cssi/SciSciGPT-SciSciNet huggingface dataset:
Communication Cost for Updating Linear Functions When Message Updates are Sparse: Connections to Maximally Recoverable Codes

We consider a communication problem in which an update of the source message needs to be conveyed to one or more distant receivers that are interested in maintaining specific linear functions of the source message. The setting is one in which the updates are sparse in nature, and where neither the source nor the receiver(s) is aware of the exact difference vector , but only know the amount of sparsity that is present in the difference vector. Under this setting, we are interested in devising linear encoding and decoding schemes that minimize the communication cost involved. We show that the optimal solution to this problem is closely related to the notion of maximally recoverable codes (MRCs), which were originally introduced in the context of coding for storage systems. In the context of storage, MRCs guarantee optimal erasure protection when the system is partially constrained to have local parity relations among the storage nodes. In our problem, we show that optimal solutions exist if and only if MRCs of certain kind (identified by the desired linear functions) exist. We consider point-to-point and broadcast versions of the problem and identify connections to MRCs under both these settings. For the point-to-point setting, we show that our linear-encoder-based achievable scheme is optimal even when non-linear encoding is permitted. The theory is illustrated in the context of updating erasure coded storage nodes. We present examples based on modern storage codes, such as the minimum bandwidth regenerating codes.
Who said what: Modeling individual labelers improves classification

Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label or to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computer-aided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona (2010); Mnih and Hinton (2012). Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels for training.
Coupled Topological Surface Modes in Gyrotropic Structures: Green's Function Analysis

At a transition in a wave-guiding structure, part of the incident energy is transmitted and part of the energy is reflected. When the waveguide has nontrivial topological properties, however, the transition may occur with no backscattering and with unusual modal coupling/transformations. Within this context, we discuss the response of a nonreciprocal topological structure composed of two nearby interfaces between oppositely biased gyrotropic media and an isotropic medium, which support unidirectional surface modes (topological modes). We provide an exact Green's function analysis of this structure, and we discuss how the topological surface modes are modified when the two interfaces are brought closer and eventually merged. We show that the resulting mode conversion is independent of the transition geometry.
On the worst-case communication overhead for distributed data shuffling

Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent. In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency. Several computational tasks are of sequential nature, and involve multiple passes over the data. At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process. This random re-shuffling operation comes at the cost of extra communication overhead, since at each shuffle, new data points need to be delivered to the distributed workers. In this paper, we focus on characterizing the information theoretically optimal communication overhead for the distributed data shuffling problem. We propose a novel coded data delivery scheme for the case of no excess storage, where every worker can only store the assigned data batches under processing. Our scheme exploits a new type of coding opportunity and is applicable to any arbitrary shuffle, and for any number of workers. We also present information theoretic lower bounds on the minimum communication overhead for data shuffling, and show that the proposed scheme matches this lower bound for the worst-case communication overhead.
Emotion schemas are embedded in the human visual system

Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, then emotions may be afforded by features of the sensory environment. However, few computational models describe how combinations of stimulus features evoke different emotions. Here, we develop a convolutional neural network that accurately decodes images into 11 distinct emotion categories. We validate the model using more than 25,000 images and movies and show that image content is sufficient to predict the category and valence of human emotion ratings. In two functional magnetic resonance imaging studies, we demonstrate that patterns of human visual cortex activity encode emotion category-related model output and can decode multiple categories of emotional experience. These results suggest that rich, category-specific visual features can be reliably mapped to distinct emotions, and they are coded in distributed representations within the human visual system.
Electric field conjugation for ground-based high-contrast imaging: robustness study and tests with the Project 1640 coronagraph

The electric field conjugation (EFC) algorithm has shown promise for removing scattered starlight from high-contrast imaging measurements, both in numerical simulations and laboratory experiments. To prepare for the deployment of EFC using ground-based telescopes, we investigate the response of EFC to unaccounted for deviations from an ideal optical model. We explore the linear nature of the algorithm by assessing its response to a range of inaccuracies in the optical model generally present in real systems. We find that the algorithm is particularly sensitive to unresponsive deformable mirror (DM) actuators, misalignment of the Lyot stop, and misalignment of the focal plane mask. Vibrations and DM registration appear to be less of a concern compared to values expected at the telescope. We quantify how accurately one must model these core coronagraph components to ensure successful EFC corrections. We conclude that while the condition of the DM can limit contrast, EFC may still be used to improve the sensitivity of high-contrast imaging observations. Our results have informed the development of a full EFC implementation using the Project 1640 coronagraph at Palomar observatory. While focused on a specific instrument, our results are applicable to the many coronagraphs that may be interested in employing EFC.
Active Clothing Material Perception Using Tactile Sensing and Deep Learning

Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.
Stability of catenoids and helicoids in hyperbolic space

In this paper, we study the stability of catenoids and helicoids in the hyperbolic $3$-space $\mathbb{H}^3$. (1) For a family of spherical minimal catenoids $\{\mathcal{C}_a\}_{a>0}$ in $\mathbb{H}^3$, there exist two constants $0<a_c<a_l$ such that $\bullet$ $\mathcal{C}_a$ is an unstable minimal surface with index one if $a<a_c$, $\bullet$ $\mathcal{C}_a$ is a globally stable minimal surface if $a\geq{}a_c$, and $\bullet$ $\mathcal{C}_a$ is a least area minimal surface in the sense of Meeks and Yau if $a\geq{}a_l$. (2) For a family of minimal helicoids $\{\mathcal{H}_{\bar{a}}\}_{\bar{a}\geq{}0}$ in $\mathbb{H}^3$, there exists a constant $\bar{a}_c=\coth(a_c)$ such that $\bullet$ $\mathcal{H}_{\bar{a}}$ is a globally stable minimal surface if $0\leq\bar{a}\leq\bar{a}_c$, and $\bullet$ $\mathcal{H}_{\bar{a}}$ is an unstable minimal surface with index infinity if $\bar{a}>\bar{a}_c$.
Additive posets, CW-complexes, and graphs

Abstract We introduce and study additive posets. We show that the top homology group (with coefficients in Z ∕ 2 Z ) of a finite dimensional CW-complex carries a structure of an additive poset invariant under subdivisions. Applications to CW-complexes and graphs are discussed.
Towards Stability and Optimality in Stochastic Gradient Descent

Iterative procedures for parameter estimation based on stochastic gradient descent (sgd) allow the estimation to scale to massive data sets. However, they typically suer from numerical instability, while estimators based on sgd are statistically inecient as they do not use all the informationinthedataset. Toaddressthesetwoissueswe propose an iterative estimation procedure termed averaged implicit sgd (ai-sgd). For statistical eciency ai-sgd employs averaging of the iterates, which achieves the Cramer-Rao bound under strong convexity, i.e., it is asymptotically an optimal unbiased estimator of the true parameter value. For numerical stability ai-sgd employs animplicitupdateateachiteration, whichissimilar to updates performed by proximal operators in optimization. In practice, ai-sgd achieves competitive performance with state-of-the-art procedures. Furthermore, it is more stable than averaging procedures that do not employ proximal updates, and is simple to implement as it requires fewer tunable hyperparameters than procedures that do employ proximal updates.
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form

Semidefinite programs (SDP) are important in learning and combinatorial optimization with numerous applications. In pursuit of low-rank solutions and low complexity algorithms, we consider the Burer--Monteiro factorization approach for solving SDPs. We show that all approximate local optima are global optima for the penalty formulation of appropriately rank-constrained SDPs as long as the number of constraints scales sub-quadratically with the desired rank of the optimal solution. Our result is based on a simple penalty function formulation of the rank-constrained SDP along with a smoothed analysis to avoid worst-case cost matrices. We particularize our results to two applications, namely, Max-Cut and matrix completion.
Polynomials with prescribed bad primes

We tabulate polynomials in ℚ[t] with a given factorization partition, bad reduction entirely within a given set of primes, and satisfying auxiliary conditions associated to 0, 1, and ∞. We explain how these polynomials are of particular interest because of their role in the construction of nonsolvable number fields of arbitrarily large degree and bounded ramification.
Photovoltaic generation in distribution networks: Optimal vs. random installation

Nowadays common practice in deploying photovoltaic distributed generations (PVDGs) is customer-based installation in the distribution network. Increasing level of PVDG applications and expedite approval by utilities have raised concern about the negative impacts of PVDG installations on the distribution network operations such as reverse power flows and undesirable voltage fluctuations. One potential solutions is to optimize the siting and sizing of these distributed renewable generation resources. This paper presents a comparative study on both optimal and randomized installation of PVDGs with the latter modeling real life customer-based renewable integration. The proposed models examine and compare the impacts of PVDG installation on distribution network operation. Numerical simulations have been performed on a local distribution network model with realistic load profiles, GIS information, local solar insolation, and feeder and voltage settings. It is found that when the distribution system has a medium penetration ratio optimal PVDG installations may introduce essential improvements in terms of voltage deviation and energy loss reduction than randomized installation. However, if the penetration ratio is very low or extremely high there will be not significant difference between the two.
Entropy, stability, and harmonic map flow

Inspired by work of Colding-Minicozzi on mean curvature flow, Zhang introduced a notion of entropy stability for harmonic map flow. We build further upon this work in several directions. First we prove the equivalence of entropy stability with a more computationally tractable $\mathcal F$-stability. Then, focusing on the case of spherical targets, we prove a general instability result for high-entropy solitons. Finally, we exploit results of Lin-Wang to observe long time existence and convergence results for maps into certain convex domains and how they relate to generic singularities of harmonic map flow.
Examples of finite free complexes of small rank and small homology

This work concerns finite free complexes over commutative noetherian rings, in particular over group algebras of elementary abelian groups. The main contribution is the construction of complexes such that the total rank of their underlying free modules, or the total length of their homology, is less than predicted by various conjectures in the theory of transformation groups and in local algebra.
The Role of Conversation Context for Sarcasm Detection in Online Interactions

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker’s sarcastic intent is not always obvious without additional context. Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response. We show that the conditional LSTM network (Rocktaschel et al. 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response. To address the second issue, we present a qualitative analysis of attention weights produced by the LSTM models with attention and discuss the results compared with human performance on the task.
On Yau’s theorem for effective orbifolds

Abstract In 1978 Yau (Yau, 1978) confirmed a conjecture due to Calabi (1954) stating the existence of Kahler metrics with prescribed Ricci forms on compact Kahler manifolds. A version of this statement for effective orbifolds can be found in the literature (Joyce, 2000; Boyer and Galicki, 2008; Demailly and Kollar, 2001). In this expository article, we provide details for a proof of this orbifold version of the statement by adapting Yau’s original continuity method to the setting of effective orbifolds in order to solve a Monge–Ampere equation. We then outline how to obtain Kahler–Einstein metrics on orbifolds with negative first Chern class by solving a slightly different Monge–Ampere equation. We conclude by listing some explicit examples of Calabi–Yau orbifolds, which consequently admit Ricci flat metrics by Yau’s theorem for effective orbifolds.
CP violation in top-quark physics

In this paper I discuss how to search for CP violation in top-quark pair production and decay using T-odd correlations. I discuss two examples which illustrate many of the relevant features: CP violation in a heavy neutral Higgs boson; and CP violating anomalous top-quark couplings. I present some numerical results for the LHC and some for the Tevatron.
The Case for Black Hole Thermodynamics, Part I: Phenomenological Thermodynamics

Abstract I give a fairly systematic and thorough presentation of the case for regarding black holes as thermodynamic systems in the fullest sense, aimed at readers with some familiarity with thermodynamics, quantum mechanics and general relativity but not presuming advanced knowledge of quantum gravity. I pay particular attention to (i) the availability in classical black hole thermodynamics of a well-defined notion of adiabatic intervention; (ii) the power of the membrane paradigm to make black hole thermodynamics precise and to extend it to local-equilibrium contexts; (iii) the central role of Hawking radiation in permitting black holes to be in thermal contact with one another; (iv) the wide range of routes by which Hawking radiation can be derived and its back-reaction on the black hole calculated; (v) the interpretation of Hawking radiation close to the black hole as a gravitationally bound thermal atmosphere. In an appendix I discuss recent criticisms of black hole thermodynamics by Dougherty and Callender. This paper confines its attention to the thermodynamics of black holes; a sequel will consider their statistical mechanics.
How To Place a Point to Maximize Angles.

We describe a randomized algorithm that, given a set of points in the plane, computes the best location to insert a new point, such that the Delaunay triangulation of the resulting point set has the largest possible minimum angle. The expected running time of our algorithm is at most cubic on any input, improving the roughly quartic time of the best previously known algorithm.
Incentive compatible budget elicitation in multi-unit auctions

In this paper, we consider the problem of designing incentive compatible auctions for multiple (homogeneous) units of a good, when bidders have private valuations and private budget constraints. When only the valuations are private and the budgets are public, Dobzinski et al [8] show that the adaptive clinching auction is the unique incentive-compatible auction achieving Pareto-optimality. They further show that this auction is not truthful with private budgets, so that there is no deterministic Pareto-optimal auction with private budgets. Our main contribution is to show the following Budget Monotonicity property of this auction: When there is only one infinitely divisible good, a bidder cannot improve her utility by reporting a budget smaller than the truth. This implies that the adaptive clinching auction is incentive compatible when over-reporting the budget is not possible (for instance, when funds must be shown upfront). We can also make reporting larger budgets suboptimal with a small randomized modification to the auction. In either case, this makes the modified auction Pareto-optimal with private budgets. We also show that the Budget Monotonicity property does not hold for auctioning indivisible units of the good, showing a sharp contrast between the divisible and indivisible cases. The Budget Monotonicity property also implies other improved results in this context. For revenue maximization, the same auction improves the best-known competitive ratio due to Abrams [1] by a factor of 4, and asymptotically approaches the performance of the optimal single-price auction. Finally, we consider the problem of revenue maximization (or social welfare) in a Bayesian setting. We allow the bidders have public size constraints (on the amount of good they are willing to buy) in addition to private budget constraints. We show a simple poly-time computable 5.83-approximation to the optimal Bayesian incentive compatible mechanism, that is implementable in dominant strategies. Our technique again crucially needs the ability to prevent bidders from over-reporting budgets via randomization. We show the approximation result via designing a rounding scheme for an LP relaxation of the problem, which may be of independent interest.
Human Decision-Making under Limited Time

Abstract Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints---i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations. We found that our bounded-rational model accounts well for the data. The decomposition of the fitted model parameter into the subjects' expected utility function and resource parameter provide interesting insight into the subjects' information capacity limits. Our results confirm that humans gradually fall back on their learned prior choice patterns when confronted with increasing resource limitations.
Time-resolved dynamics of the spin Hall effect

When current is passed through certain semiconductors or metals, spins of opposite sign accumulate on opposing boundaries. The phenomenon is known as the spin Hall effect, and now, for the first time, its dynamics has been measured directly. The generation and manipulation of carrier spin polarization in semiconductors solely by electric fields has garnered significant attention as both an interesting manifestation of spin–orbit physics as well as a valuable capability for potential spintronics devices1,2,3,4. One realization of these spin–orbit phenomena, the spin Hall effect5,6, has been studied as a means of all-electrical spin-current generation and spin separation in both semiconductor and metallic systems. Previous measurements of the spin Hall effect7,8,9,10,11 have focused on steady-state generation and time-averaged detection, without directly addressing the accumulation dynamics on the timescale of the spin-coherence time. Here, we demonstrate time-resolved measurement of the dynamics of spin accumulation generated by the extrinsic spin Hall effect in a doped GaAs semiconductor channel. Using electrically pumped time-resolved Kerr rotation, we image the accumulation, precession and decay dynamics near the channel boundary with spatial and temporal resolution and identify multiple evolution time constants. We model these processes with time-dependent diffusion analysis using both exact and numerical solution techniques and find that the underlying physical spin-coherence time differs from the dynamical rates of spin accumulation and decay observed near the sample edges.
Multi-armed Bandit Problems with Strategic Arms

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward $v_a$ and can choose an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All non-pulled arms get reward $0$. Each strategic arm tries to maximize its own utility over the course of $T$ rounds. Our goal is to design an algorithm for the principal incentivizing these arms to pass on as much of their private rewards as possible. When private rewards are stochastically drawn each round ($v_a^t \leftarrow D_a$), we show that: - Algorithms that perform well in the classic adversarial multi-armed bandit setting necessarily perform poorly: For all algorithms that guarantee low regret in an adversarial setting, there exist distributions $D_1,\ldots,D_k$ and an approximate Nash equilibrium for the arms where the principal receives reward $o(T)$. - Still, there exists an algorithm for the principal that induces a game among the arms where each arm has a dominant strategy. When each arm plays its dominant strategy, the principal sees expected reward $\mu'T - o(T)$, where $\mu'$ is the second-largest of the means $\mathbb{E}[D_{a}]$. This algorithm maintains its guarantee if the arms are non-strategic ($x_a = v_a$), and also if there is a mix of strategic and non-strategic arms.
Identifying the neural representation of fast and slow states in force field adaptation via fMRI

Although neurorehabilitation is centered on motor learning and control processes, our understanding of how the brain learns to control movement is still limited. Motor adaptation is an error-driven motor learning process that is amenable to study in the laboratory setting. Behavioral studies of motor adaptation have coupled clever task design with computational modeling to study the control processes that underlie motor adaptation. These studies provide evidence of fast and slow learning states in the brain that combine to control neuromotor adaptation.Currently, the neural representation of these states remains unclear, especially for adaptation to changes in task dynamics, commonly studied using force fields imposed by a robotic device. Our group has developed the MR-SoftWrist, a robot capable of executing dynamic adaptation tasks during functional magnetic resonance imaging (fMRI) that can be used to localize these networks in the brain.We simulated an fMRI experiment to determine if signal arising from a switching force field adaptation task can localize the neural representations of fast and slow learning states in the brain. Our results show that our task produces reliable behavioral estimates of fast and slow learning states, and distinctly measurable fMRI activations associated with each state under realistic levels of behavioral and measurement noise. Execution of this protocol with the MR-SoftWrist will extend our knowledge of how the brain learns to control movement.
Estimating Causal Effects of Tone in Online Debates

Statistical methods applied to social media posts shed light on the dynamics of online dialogue. For example, users' wording choices predict their persuasiveness and users adopt the language patterns of other dialogue participants. In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses. The challenge for this estimation is that a reply's tone and subsequent responses are confounded by the users' ideologies on the debate topic and their emotions. To overcome this challenge, we learn representations of ideology using generative models of text. We study debates from 4Forums and compare annotated tones of replying such as emotional versus factual, or reasonable versus attacking. We show that our latent confounder representation reduces bias in ATE estimation. Our results suggest that factual and asserting tones affect dialogue and provide a methodology for estimating causal effects from text.
Risk Averse Robust Adversarial Reinforcement Learning

Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the training environment and may not avoid rare, catastrophic events such as automotive accidents. A classical technique for improving the robustness of reinforcement learning algorithms is to train on a set of randomized environments, but this approach only guards against common situations. Recently, robust adversarial reinforcement learning (RARL) was developed, which allows efficient applications of random and systematic perturbations by a trained adversary. A limitation of RARL is that only the expected control objective is optimized; there is no explicit modeling or optimization of risk. Thus the agents do not consider the probability of catastrophic events (i.e., those inducing abnormally large negative reward), except through their effect on the expected objective. In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary. We test our approach on a self-driving vehicle controller. We use an ensemble of policy networks to model risk as the variance of value functions. We show through experiments that a risk-averse agent is better equipped to handle a risk-seeking adversary, and experiences substantially fewer crashes compared to agents trained without an adversary. Supplementary materials are available at https://sites.google.com/view/rararl.
False-negative upper extremity ultrasound in the initial evaluation of patients with suspected subclavian vein thrombosis due to thoracic outlet syndrome (Paget-Schroetter syndrome)

Abstract Objective To assess the utilization and consequences of upper extremity Duplex ultrasound in the initial diagnostic evaluation of patients with suspected subclavian vein (SCV) thrombosis and venous thoracic outlet syndrome (VTOS). Methods A retrospective single-center review was conducted for patients that underwent primary surgical treatment for VTOS between 2008 and 2017, in whom an upper extremity ultrasound had been performed as the initial diagnostic test (n = 214). Clinical and treatment characteristics were compared between patients with positive and false-negative ultrasound studies. Results There were 122 men (57%) and 92 women (43%) that had presented with spontaneous idiopathic arm swelling, including 28 (13%) with proven pulmonary embolism, at a mean age of 30.7 ± 0.8 years (range 14-69). Upper extremity ultrasound had been performed 23.8 ± 12.2 days after the onset of symptoms, with confirmation of axillary-SCV thrombosis in 169 patients (79%) and negative results in 45 (21%). Of the false-negative ultrasound study reports, only 8 (18%) acknowledged limitations in visualizing the central SCV. Definitive diagnostic imaging (DDI) had been obtained by upper extremity venography in 175 (82%), computed tomography angiography in 24 (11%), and magnetic resonance angiography in 15 (7%), with 142 (66%) undergoing catheter-directed axillary-SCV thrombolysis. The mean interval between initial ultrasound and DDI was 48.9 ± 14.2 days with no significant difference between groups, but patients with a positive ultrasound were more likely to have DDI within 48 hours than those with a false-negative ultrasound (44% vs 24%; P = .02). At the time of surgical treatment, the SCV was widely patent following paraclavicular decompression and external venolysis alone in 74 patients (35%). Patch angioplasty was performed for focal SCV stenosis in 76 (36%) and bypass graft reconstruction for long-segment axillary-SCV occlusion in 63 (29%). Patients with false-negative initial ultrasound studies were significantly more likely to require SCV bypass reconstruction than those with a positive ultrasound (44% vs 25%; P = .02). Conclusions Duplex ultrasound has significant limitations in the initial evaluation of patients with suspected SCV thrombosis, with false-negative results in 21% of patients with proven VTOS. This is rarely acknowledged in ultrasound reports, but false-negative ultrasound studies have the potential to delay definitive imaging, thrombolysis, and further treatment for VTOS. Initial false-negative ultrasound results are associated with progressive thrombus extension and a more frequent need for SCV bypass reconstruction at the time of surgical treatment.
Learning Bilingual Word Embeddings Using Lexical Definitions.

Bilingual word embeddings, which represent lexicons of different languages in a shared embedding space, are essential for supporting semantic and knowledge transfers in a variety of cross-lingual NLP tasks. Existing approaches to training bilingual word embeddings require either large collections of pre-defined seed lexicons that are expensive to obtain, or parallel sentences that comprise coarse and noisy alignment. In contrast, we propose BiLex that leverages publicly available lexical definitions for bilingual word embedding learning. Without the need of predefined seed lexicons, BiLex comprises a novel word pairing strategy to automatically identify and propagate the precise fine-grain word alignment from lexical definitions. We evaluate BiLex in word-level and sentence-level translation tasks, which seek to find the cross-lingual counterparts of words and sentences respectively. BiLex significantly outperforms previous embedding methods on both tasks.
Galois scaffolds and semistable extensions

Abstract Let K be a local field and let L / K be a totally ramified Galois extension of degree p n . Being semistable [2] and possessing a Galois scaffold [4] are two conditions which facilitate the computation of the additive Galois module structure of L / K . In this note we show that L / K is semistable if and only if L / K has a Galois scaffold. We also give sufficient conditions in terms of Galois scaffolds for the extension L / K to be stable.
Some neglected outcomes of organizational use of computing technology—And their implications for systems designers

Twenty-five years after the first commercial application of computers, it now seems possible to assess their consequences on the basis of user experience. Until recently, most assessment was done on the basis of early predictions inspired by the technological capabilities of computers. But these predictions have missed the mark considerably. Not only did the technology of computing fail to stand still, but it has been demonstrated time and again that just because a capability exists does not mean that it will be used as intended.
Detected Changes in Precipitation Extremes at Their Native Scales Derived from In Situ Measurements

AbstractThe gridding of daily accumulated precipitation—especially extremes—from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates...
Lasting effects of repeated ∆9‐tetrahydrocannabinol vapour inhalation during adolescence in male and female rats

Author(s): Nguyen, Jacques D; Creehan, Kevin M; Kerr, Tony M; Taffe, Michael A | Abstract: Background and purposeAdolescents are regularly exposed to ∆9 -tetrahydrocannabinol (THC) via smoking and, more recently, vaping cannabis extracts. Growing legalization of cannabis for medical and recreational purposes, combined with decreasing perceptions of harm, makes it increasingly important to determine the consequences of frequent adolescent exposure for motivated behaviour and lasting tolerance in response to THC.Experimental approachesMale and female rats inhaled THC vapour, or that from the propylene glycol (PG) vehicle, twice daily for 30nmin from postnatal day (PND) 35-39 and PND 42-46 using an e-cigarette system. Thermoregulatory responses to vapour inhalation were assessed by radio-telemetry during adolescence and from PND 86-94. Chow intake was assessed in adulthood. Blood samples were obtained from additional adolescent groups following initial THC inhalation and after 4ndays of twice daily exposure. Additional groups exposed repeatedly to THC or PG during adolescence were evaluated for intravenous self-administration of oxycodone as adults.Key resultsFemale, not male, adolescents developed tolerance to the hypothermic effects of THC inhalation in the first week of repeated exposure despite similar plasma THC levels. Each sex exhibited tolerance to THC hypothermia in adulthood after repeated adolescent THC. However, enhanced potency was found in females. Repeated THC male rats consumed more food than their PG-treated control group, without significant bodyweight differences. Adolescent THC did not alter oxycodone self-administration in either sex but increased fentanyl self-administration in females.Conclusions and implicationsRepeated THC vapour inhalation in adolescent rats has lasting consequences observable in adulthood.
Patterns of Effort Contribution and Demand and User Classification based on Participation Patterns in NPM Ecosystem

Background: Open source requires participation of volunteer and commercial developers (users) in order to deliver functional high-quality components. Developers both contribute effort in the form of patches and demand effort from the component maintainers to resolve issues reported against it. Open source components depend on each other directly and transitively, and evidence suggests that more effort is required for reporting and resolving the issues reported further upstream in this supply chain. Aim: Identify and characterize patterns of effort contribution and demand throughout the open source supply chain and investigate if and how these patterns vary with developer activity; identify different groups of developers; and predict developers' company affiliation based on their participation patterns. Method: 1,376,946 issues and pull-requests created for 4433 NPM packages with over 10,000 monthly downloads and full (public) commit activity data of the 272,142 issue creators is obtained and analyzed and dependencies on NPM packages are identified. Fuzzy c-means clustering algorithm is used to find the groups among the users based on their effort contribution and demand patterns, and Random Forest is used as the predictive modeling technique to identify their company affiliations. Result: Users contribute and demand effort primarily from packages that they depend on directly with only a tiny fraction of contributions and demand going to transitive dependencies. A significant portion of demand goes into packages outside the users' respective supply chains (constructed based on publicly visible version control data). Three and two different groups of users are observed based on the effort demand and effort contribution patterns respectively. The Random Forest model used for identifying the company affiliation of the users gives a AUC-ROC value of 0.68, and variables representing aggregate participation patterns proved to be the important predictors. Conclusion: Our results give new insights into effort demand and supply at different parts of the supply chain of the NPM ecosystem and its users and suggests the need to increase visibility further upstream.
Neuropathy and Neural Plasticity in the Subcutaneous White Adipose Depot

The difficulty in obtaining as well as maintaining weight loss, together with the impairment of metabolic control in conditions like diabetes and cardiovascular disease, may represent pathological situations of inadequate neural communication between the brain and peripheral organs and tissues. Innervation of adipose tissues by peripheral nerves provides a means of communication between the master metabolic regulator in the brain (chiefly the hypothalamus), and energy-expending and energy-storing cells in the body (primarily adipocytes). Although chemical and surgical denervation studies have clearly demonstrated how crucial adipose tissue neural innervation is for maintaining proper metabolic health, we have uncovered that adipose tissue becomes neuropathic (ie: reduction in neurites) in various conditions of metabolic dysregulation. Here, utilizing both human and mouse adipose tissues, we present evidence of adipose tissue neuropathy, or loss of proper innervation, under pathophysiological conditions such as obesity, diabetes, and aging, all of which are concomitant with insult to the adipose organ as well as metabolic dysfunction. Neuropathy is indicated by loss of nerve fiber protein expression, reduction in synaptic markers, and lower neurotrophic factor expression in adipose tissue. Aging-related adipose neuropathy particularly results in loss of innervation around the tissue vasculature, which cannot be reversed by exercise. Together with indications of neuropathy in muscle and bone, these findings underscore that peripheral neuropathy is not restricted to classic tissues like the skin of distal extremities, and that loss of innervation to adipose may trigger or exacerbate metabolic diseases. In addition, we have demonstrated stimulation of adipose tissue neural plasticity with cold exposure, which may ameliorate adipose neuropathy and be a potential therapeutic option to re-innervate adipose and restore metabolic health.
Partial Compilation of Variational Algorithms for Noisy Intermediate-Scale Quantum Machines

Quantum computing is on the cusp of reality with Noisy Intermediate-Scale Quantum (NISQ) machines currently under development and testing. Some of the most promising algorithms for these machines are variational algorithms that employ classical optimization coupled with quantum hardware to evaluate the quality of each candidate solution. Recent work used GRadient Descent Pulse Engineering (GRAPE) to translate quantum programs into highly optimized machine control pulses, resulting in a significant reduction in the execution time of programs. This is critical, as quantum machines can barely support the execution of short programs before failing. However, GRAPE suffers from high compilation latency, which is untenable in variational algorithms since compilation is interleaved with computation. We propose two strategies for partial compilation, exploiting the structure of variational circuits to pre-compile optimal pulses for specific blocks of gates. Our results indicate significant pulse speedups ranging from 1.5x-3x in typical benchmarks, with only a small fraction of the compilation latency of GRAPE.
The Taste for Status in International Comparison

This paper provides the first comparative analysis of the preference for social status across countries. We develop and provide support for two hypotheses: the cultural foundations hypothesis, which claims that individuals’ preferences for status are rooted in persistent cultural values, and the standard of living hypothesis, which states that in more developed economies, relative income matters more and absolute income matters less to individual utility. To investigate these hypotheses, we propose a theoretically grounded measure of the taste for status, the marginal rate of substitution of relative for absolute income. We find empirical support both of these hypotheses. Specifically, we find that the taste for status is positively associated with individualism, egalitarianism and per capita income. We also identify and provide evidence regarding two threshold values of the taste for status, which are associated with the onset of status preferences and with the emergence of an hedonic treadmill. Our estimates indicate that most countries fall between these two thresholds, and thus experience a positive taste for social status. Only the poorest and most hierarchical and collectivist countries fall below the threshold for a taste for status, and only the richest and most individualist and egalitarian countries experience an hedonic treadmill.
IKAP-Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis.

Background In single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant. Findings To accelerate this process, we have developed IKAP-an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology. Conclusions By tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.
Dissection of central clock function in Drosophila through cell-specific CRISPR-mediated clock gene disruption

In Drosophila, ~150 neurons expressing molecular clock proteins regulate circadian behavior. Sixteen of these neurons secrete the neuropeptide Pdf and have been called 'master pacemakers' because they are essential for circadian rhythms. A subset of Pdf+ neurons (the morning oscillator) regulates morning activity and communicates with other non-Pdf+ neurons, including a subset called the evening oscillator. It has been assumed that the molecular clock in Pdf+ neurons is required for these functions. To test this, we developed and validated Gal4-UAS based CRISPR tools for cell-specific disruption of key molecular clock components, period and timeless. While loss of the molecular clock in both the morning and evening oscillators eliminates circadian locomotor activity, the molecular clock in either oscillator alone is sufficient to rescue circadian locomotor activity in the absence of the other. This suggests that clock neurons do not act in a hierarchy but as a distributed network to regulate circadian activity.