least one-hundred years, perhaps to antiquity. Sweeping improvements in the accuracy of measured and predicted electron charge densities, alongside our largely bondcentric understanding of molecules and materials, heighten this desire with means and significance. Here we present a method for analyzing chemical bonds and their energy distributions in a two-dimensional projected space called the condensed charge density. Bond "silhouettes" in the condensed charge density can be reverse-projected to reveal precise three-dimensional bonding regions we call bond bundles. We show that delocalized metallic bonds and organic covalent bonds alike can be objectively analyzed, the formation of bonds observed, and that the crystallographic structure of simple metals can be rationalized in terms of bond bundle structure. Our method also reproduces the expected results of organic chemistry, enabling the recontextualization of existing bond models from a charge density perspective.
Preliminary Results in Current Profile Estimation and Doppler-aided Navigation for Autonomous Underwater Gliders

This paper describes the development and experimental results of navigation algorithms for an autonomous underwater glider (AUG) that uses an on-board acoustic Doppler current profiler (ADCP). AUGs are buoyancy-driven autonomous underwater vehicles that use small hydrofoils to make forward progress while profiling vertically. During each dive, which can last up to 6 hours, the Seaglider AUG used in this experiment typically reaches the depth of 1000 m and travels 3-6 km horizontally through the water, relying solely on dead-reckoning. Horizontal through-the-water (TTW) progress of AUG is 20-30 cm/s, which is comparable to the speed of the stronger ocean currents. Underwater navigation of an AUG in the presence of unknown advection therefore presents a considerable challenge. We develop two related formulations for post-processing. Both use ADCP observations, through the water velocity estimates, and GPS fixes to estimate current profiles. However, while the first solves an explicit inverse problem for the current profiles only, the second solves a deconvolution problem that infers both states and current profiles using a state-space model.Both approaches agree on their estimates of the ocean current profile through which the AUG was flown using measurements of current relative to the AUG from the ADCP, and estimates of the AUGs TTW velocity from a hydrodynamic model. The result is a complete current profile along the AUGs trajectory, as well as over-the-ground (OTG) velocities for the AUG that can be used for more accurate subsea positioning. Results are demonstrated using 1 MHz ADCP data collected from a Seaglider AUG deployed for 49 days off the north coast of Alaska during August and September 2017. The results are compared to ground truth data from the top 40 meters of the water column, from a moored, upward-facing 600 kHz ADCP. Consequences of the state-space formulation are discussed in the Conclusions section.
Spectral tuning of adaptation supports coding of sensory context in auditory cortex

Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information over hundreds of milliseconds. Sound-evoked activity in auditory cortex typically has much shorter latency, but the acoustic context, i.e., sound history, can modulate sound evoked activity over longer periods. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific adaption and contrast gain control. However, an encoding model that links context to natural sound processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo adaptation mimicking short-term synaptic plasticity (STP) can account for contextual effects during natural sound processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were characterized by a standard linear-nonlinear spectro-temporal receptive field (LN) model and variants that incorporated STP-like adaptation. In the adapting models, STP was applied either globally across all input spectral channels or locally to subsets of channels. For most neurons, models incorporating local STP predicted neural activity as well or better than LN and global STP models. The strength of nonlinear adaptation varied across neurons. Within neurons, adaptation was generally stronger for spectral channels with excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP models were compared between passive and active behavior conditions, response gain often changed, but average STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a mechanism with STP-like dynamics, may support representation of the complex spectro-temporal patterns that comprise natural sounds across wide-ranging sensory contexts.
REPAIR: Removing Representation Bias by Dataset Resampling

Modern machine learning datasets can have biases for certain representations that are leveraged by algorithms to achieve high performance without learning to solve the underlying task. This problem is referred to as “representation bias”. The question of how to reduce the representation biases of a dataset is investigated and a new dataset REPresentAtion bIas Removal (REPAIR) procedure is proposed. This formulates bias minimization as an optimization problem, seeking a weight distribution that penalizes examples easy for a classifier built on a given feature representation. Bias reduction is then equated to maximizing the ratio between the classification loss on the reweighted dataset and the uncertainty of the ground-truth class labels. This is a minimax problem that REPAIR solves by alternatingly updating classifier parameters and dataset resampling weights, using stochastic gradient descent. An experimental set-up is also introduced to measure the bias of any dataset for a given representation, and the impact of this bias on the performance of recognition models. Experiments with synthetic and action recognition data show that dataset REPAIR can significantly reduce representation bias, and lead to improved generalization of models trained on REPAIRed datasets. The tools used for characterizing representation bias, and the proposed dataset REPAIR algorithm, are available at https://github.com/JerryYLi/Dataset-REPAIR/.
Changing linear polarization properties in the dust tail of comet C/2012 S1 (ISON)

Abstract Polarization measurements made with the STEREO/SECCHI COR1 and COR2 coronagraphs during the perihelion passage of sungrazing comet C/2012 S1 (ISON) in November 2013 are presented and compared with earlier measurements made of Kreutz sungrazing comet C/2011 W3 (Lovejoy). Comet ISON was an Oort Cloud object, probably entering the solar system for the first time. Strong evidence was found for a radical change in the polarization properties once the tail particles approached within 10 solar radii of Sun center. Above this apparent threshold, the polarization properties of Comet ISON appeared very similar to those seen previously for Comet Lovejoy, with high positive polarization, and a broad negative branch reaching out to an inversion point between 40°–50°. Below 10 solar radii the polarization levels drop dramatically; an effect not seen for Comet Lovejoy. We speculate that radiation processes close to the Sun, and possibly starting as far out as 0.25 AU (50 solar radii), are responsible for the high levels of polarization seen in both comets. This may be due either to the disassociation of fluffy aggregates into the component monomers, or the sublimation of organic refractory mantles from silicate grain cores, and subsequent crystallization of the silicate cores. These two scenarios may happen in conjunction with each other. The mechanism behind the precipitous drop in Comet ISON’s polarization below 10 solar radii is unclear, but may be due to new much larger particles being produced from the breakup of macroscopic debris material embedded within the tail, possibly caused by the sublimation of olivines.
Advanced Channel Analysis Method using Channel Quality Comparison and Design of Experiments

A new SI simulation method called Advanced CQC is introduced. Advanced CQC Method can help PCB designers to check the board design quality quickly and also provide PCB manufacture variation information for design trade-off once the product design is out of guideline.
Adaptive Task Allocation for Mobile Edge Learning

This paper aims to establish a new optimization paradigm to efficiently execute distributed learning tasks on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this new paradigm as “Mobile Edge Learning (MEL)”. The problem of adaptive task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computation on heterogeneous nodes, are bounded by a preset duration. The problem is first formulated as a quadratically-constrained integer linear problem. Being NP-hard, the paper relaxes it into a non-convex problem over real variables. We then propose a solution based on deriving analytical upper bounds on the optimal solution of this relaxed problem using KKT conditions. The merits of this proposed solution is exhibited by comparing its performances to both numerical approaches and the equal task allocation approach.
Nitric oxide acts as a cotransmitter in a subset of dopaminergic neurons to diversify memory dynamics

Animals employ diverse learning rules and synaptic plasticity dynamics to record temporal and statistical information about the world. However, the molecular mechanisms underlying this diversity are poorly understood. The anatomically defined compartments of the insect mushroom body function as parallel units of associative learning, with different learning rates, memory decay dynamics and flexibility (Aso and Rubin, 2016). Here, we show that nitric oxide (NO) acts as a neurotransmitter in a subset of dopaminergic neurons in Drosophila. NO's effects develop more slowly than those of dopamine and depend on soluble guanylate cyclase in postsynaptic Kenyon cells. NO acts antagonistically to dopamine; it shortens memory retention and facilitates the rapid updating of memories. The interplay of NO and dopamine enables memories stored in local domains along Kenyon cell axons to be specialized for predicting the value of odors based only on recent events. Our results provide key mechanistic insights into how diverse memory dynamics are established in parallel memory systems.
SPDI: data model for variants and applications at NCBI.

Motivation Normalizing sequence variants on a reference, projecting them across congruent sequences and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function. Inconsistent representation of variants among variant callers, local databases and tools result in discrepancies that complicate analysis. NCBI's genetic variation resources, dbSNP and ClinVar, require a robust, scalable set of principles to manage asserted sequence variants. Results The SPDI data model defines variants as a sequence of four attributes: sequence, position, deletion and insertion, and can be applied to nucleotide and protein variants. NCBI web services convert representations among HGVS, VCF and SPDI and provide two functions to aggregate variants. One, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the 'Contextual Allele'. The SPDI data model, with its four operations, defines exactly the reference subsequence affected by the variant, even in repeat regions, such as homopolymer and other sequence repeats. The second function projects variants across congruent sequences and depends on an alignment dataset of non-assembly NCBI RefSeq sequences (prefixed NM, NR and NG), as well as inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs), supporting robust projection of variants across congruent sequences and assembly versions. The variant is projected to all congruent Contextual Alleles. One of these Contextual Alleles, typically the allele based on the latest assembly version, represents the entire set, is designated the unique 'Canonical Allele' and is used directly to aggregate variants across congruent sequences. Availability and implementation The SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0. Supplementary information Supplementary data are available at Bioinformatics online.
Creation or Destruction? STEM OPT Extension and Employment of Information Technology Professionals.

Information technology (IT) professionals play an important role in firms' IT investments, technology formation, development, and innovation, contributing to economic growth in the U.S. The use of temporary work visas and related immigration policies that import foreign IT professionals has attracted a significant controversy and policy debates in the U.S. On the one hand, foreign IT professionals may complement domestic IT professionals by facilitating local innovation and expanding local employment. On the other hand, foreign professionals may substitute the domestic counterparts by intensifying labor-market competition, leading to a decrease in wage or even crowding-out of domestic IT professionals. In this study, we focus on an extension in the Optional Practical Training (OPT) program for graduates in science, technology, engineering, and mathematics (STEM) from U.S. institutions, a policy change which greatly increases the supply of foreign IT professionals in local labor markets. Specifically, we explore: (1) the effects of the OPT extension on the number and wage of domestic workers in STEM occupations and (2) how the effects may differ between IT and non-IT STEM occupations. We test the effects using a novel and rich data set that is merged from several sources and use a difference-in-differences model to bring identification. Our results demonstrate that an increase in the supply of foreign IT professionals as a result of the OPT extension boosts employment for domestic IT professionals. The unique characteristics of IT human capital differentiate the impacts on IT occupations from non-IT STEM occupations. This study contributes to the information systems, labor economics, and public policy literature by quantifying the impacts of a policy change on the supply and return of IT professionals and provides rich implications for policymakers.
The tradeoff between relevance and comparability in segment reporting

Abstract The rule change for segment reporting in 1998 has arguably made segment reporting more relevant through the adoption of the management approach. Meanwhile, the management approach has resulted in a decrease in the comparability of segment income. We introduce firm-specific measures of changes in relevance and comparability due to the rule change. Our treatment firms experienced an increase in the relevance of segment reporting but a large decrease in the comparability of segment income; our benchmark firms barely experienced any changes in relevance and comparability. We examine earnings forecasts before vs. after the rule change issued by financial analysts—a major user group of segment reporting. Relative to benchmark firms, treatment firms’ analyst forecast error reductions around the segment disclosure event are not significantly different after the rule change than before the rule change, but treatment firms’ forecast dispersion reductions around the segment disclosure event are significantly larger after the rule change than before the rule change. These results suggest that despite the decrease in comparability, the new segment reporting rule has increased the decision usefulness of segment information by decreasing disagreement among analysts.
Hofstede's Cultural Dimensions and Tourist Behaviors: A Review and Conceptual Framework

This paper develops a conceptual framework for analyzing tourist behaviors and identifies three categories of behaviors based on the applications of Hofstede’s cultural dimensions and the processes underlying these influences. Our findings indicate that tourist behaviors in the Before-Travel, During-Travel, and After-Travel stages differ significantly in terms of the applicability and process through which Hofstede’s cultural dimensions operate. The results of our analysis suggest three categories of behavioral patterns, namely, “Social Interaction Driven Travel Behaviors,” (SID), “Risk Tendencies Driven Travel Behaviors,” (RTD), and “Collectivity Orientation Driven Travel Behaviors,” (COD). SID relates to the evaluation of travel experiences in the after-travel stage. The dominant cultural values associated with SID are Individualism/Collectivism, Masculinity/Femininity, and Power Distance. These three values act either independently or in pairs or all three together. RTD relates to the consumption of travel products in the during-travel stage, and COD relates to the formation of travel preferences in the before-travel stage. Individualism/Collectivism and Uncertainty Avoidance are associated with both RTD and COD. However, the underlying processes differ for these two categories of travel behaviors. In addition to their independent influences on travel behaviors, these two values associated with RTD and COD also have an interactive effect. For RTD, the Uncertainty Avoidance motive determines the Individualism/Collectivism outcome, whereas, for COD, the opposite is true: the Individualism/Collectivism determines the Uncertainty Avoidance outcome. The paper also discusses the application of a fifth cultural dimension, Confucian Dynamism (short-term versus long-term orientation), for the study of tourists’ behaviors.
High Remaining Factors in the Photovoltaic Performance of Perovskite Solar Cells after High-Fluence Electron Beam Irradiations

Metal halide perovskite solar cells have progressed rapidly over the past decade, providing an exceptional opportunity for space photovoltaic (PV) power applications. However, the solar cells to be...
Short and Sparse Deconvolution --- A Geometric Approach

Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in applications such as image deblurring, microscopy, neural spike sorting, and more. The problem is challenging in both theory and practice, as natural optimization formulations are nonconvex. Moreover, practical deconvolution problems involve smooth motifs (kernels) whose spectra decay rapidly, resulting in poor conditioning and numerical challenges. This paper is motivated by recent theoretical advances \citep{zhang2017global,kuo2019geometry}, which characterize the optimization landscape of a particular nonconvex formulation of SaSD. This is used to derive a {\em provable} algorithm which exactly solves certain non-practical instances of the SaSD problem. We leverage the key ideas from this theory (sphere constraints, data-driven initialization) to develop a {\em practical} algorithm, which performs well on data arising from a range of application areas. We highlight key additional challenges posed by the ill-conditioning of real SaSD problems, and suggest heuristics (acceleration, continuation, reweighting) to mitigate them. Experiments demonstrate the performance and generality of the proposed method.
How Should an Agent Practice

We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available. During practice, the environment may differ from the one available for training and evaluation with extrinsic rewards. We refer to this setup of alternating periods of practice and objective evaluation as practice-match, drawing an analogy to regimes of skill acquisition common for humans in sports and games. The agent must effectively use periods in the practice environment so that performance improves during matches. In the proposed method the intrinsic practice reward is learned through a meta-gradient approach that adapts the practice reward parameters to reduce the extrinsic match reward loss computed from matches. We illustrate the method on a simple grid world, and evaluate it in two games in which the practice environment differs from match: Pong with practice against a wall without an opponent, and PacMan with practice in a maze without ghosts. The results show gains from learning in practice in addition to match periods over learning in matches only.
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision

Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision – which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system.
Stochastic PDE Limit of the Six Vertex Model

We study the stochastic six vertex model and prove that under weak asymmetry scaling (i.e., when the parameter $$\Delta \rightarrow 1^+$$ so as to zoom into the ferroelectric/disordered phase critical point) its height function fluctuations converge to the solution to the Kardar–Parisi–Zhang (KPZ) equation. We also prove that the one-dimensional family of stochastic Gibbs states for the symmetric six vertex model converge under the same scaling to the stationary solution to the stochastic Burgers equation. Our proofs rely upon the Markov (self) duality of our model. The starting point is an exact microscopic Hopf–Cole transform for the stochastic six vertex model which follows from the model’s known one-particle Markov self-duality. Given this transform, the crucial step is to establish self-averaging for specific quadratic function of the transformed height function. We use the model’s two-particle self-duality to produce explicit expressions (as Bethe ansatz contour integrals) for conditional expectations from which we extract time-decorrelation and hence self-averaging in time. The crux of our Markov duality method is that the entire convergence result reduces to precise estimates on the one-particle and two-particle transition probabilities. Previous to our work, Markov dualities had only been used to prove convergence of particle systems to linear Gaussian SPDEs (e.g. the stochastic heat equation with additive noise).
Hybrid Camera Pose Estimation With Online Partitioning for SLAM

This letter presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to monocular Simultaneous Localization and Mapping (SLAM) systems. Breaking through the limitations of fixed-size temporal partitioning in many conventional SLAM pipelines, our approach significantly improves the accuracy of local bundle adjustment by gathering spatially-strongly-connected cameras into each block. With the dynamic initialization using intermediate computation values, we improve the Levenberg-Marquardt solver to further enhance the efficiency of the local optimization. Moreover, the dense data association between blocks by our co-visibility-based partitioning enables us to explore and implement motion averaging to efficiently align the blocks globally, updating camera motion estimations on-the-fly. Experiments on benchmarks convincingly demonstrate the practicality and robustness of our proposed approach by significantly outperforming conventional approaches.
Real-Time Local Volt/VAR Control Under External Disturbances with High PV Penetration

Volt/var control (VVC) of smart PV inverter is becoming one of the most popular solutions to address the voltage challenges associated with high PV penetration. This work focuses on the local droop VVC recommended by the grid integration standards IEEE1547, rule21 and addresses their major challenges i.e. appropriate parameters selection under changing conditions, and the control being vulnerable to instability (or voltage oscillations) and significant steady state error (SSE). This is achieved by proposing a two-layer local real-time adaptive VVC that has two major features i.e. a) it is able to ensure both low SSE and control stability simultaneously without compromising either; and b) it dynamically adapts its parameters to ensure good performance in a wide range of external disturbances such as sudden cloud cover, cloud intermittency, and substation voltage changes. A theoretical analysis and convergence proof of the proposed control is also discussed. The proposed control is implementation friendly as it fits well within the integration standard framework and depends only on the local bus information. The performance is compared with the existing droop VVC methods in several scenarios on a large unbalanced 3-phase feeder with detailed secondary side modeling.
Borrow with Sorrow? The Changing Risk Profile of Sub-Saharan Africa's Debt

In the post-global financial crisis period, the financing of countercyclical policies led not only to a reduction in the fiscal surpluses across Sub-Saharan African countries, but also an increase in their levels of indebtedness. Although public debt for the region in 2018 was still below that of the pre-debt forgiveness period, the risk profile of public debt has sharply increased. The share of concessional public debt has been declining while that owed to private creditors and non–Paris Club bilateral creditors has been rising. The resulting reconfiguration of public debt has led to increased debt service in the region. Hence, the higher risk profile of debt and rising payments might lower the threshold for debt distress in the region. Addressing public debt vulnerabilities requires the buildup of external and fiscal buffers by conducting prudent fiscal policies and implementing growth-enhancing reforms, and the strengthening of debt management practices. However, the policy toolkit can be enlarged by gradually moving from debt management to balance-sheet management of the public sector, and policies to boost the efficiency of public investment.
Evidence linking atopy and staphylococcal superantigens to the pathogenesis of lymphomatoid papulosis, a recurrent CD30+ cutaneous lymphoproliferative disorder

Background Primary cutaneous CD30+ lymphoproliferative disorders (CD30CLPD) are the second most common type of cutaneous T cell lymphoma (CTCL) and include lymphomatoid papulosis (LyP) and primary cutaneous anaplastic large cell lymphoma (pcALCL). Case reports and small patient series suggest an association of CD30CLPD with atopic disorders. However, the prevalence of atopy in patients with CD30CLPD in retrospective studies depends on patients’ recall which is not always reliable. More objective criteria of atopy include evidence of skin reactivity to allergens (positive prick test) and evidence of allergen-specific IgE in serum. This study was undertaken to test the hypothesis that atopy is prevalent in patients with CD30CLPD using serologic criteria of allergen-specific IgE antibodies to aeroallergens and Staphylococcal aureus enterotoxin superantigens (SSAgs). Methods We tested serum samples of CD30CLPD for common IgE-specific airborne allergens with the Phadiatop test, which if positive, is regarded as serologic evidence of atopy in adults. Sera were also tested for IgE antibodies reactive to three Staphylococcal enterotoxins with superantigenic properties (SSAg-IgE). Control sera were obtained from adult subjects evaluated for rhino-sinusitis and a negative Phadiatop test. Patients’ history of an atopic disorder was obtained by retrospective chart review. Findings Nearly 50% of patients with the most common LyP types (A and C) had a positive Phadiatop test for allergic sensitization to common airborne allergens, and total serum IgE (IgE-t) was increased compared to non-atopic controls. At the IgE antibody concentration generally used to define serologic atopy (≥ 0.35 kUA/L), 8/31 (26%) samples of CD30CLPD and 7/28 (25%) samples of LyP were reactive to at least one SSAg-IgE compared to 3/52 (6%) control specimens (P = 0.016 and P = 0.028, respectively). TSST1-IgE was detected in 7 (23%) specimens of CD30CLPD, often together with SEB-IgE; SEA-IgE ≥ 0.35 kUA/L was not detected. For control specimens, TSST1-IgE exceeded the 0.35 kUA/L threshold in 3 (6%) specimens. Conclusions Patients with LyP types A and C have serologic evidence of atopy against common airborne antigens and SSAgs when compared to control adult subjects who had rhino-sinusitis and a negative Phadiatop test for aero-IgEs. Serologic evidence of atopy exceeded that determined by LyP patients’ personal history. The findings support our hypothesis that an atopic diathesis may contribute to the pathogenesis of the most common types of LyP (A and C).
Scheduling in the Presence of Data Intensive Compute Jobs

We study the performance of non-adaptive scheduling policies in computing systems with multiple servers. Compute jobs are mostly regular, with modest service requirements. However, there are sporadic data intensive jobs, whose expected service time is much higher than that of the regular jobs. For this model, we are interested in the effect of scheduling policies on the average time a job spends in the system. To this end, we introduce two performance indicators in a simplified, only-arrival system. We believe that these performance indicators are good predictors of the relative performance of the policies in the queuing system, which is supported by simulations results.
The fibroblast-derived protein PI16 controls neuropathic pain.

Chronic pain is a major clinical problem of which the mechanisms are incompletely understood. Here, we describe the concept that PI16, a protein of unknown function mainly produced by fibroblasts, controls neuropathic pain. The spared nerve injury (SNI) model of neuropathic pain increases PI16 protein levels in fibroblasts in dorsal root ganglia (DRG) meninges and in the epi/perineurium of the sciatic nerve. We did not detect PI16 expression in neurons or glia in spinal cord, DRG, and nerve. Mice deficient in PI16 are protected against neuropathic pain. In vitro, PI16 promotes transendothelial leukocyte migration. In vivo, Pi16−/− mice show reduced endothelial barrier permeability, lower leukocyte infiltration and reduced activation of the endothelial barrier regulator MLCK, and reduced phosphorylation of its substrate MLC2 in response to SNI. In summary, our findings support a model in which PI16 promotes neuropathic pain by mediating a cross-talk between fibroblasts and the endothelial barrier leading to barrier opening, cellular influx, and increased pain. Its key role in neuropathic pain and its limited cellular and tissue distribution makes PI16 an attractive target for pain management.
Discovering patterns of online popularity from time series

Abstract How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform, or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we leverage a multifaceted temporal analysis of the evolution of popular online content. We present dipm-SC: a multidimensional shape-based time-series clustering algorithm with a heuristic to find the optimal number of clusters. First, we validate the accuracy of our algorithm on synthetic datasets generated from benchmark time series models. Second, we show that dipm-SC can uncover meaningful clusters of popularity behaviors in real-world GitHub and Twitter datasets. By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we discover two main patterns of popularity: bursty and steady temporal behaviors. Furthermore, we find that the way popularity is gained over time has no significant impact on the final cumulative popularity.
Bile acid metabolism is altered in multiple sclerosis and supplementation ameliorates neuroinflammation

Multiple sclerosis (MS) is an inflammatory demyelinating disorder of the CNS. Bile acids are cholesterol metabolites that can signal through receptors on cells throughout the body, including in the CNS and the immune system. Whether bile acid metabolism is abnormal in MS is unknown. Using global and targeted metabolomic profiling, we identified lower levels of circulating bile acid metabolites in multiple cohorts of adult and pediatric patients with MS compared with controls. In white matter lesions from MS brain tissue, we noted the presence of bile acid receptors on immune and glial cells. To mechanistically examine the implications of lower levels of bile acids in MS, we studied the in vitro effects of an endogenous bile acid, tauroursodeoxycholic acid (TUDCA), on astrocyte and microglial polarization. TUDCA prevented neurotoxic (A1) polarization of astrocytes and proinflammatory polarization of microglia in a dose-dependent manner. TUDCA supplementation in experimental autoimmune encephalomyelitis reduced the severity of disease through its effects on G protein-coupled bile acid receptor 1 (GPBAR1). We demonstrate that bile acid metabolism was altered in MS and that bile acid supplementation prevented polarization of astrocytes and microglia to neurotoxic phenotypes and ameliorated neuropathology in an animal model of MS. These findings identify dysregulated bile acid metabolism as a potential therapeutic target in MS.
Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

Dynamic affinity load balancing of multi-type tasks on multi-skilled servers, when the service rate of each task type on each of the servers is known and can possibly be different from each other, is an open problem for over three decades. The goal is to do task assignment on servers in a real time manner so that the system becomes stable, which means that the queue lengths do not diverge to infinity in steady state (throughput optimality), and the mean task completion time is minimized (delay optimality). The fluid model planning, Max-Weight, and c- $\mu $ -rule algorithms have theoretical guarantees on optimality in some aspects for the affinity problem, but they consider a complicated queueing structure and either require the task arrival rates, the service rates of tasks on servers, or both. In many cases that are discussed in the introduction section, both task arrival rates and service rates of different task types on different servers are unknown. In this work, the Blind GB-PANDAS algorithm is proposed which is completely blind to task arrival rates and service rates. Blind GB-PANDAS uses an exploration-exploitation approach for load balancing. We prove that Blind GB-PANDAS is throughput optimal under arbitrary and unknown distributions for service times of different task types on different servers and unknown task arrival rates. Blind GB-PANDAS desires to route an incoming task to the server with the minimum weighted-workload, but since the service rates are unknown, such routing of incoming tasks is not guaranteed which makes the throughput optimality analysis more complicated than the case where service rates are known. Our extensive experimental results reveal that Blind GB-PANDAS significantly outperforms existing methods in terms of mean task completion time at high loads.