cssi/SciSciGPT-SciSciNet huggingface dataset:
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates

How should a robot that collaborates with multiple people decide upon the distribution of resources (e.g. social attention, or parts needed for an assembly)? People are uniquely attuned to how resources are distributed. A decision to distribute more resources to one team member than another might be perceived as unfair with potentially detrimental effects for trust. We introduce a multi-armed bandit algorithm with fairness constraints, where a robot distributes resources to human teammates of different skill levels. In this problem, the robot does not know the skill level of each human teammate, but learns it by observing their performance over time. We define fairness as a constraint on the minimum rate that each human teammate is selected throughout the task. We provide theoretical guarantees on performance and perform a large-scale user study, where we adjust the level of fairness in our algorithm. Results show that fairness in resource distribution has a significant effect on users’ trust in the system. ACM Reference Format: Houston Claure, Yifang Chen, Jignesh Modi, Malte Jung, and Stefanos Nikolaidis. 2020. Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates. In Proceedings of the 2020ACM/IEEE International Conference on Human-Robot Interaction (HRI’20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374806
Gene discoveries in autism are biased towards comorbidity with intellectual disability.

Background Autism typically presents with highly heterogeneous features, including frequent comorbidity with intellectual disability (ID). The overlap between these phenotypes has confounded the diagnosis and discovery of genetic factors associated with autism. We analysed pathogenic de novo genetic variants in individuals with autism who had either ID or normal cognitive function to determine whether genes associated with autism also contribute towards ID comorbidity. Methods We analysed 2290 individuals from the Simons Simplex Collection for de novo likely gene-disruptive (LGD) variants and copy-number variants (CNVs), and determined their relevance towards IQ and Social Responsiveness Scale (SRS) measures. Results Individuals who carried de novo variants in a set of 173 autism-associated genes showed an average 12.8-point decrease in IQ scores (p=5.49×10−6) and 2.8-point increase in SRS scores (p=0.013) compared with individuals without such variants. Furthermore, individuals with high-functioning autism (IQ >100) had lower frequencies of de novo LGD variants (42 of 397 vs 86 of 562, p=0.021) and CNVs (9 of 397 vs 24 of 562, p=0.065) compared with individuals who manifested both autism and ID (IQ Conclusions Pathogenic de novo variants disrupting autism-associated genes contribute towards autism and ID comorbidity, while other genetic factors are likely to be causal for high-functioning autism.
On topological complexity of hyperbolic groups

We show that the topological complexity of a finitely generated torsion free hyperbolic group $\pi$ with $\cd\pi=n$ equals $2n$.
The Relationship of Special Education Teacher Performance on Observation Instruments With Student Outcomes.

In this study, we examined the relationship of special education teachers’ performance on the Recognizing Effective Special Education Teachers (RESET) Explicit Instruction observation protocol with...
Divisor sums representable as the sum of two squares

Let $s(n)$ denote the sum of the proper divisors of the natural number $n$. We show that the number of $n \leq x$ such that $s(n)$ is a sum of two squares has order of magnitude $x/\sqrt{\log x}$, which agrees with the count of $n \leq x$ which are a sum of two squares. Our result confirms a special case of a conjecture of Erd{\H o}s, Granville, Pomerance and Spiro, who in a 1990 paper asserted that if $\mathcal{A} \subset \mathbb{N}$ has asymptotic density zero (e.g. if $\mathcal{A}$ is the set of $n \leq x$ which are a sum of two squares), then $s^{-1}(\mathcal{A})$ also has asymptotic density zero.
Impact of New UNOS Allocation Criteria on Transplant Practices and Outcomes

Purpose In October 2018, a new heart allocation policy was implemented. The new system was intended to decrease time on the wait list and prioritize allocation to the sickest patients. We examined the effects of this new policy on heart transplant recipients who underwent transplant one year prior to and one year after the policy change. Methods Adult patients who underwent heart transplant at our institution from October 2017 to September 2019 were identified and divided into two cohorts based on whether the date of transplant was before or after October 2018. Patient demographics, clinical data, and bridging strategy were recorded. Early outcomes studied included ischemic time, presence of severe PGD, need for renal replacement therapy or tracheostomy, duration of ICU stay, and total hospital stay. Results In total, 66 patients were identified. 38 patients were transplanted the year prior to the allocation change and 28 patients the year after. The most common listing status prior to the change was Status 1B (74%) and after was Status 2 (54%). After the allocation change, there was a decrease in average wait-time to transplant (313 days vs. 53 days, p=.04). In addition, patients were more likely to be inpatient prior to their transplant (11% vs. 68%, p Conclusion Implementation of the new allocation system resulted in dramatic changes in the bridging strategy utilized at our institution. The number of patients supported with durable LVADs prior to transplant was substantially decreased and the use of temporary mechanical support increased following the change. Early post-transplant outcomes appear similar. Further investigation is needed to assess the long-term impact of the allocation change.
PNS modules for the synthesis of parallel self-organizing hierarchical neural networks

The PNS module is discussed as the building block for the synthesis of parallel, self-organizing, hierarchical, neural networks (PSHNNs). The PNS module contains three submodules (units), the first two of which are created as simple neural network constructs and the last of which is a statistical unit. The first two units are fractile in nature, meaning that each such unit may itself consist of a number of parallel PNS modules in a fractile fashion. Through a mechanism of statistical acceptance or rejection of input vectors for classification, the sample space is divided into a number of regions. The input vectors belonging to each region are classified by a dedicated set of PNS modules. This strategy results in considerably higher accuracy of classification and better generalization as compared to previous neural network models. If the delta rule network is used to generate the first two units, each region approximates a linearly separable region. In this sense, the total system becomes similar to a piecewise linear model. The various regions are determined nonlinearly by the first and third units of the PNS modules.
Regional complexity analysis of algorithms for nonconvex smooth optimization

A strategy is proposed for characterizing the worst-case performance of algorithms for solving nonconvex smooth optimization problems. Contemporary analyses characterize worst-case performance by providing, under certain assumptions on an objective function, an upper bound on the number of iterations (or function or derivative evaluations) required until a pth-order stationarity condition is approximately satisfied. This arguably leads to conservative characterizations based on certain objectives rather than on ones that are typically encountered in practice. By contrast, the strategy proposed in this paper characterizes worst-case performance separately over regions comprising a search space. These regions are defined generically based on properties of derivative values. In this manner, one can analyze the worst-case performance of an algorithm independently from any particular class of objectives. Then, once given a class of objectives, one can obtain a tailored complexity analysis merely by delineating the types of regions that comprise the search spaces for functions in the class. Regions defined by first- and second-order derivatives are discussed in detail and example complexity analyses are provided for a few standard first- and second-order algorithms when employed to minimize convex and nonconvex objectives of interest. It is also explained how the strategy can be generalized to regions defined by higher-order derivatives and for analyzing the behavior of higher-order algorithms.
Impatience and Time Inconsistency in Discounting Models

Extant theories of intertemporal choice entangle two aspects of time preference: impatience and time inconsistency. Impatient people focus on present consumption without worrying too much about the...
Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

Loss minimization in distribution networks (DN) is of great significance since the trend to the distributed generation (DG) requires the most efficient operating scenario possible for economic viability variations. Moreover, voltage instability in DNs is a critical phenomenon and can lead to a major blackout in the system. The decreasing voltage stability level restricts the increase of load served by distribution companies. DG can be used to improve DN capabilities and brings new opportunities to traditional DNs. However, installation of DG in non-optimal places can result in an increase in system losses, voltage problems, etc. In this paper, genetic algorithm (GA), harmony search algorithm (HSA) and improved HSA have been applied to determine the optimal location of DGs. Simulation results for an IEEE 33 bus network are compared for different algorithms, and the best algorithm is stated for minimum losses.
Impact of economic policy uncertainty on disclosure and pricing of earnings news

In this paper we examine the effect of economic policy uncertainty on reactions of capital market participants and actions of corporate managers. In particular, we investigate two issues: (1) whether the economic policy uncertainty has a short-term and long-term impact on the interpretation of earnings news and (2) whether the managers strategically time the release of good/bad news to the uncertainty in economic policies. We use a sample of 2973 unique firms from 1990 to 2016 and find that economic policy uncertainty lowers investors’ confidence in earnings, resulting in lower earnings response coefficient. Further test shows that the adverse effect of economic policy uncertainty on earnings informativeness is long-lasting and cannot be explained by the publicly available pre-disclosure information. We also find that firms are more likely to disclose bad news and losses when the economic policy uncertainty is high. These findings are not explained by earnings management and persist even after controlling for commonly used proxies of earnings management. Finally, we show that economic policy uncertainty is distinct from firm-specific uncertainty and that they both have incremental effects on the earnings response coefficient.
Spiral structure and star formation. I. Formation mechanisms and mean free paths

The wide variety of optical appearances observed in spiral galaxies has encouraged the growth of two theoretical approaches to explain the spiral patterns exhibited by such young objects as OB associations, giant H II regions, and dark dust lanes. These approaches are related to the density wave theory of spiral structure and 'stochastic, self-propagating star formation'. Levinson and Roberts (1981) tried to reconcile these two theoretical approaches, and considered a disk filled with discrete gas clouds. The present investigation is concerned with refinements and extensions of the model of Levinson and Roberts. Attention is given to gravitational forces and dynamical propagation, cloud-cloud collisions, supernova interactions, computational models, a theory concerning the interactions of the gas cloud and stellar association systems, the time evolution of the gas cloud-stellar association systems, and aspects of collisionally triggered star formation.
Invariants of polynomials mod Frobenius powers

Abstract Lewis, Reiner, and Stanton conjectured a Hilbert series for a space of invariants under an action of finite general linear groups using ( q , t ) -binomial coefficients. This work gives an analog in positive characteristic of theorems relating various Catalan numbers to the representation theory of rational Cherednik algebras. They consider a finite general linear group as a reflection group acting on the quotient of a polynomial ring by iterated powers of the irrelevant ideal under the Frobenius map. We prove a variant of their conjecture in the local case, when the group acting fixes a reflecting hyperplane.
Modeling microbial abundances and dysbiosis with beta-binomial regression

Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem. In the context of microbiome studies, this problem arises when researchers wish to use a sample from a population of microbes to estimate the population proportion of a particular taxon, known as the taxon's relative abundance. In this paper, we propose a beta-binomial model for this task. Like existing models, our model allows for a taxon's relative abundance to be associated with covariates of interest. However, unlike existing models, our proposal also allows for the overdispersion in the taxon's counts to be associated with covariates of interest. We exploit this model in order to propose tests not only for differential relative abundance, but also for differential variability. The latter is particularly valuable in light of speculation that dysbiosis, the perturbation from a normal microbiome that can occur in certain disease conditions, may manifest as a loss of stability, or increase in variability, of the counts associated with each taxon. We demonstrate the performance of our proposed model using a simulation study and an application to soil microbial data.
Balance of Power: The Governance Structure of International Branch Campus in China

Overseas branch campus is an important component of the internationalization of higher education. This research studies three branch campuses in China—NYU-Shanghai, Duke Kunshan and Wenzhou-Kean University, analyzing their senior leaderships and the power of the home university and the local partners. Though this research is still in process, it would certainly contribute to our understanding of the internationalization of higher education in China and help institutions to choose appropriate strategies.
Wage Differentials, Bargaining Protocols, and Trade Unionism in Mid-Twentieth Century American Labor Markets

Income inequality in the United States has been lower in periods when trade unionism has been strong. Using observations on wages by occupation, by geography, and by gender in collective bargaining...
Doping studies of n-type CsBi4Te6 thermoelectric materials

We have previously reported the successful p-type doping of CsBi4Te6 which had a high figure of merit at temperatures below 300 K. In this study, several dopants were explored to make n-type CsBi4Te6. A program of measurements was performed to identify the optimum doping concentration for several series of dopants. The highest power factors occurred around 125 K for the 0.5% Sn doped CsBi4Te6 sample which had a power factor of 21.9 μW/cm•K2 and 1.0% Te doped CsBi4Te6 which had a power factor of 21.7 μW/cm•K2.
Gains through selection for grain yield in a winter wheat breeding program.

Increased genetic gain for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), marker-based (MS), genomic (GS), and a combination of PS and GS (PS+GS). Ten populations of Washington State University (WSU) winter wheat breeding lines including a diversity panel and F5 and double haploid lines evaluated from 2015 to 2019 growing seasons for grain yield in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield “enhancing” alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual selection strategies (MS and GS) showed the highest mean response (R = 0.61) within the same environment. When combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS within the same environment. Validating selection strategies in different environments resulted in low to negative R values indicating the effects of genotype-by-environment interactions for grain yield. MS was not successful in terms of R relative to the other selection approaches; using this strategy resulted in a significant (P < 0.05) decrease in response to selection compared with the other approaches. An integrated PS+GS approach could result in optimal genetic gain within the same environment, whereas a PS strategy might be a viable option for grain yield validated in different environments. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.
CHAPTER 10 – Space

This chapter provides two event calculus axiomatizations of space: relational space and metric space. As many instances of commonsense reasoning involve space. Here it describes how these axiomatizations can be used to solve some sample problems. A closely related issue that one considers is object identity: Two objects observed at different times and locations in space may or may not be the same object. In the commonsense world, objects stand in various spatial relations to other objects. Furthermore, the commonsense world involves objects located at specific points in a metric space and objects that trace specific paths over time in that space. For example, when an apple located eight feet above the ground starts to fall, it follows a particular path toward the ground. The chapter presents a representation of metric space in the event calculus.
Virtual Critical Coupling

Electromagnetic resonators are a versatile platform to harvest, filter, and trap electromagnetic energy, at the basis of many applications from microwaves to optics. Resonators with a large intrins...
3 – Resource Acquisition

This chapter addresses the physiological and behavioral mechanisms for finding and exploiting resources. Insects, as do all organisms, must acquire energy and material resources to synthesize the organic molecules necessary for life processes of maintenance, growth, and reproduction. Dietary requirements reflect the size and the life stage of the insect and the quality of the food resources. Insects exhibit a variety of physiological and behavioral strategies for finding, evaluating, and exploiting potential resources. The defensive chemistry of plants and insects affects their quality as food, and is a basis for host choice by herbivorous and entomophagous insects respectively. The nutritional value of resources varies among host species, among tissues of a single organism, and even within tissues of a particular type. Chemicals also communicate the availability of food and provide powerful cues that influence insect foraging behavior. Insects are capable of detecting food resources over considerable distances. The perception of chemical cues that indicate availability of hosts is influenced by concentration gradients in air or water, environmental factors that affect downwind or downstream dispersion of the chemical, and sensitivity to particular odors. The efficiency of resource acquisition may improve over time as a result of learning. Although much of insect behavior may be innate, learning has been documented for many insects.
Comparison of overall survival in gallbladder carcinoma at academic versus community cancer centers: An analysis of the National Cancer Data Base

Background and objectives Gallbladder carcinoma (GBC) has a poor prognosis. Studies demonstrated that teaching facilities may provide a lower risk of mortality in patients undergoing pancreatic and colon resection vs nonteaching facilities. We hypothesized that survival rates are higher in academic cancer centers (ACCs) vs community cancer centers (CCCs). Methods Patients with all stages of GBC were identified from the National Cancer Database (2007-2012). Propensity score matching adjusted for selection bias. Descriptive statistics were calculated for all variables. Overall survival (OS) was compared by facility type (ACC vs CCC) and case volume (low vs high) via multivariable Cox proportional hazards regression. Results A total of 7967 patients met the inclusion criteria. Following propensity matching, 2801 patients were analyzed from each facility type. Median OS following surgery was higher for ACC (20.99 months, 95% confidence interval [CI], 19.61-22.64, P = .002) than CCC (17.68 months, 95% CI, 16.46-19.25). Following Cox modeling, GBC treatment at ACCs was a protective factor for OS (adjusted hazard ratio 0.876, 95% CI, 0.801-0.958, P = .004). Discussion GBC treatment at ACCs is an independent predictor of OS. High volume ACCs are associated with improved OS compared with low volume ACCs. The site of care and case volume in ACCs may contribute to improved survival outcomes.
AL: An Artificial Language

This paper presents the original design specifications for an interactive interpretive language. The initial stimulus for designing an extended language was developed from experiences gained in designing and implementing the Pitt Interpretive Language (PIL). The language gains power over many other algebraic languages by allowing the use of five classes of data.
Leveraging elastic instabilities for amplified performance: Spine-inspired high-speed and high-force soft robots

Soft machines typically exhibit slow locomotion speed and low manipulation strength because of intrinsic limitations of soft materials. Here, we present a generic design principle that harnesses mechanical instability for a variety of spine-inspired fast and strong soft machines. Unlike most current soft robots that are designed as inherently and unimodally stable, our design leverages tunable snap-through bistability to fully explore the ability of soft robots to rapidly store and release energy within tens of milliseconds. We demonstrate this generic design principle with three high-performance soft machines: High-speed cheetah-like galloping crawlers with locomotion speeds of 2.68 body length/s, high-speed underwater swimmers (0.78 body length/s), and tunable low-to-high-force soft grippers with over 1 to 103 stiffness modulation (maximum load capacity is 11.4 kg). Our study establishes a new generic design paradigm of next-generation high-performance soft robots that are applicable for multifunctionality, different actuation methods, and materials at multiscales.
Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning

We consider distributed gradient descent in the presence of stragglers. Recent work on gradient coding and approximate gradient coding have shown how to add redundancy in distributed gradient descent to guarantee convergence even if some workers are stragglers —that is, slow or non-responsive. In this work we propose an approximate gradient coding scheme called Stochastic Gradient Coding (SGC), which works when the stragglers are random. SGC distributes data points redundantly to workers according to a pair-wise balanced design, and then simply ignores the stragglers. We prove that the convergence rate of SGC mirrors that of batched Stochastic Gradient Descent (SGD) for the $\ell _{2}$ loss function, and show how the convergence rate can improve with the redundancy. We also provide bounds for more general convex loss functions. We show empirically that SGC requires a small amount of redundancy to handle a large number of stragglers and that it can outperform existing approximate gradient codes when the number of stragglers is large.
Detection of individual normal modes of oscillation of the Sun in the period range from 2 hr to 10 min in solar diameter studies

New observations of solar oscillations are reported. Power density spectra derived from these observations reveal narrow-band oscillations that are spatially global, have spatial symmetry properties that are either symmetric or antisymmetric for reflection about the center of the solar disk and also about the solar equator, and have coherence times ≳ 41 days. Large-scale differential refraction effects have been reduced by a factor of 105 over that found in previous solar diameter studies by the design of the experiment; thus, these effects are eliminated as a possible source of the oscillations. A discussion is presented of this reduction as well as other features of the observing and analysis program. It is concluded that the probability is very high that individual normal modes of oscillation of the Sun have been detected in the period range from 2 hr to 10 min.
Enhanced diffusivity in perturbed senile reinforced random walk models

We consider diffusivity of random walks with transition probabilities depending on the number of consecutive traversals of the last traversed edge, the so called senile reinforced random walk (SeRW). In one dimension, the walk is known to be sub-diffusive with identity reinforcement function. We perturb the model by introducing a small probability $\delta$ of escaping the last traversed edge at each step. The perturbed SeRW model is diffusive for any $\delta >0 $, with enhanced diffusivity ($\gg O(\delta^2)$) in the small $\delta$ regime. We further study stochastically perturbed SeRW models by having the last edge escape probability of the form $\delta\, \xi_n$ with $\xi_n$'s being independent random variables. Enhanced diffusivity in such models are logarithmically close to the so called residual diffusivity (positive in the zero $\delta$ limit), with diffusivity between $O\left(\frac{1}{|\log\delta |}\right)$ and $O\left(\frac{1}{\log|\log\delta|}\right)$. Finally, we generalize our results to higher dimensions where the unperturbed model is already diffusive. The enhanced diffusivity can be as much as $O(\log^{-2}\delta)$.
The onset of electron-only reconnection

Motivated by recent observations of ‘electron-only’ magnetic reconnection, without an ion-scale sheet or ion outflows, in both the Earth’s magnetosheath and in numerical simulations, we study the formation and reconnection of electron-scale current sheets at low plasma , ion-to-electron temperature ratio, the aspect ratio, electron outflow velocity and the final thickness of the sheets, and thus determine under what circumstances electron-scale sheets form and reconnect.
Planning and Resilient Execution of Policies For Manipulation in Contact with Actuation Uncertainty

We propose a method for planning motion for robots with actuation uncertainty that incorporates contact with the environment and the compliance of the robot to reliably perform manipulation tasks. Our approach consists of two stages: (1) Generating partial policies using a sampling-based motion planner that uses particle-based models of uncertainty and simulation of contact and compliance; and (2) Resilient execution that updates the planned policies to account for unexpected behavior in execution which may arise from model or environment inaccuracy. We have tested our planner and policy execution in simulated SE(2) and SE(3) environments and Baxter robot. We show that our methods efficiently generate policies to perform manipulation tasks involving significant contact and compare against several simpler methods. Additionally, we show that our policy adaptation is resilient to significant changes during execution; e.g. adding a new obstacle to the environment.
Map-Matching Using Shortest Paths

We consider several variants of the map-matching problem, which seeks to find a path Q in graph G that has the smallest distance to a given trajectory P (which is likely not to be exactly on the graph). In a typical application setting, P models a noisy GPS trajectory from a person traveling on a road network, and the desired path Q should ideally correspond to the actual path in G that the person has traveled. Existing map-matching algorithms in the literature consider all possible paths in G as potential candidates for Q. We find solutions to the map-matching problem under different settings. In particular, we restrict the set of paths to shortest paths, or concatenations of shortest paths, in G. As a distance measure, we use the Frechet distance, which is a suitable distance measure for curves since it takes the continuity of the curves into account.
Effects of g-jitter on a thermally buoyant flow

The effects of g-jitter on a buoyant flow in a cylindrical enclosure is examined via a finite-difference method. The enclosure is heated nonuniformly from above and a periodic gravitational acceleration is imposed on the flow inside to simulate gravity jittering. It is found that the flow field responding to the imposed gravitational acceleration also exhibits periodic behavior and that the heat transfer rate to the fluid is increased compared to the case without g-jitter. The modulations on the three gravity components all contribute to the increase in heat transfer rate but in ways different in nature. The flow field, sometimes, cannot remain quasi-steady with the imposed gravity changes and hot, isolated packets of fluid are created away from the heat source. It is also found that reducing the total gravity level is more efficient than eliminating the gravity jittering itself in averting the effects of g-jitter after a system has been disturbed.
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation

Q-learning with neural network function approximation (neural Q-learning for short) is among the most prevalent deep reinforcement learning algorithms. Despite its empirical success, the non-asymptotic convergence rate of neural Q-learning remains virtually unknown. In this paper, we present a finite-time analysis of a neural Q-learning algorithm, where the data are generated from a Markov decision process and the action-value function is approximated by a deep ReLU neural network. We prove that neural Q-learning finds the optimal policy with $O(1/\sqrt{T})$ convergence rate if the neural function approximator is sufficiently overparameterized, where $T$ is the number of iterations. To our best knowledge, our result is the first finite-time analysis of neural Q-learning under non-i.i.d. data assumption.
Tibial forces are more useful than varus-valgus laxities for identifying and correcting overstuffing in kinematically aligned total knee arthroplasty.

Identifying and correcting varus-valgus (V-V) malalignment of the tibial component is important when balancing a kinematically aligned total knee arthroplasty (TKA). Accordingly, the primary objective was to determine whether the tibial forces or V-V laxities are more sensitive to, and thus more useful for identifying and correcting, V-V malalignments of the tibial component that overstuff a compartment. Calipered kinematically aligned TKA was performed on nine human cadaveric knees. Medial and lateral tibial forces and V-V laxities were measured from 0° to 120° flexion with an unmodified reference tibial component and modified tibial components that introduced ±1° and ±2° V-V malalignments from the reference component to overstuff either the medial or lateral compartment. Changes in the tibial forces were most sensitive to V-V malalignments at 0° flexion (medial = 118 ± 34 N/deg valgus malalignment and lateral = 79 ± 20 N/deg varus malalignment). The varus and valgus laxities were most sensitive to V-V malalignments at 30° flexion (-0.6 ± 0.1 deg/deg varus malalignment) and 120° flexion (-0.4 ± 0.2 deg/deg valgus malalignment), respectively. The maximum average signal-to-noise ratios of the sensitivities in tibial forces and V-V laxities (ie, signals) to reported measurement errors using current intraoperative technologies (14 N and 0.7°) (ie, noise) were 8.4 deg-1 and 0.9 deg-1 , respectively. Because of the greater signal-to-noise ratios, measuring tibial forces is more useful than measuring V-V laxities for identifying and correcting V-V malalignments of the tibial component that overstuff a compartment. Clinical Significance: The sensitivities of tibial forces provide objective guidance to surgeons performing V-V recuts of the tibia.
On the indeterminacy of Milnor’s triple linking number

In the 1950s Milnor defined a family of higher-order invariants generalizing the linking number. Even the first of these new invariants, the triple linking number, has received fruitful study since...
Alleviating Bottlenecks for DNN Execution on GPUs via Opportunistic Computing

Edge computing and IoT applications are severely constrained by limited hardware resource. This makes memory-consuming DNN (Deep Neural Network) frameworks not applicable to edge computing. Simple algorithms such as direct convolution are finding their way in embedded machine learning. As one of the most widely used platforms for DNN acceleration, GPUs face the bottleneck of on-chip bandwidth. This work introduces a GPU DNN execution architecture that can relieve the on-chip bandwidth bottleneck by reducing data movement through opportunistic computing. We first investigate data access patterns in the hardware's view. Then we propose two opportunistic computing techniques to predictably perform computation when data is available with the help of assistant warps. By moving computation to data, our techniques are able to significantly reduce data movement and relieve the DNN execution bottleneck. Our evaluation results show that the proposed technique can improve DNN application performance as much as 55%.
A Systematic Review of Mutations Associated with Isoniazid Resistance Points to Lower Diagnostic Sensitivity for Common Mutations and Increased Incidence of Uncommon Mutations in Clinical Strains of Mycobacterium tuberculosis

Molecular testing is rapidly becoming integral to the global tuberculosis (TB) control effort. Uncommon mechanisms of resistance can escape detection by these platforms and lead to the development of Multi-Drug Resistant (MDR) strains. This article is a systematic review of published articles that reported isoniazid (INH) resistance-conferring mutations between September-2013 and December-2019. The aims were to catalogue mutations associated with INH resistance, estimate their global prevalence and co-occurrence, and their utility in molecular diagnostics. The genes commonly associated with INH resistance, katG, inhA, fabG1, and the intergenic region oxyR-ahpC were considered in this review. In total, 52 articles were included describing 5,632 INHR clinical isolates from 31 countries. The three most frequently mutated loci continue to be katG315 (4,100), inhA-15 (786), and inhA-8 (105). However, the diagnostic value of inhA-8 is far lower than previously thought, only appearing in 25 (0.4%) INHR isolates that lacked a mutation at the first two loci. Importantly, of the four katG loci recommended by the previous systematic review for diagnostics, only katG315 was observed in our INHR isolates. This indicates continued evolution and regional differences in INH resistance. We have identified 58 loci (common to both systematic reviews) in three genomic regions as a reliable basis for molecular diagnostics. We also catalogue mutations at 49 new loci associated with INH resistance. Including all observed mutations provides a cumulative sensitivity of 85.1%. The most disconcerting is the remaining 14.9% of isolates that harbor an unknown mechanism of resistance, will escape molecular detection, and likely convert to MDR-TB, further complicating treatment. Integrating the information cataloged in this and other similar studies into current diagnostic tools is essential for combating the emergence of MDR-TB. Exclusion of this information will lead to an unnatural selection which will result in eradication of the common but propagation of the uncommon mechanisms of resistance, leading to ineffective global treatment policy and a need for region-specific regiments. Finally, the observance of many low-frequency resistance-conferring mutations point to an advantage of platforms that consider regions rather than specific loci for detection of resistance.
Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge

Deep Learning (DL) model-based AI services are increasingly offered in a variety of predictive analytics services such as computer vision, natural language processing, speech recognition. However, the quality of the DL models can degrade over time due to changes in the input data distribution, thereby requiring periodic model updates. Although cloud data-centers can meet the computational requirements of the resource-intensive and time-consuming model update task, transferring data from the edge devices to the cloud incurs a significant cost in terms of network bandwidth and are prone to data privacy issues. With the advent of GPU-enabled edge devices, the DL model update can be performed at the edge in a distributed manner using multiple connected edge devices. However, efficiently utilizing the edge resources for the model update is a hard problem due to the heterogeneity among the edge devices and the resource interference caused by the colocation of the DL model update task with latency-critical tasks running in the background. To overcome these challenges, we present Deep-Edge, a load- and interference-aware, fault-tolerant resource management framework for performing model update at the edge that uses distributed training. This paper makes the following contributions. First, it provides a unified framework for monitoring, profiling, and deploying the DL model update tasks on heterogeneous edge devices. Second, it presents a scheduler that reduces the total re-training time by appropriately selecting the edge devices and distributing data among them such that no latency-critical applications experience deadline violations. Finally, we present empirical results to validate the efficacy of the framework using a real-world DL model update case-study based on the Caltech dataset and an edge AI cluster testbed.
Bounds on Scott ranks of some polish metric spaces

If &#x1d4a9; is a proper Polish metric space and ℳ is any countable dense submetric space of &#x1d4a9;, then the Scott rank of &#x1d4a9; in the natural first-order language of metric spaces is countable and in fact at mos...
Data-Driven Reachable Set Computation using Adaptive Gaussian Process Classification and Monte Carlo Methods.

We present two data-driven methods for estimating reachable sets with probabilistic guarantees. Both methods make use of a probabilistic formulation allowing for a formal definition of a data-driven reachable set approximation that is correct in a probabilistic sense. The first method recasts the reachability problem as a binary classification problem, using a Gaussian process classifier to represent the reachable set. The quantified uncertainty of the Gaussian process model allows for an adaptive approach to the selection of new sample points. The second method uses a Monte Carlo sampling approach to compute an interval-based approximation of the reachable set. This method comes with a guarantee of probabilistic correctness, and an explicit bound on the number of sample points needed to achieve a desired accuracy and confidence. Each method is illustrated with a numerical example.
Escaping Locally Optimal Decentralized Control Polices via Damping.

We study the design of an optimal static decentralized controller with a quadratic cost and propose a variant of the homotopy continuation method using a damping technique. This method generates a series of optimal distributed control (ODC) problems via a continuous variation of the system parameters. Diverging from the classical theme of tracking a specific trajectory of locally optimal controllers for these ODC problems, we focus on the possibility of leveraging local-search algorithms to locate the globally optimal trajectory among several locally optimal controller trajectories. We analyze the continuity and asymptotic properties of the locally and globally optimal controller trajectories as the damping parameter varies. In particular, we prove that under certain conditions, there is no spurious locally optimal controller for an ODC problem with favorable control structure and a large damping parameter. As a result, the proposed method is able to locate the globally optimal trajectory with a suitable discretization in the space of the damping parameter. To demonstrate the effectiveness of this technique, it is shown that even for instances with an exponential number of connected components, damping could merge the trajectories of all local solutions to the trajectory of the global solutions. We further illustrate the convoluted behavior of the locally optimal trajectories with numerical examples on random systems.
Anomalies and Bosonization

Recently, general methods of bosonization beyond 1+1 dimensions have been developed. In this article, we review these bosonizations and extend them to systems with boundary. Of particular interest is the case when the bulk theory is a G-symmetry protected topological phase and the boundary theory has a G ’t Hooft anomaly. We discuss how, when the anomaly is not realizable in a bosonic system, the G symmetry algebra becomes modified in the bosonization of the anomalous theory. This gives us a useful tool for understanding anomalies of fermionic systems, since there is no way to compute a boundary gauge variation of the anomaly polynomial, as one does for anomalies of bosonic systems. We take the chiral anomalies in 1+1D as case studies and comment on our expectations for parity/time reversal anomalies in 2+1D. We also provide a derivation of new constraints in SPT phases with domain defects decorated by $$p+ip$$ superconductors and Kitaev strings, which is necessary to understand the bosonized symmetry algebras which appear.
WHY WE FEAR GENETIC INFORMANTS: USING GENETIC GENEALOGY TO CATCH SERIAL KILLERS.

Consumer genetics has exploded, driven by the second-most popular hobby in the United States: genealogy. This hobby has been co-opted by law enforcement to solve cold cases, by linking crime-scene DNA with the DNA of a suspect's relative, which is contained in a direct-to-consumer (DTC) genetic database. The relative’s genetic data acts as a silent witness, or genetic informant, wordlessly guiding law enforcement to a handful of potential suspects. At least thirty murderers and rapists have been arrested in this way, a process which I describe in careful detail in this article. Legal scholars have sounded many alarms, and have called for immediate bans on this methodology, which is referred to as long- range familial searching (or "LRFS"). The opponents’ concerns are many, but generally boil down to fears that LRFS will invade the privacy and autonomy of presumptively innocent individuals. These concerns, I argue, are considerably overblown. Indeed, many aspects of the methodology implicate nothing new, legally or ethically, and might even better protect privacy while exonerating the innocent. Law enforcement’s use of LRFS to solve cold cases is a bogeyman. The real threat to genetic privacy comes from shoddy consumer consent procedures, poor data security standards, and user agreements that permit rampant secondary uses of data. So why do so many legal scholars fear a world where law enforcement uses this methodology? I surmise that our fear of so-called genetic informants stems from the sticky and long-standing traps of genetic essentialism and genetic determinism, where we incorrectly attribute intentional action to our genes and fear a world where humans are controlled by our biology. Rather than banning the use of genetic genealogy to catch serial killers and rapists, I call for improved direct-to-consumer consent processes, and more transparent privacy and security measures. This will better protect genetic privacy in line with consumer expectations, while still permitting the use of LRFS to deliver justice to victims and punish those who commit society's most heinous acts.
YAP and TAZ Promote Periosteal Osteoblast Precursor Expansion and Differentiation for Fracture Repair.

In response to bone fracture, periosteal progenitor cells proliferate, expand, and differentiate to form cartilage and bone in the fracture callus. These cellular functions require the coordinated activation of multiple transcriptional programs, and the transcriptional regulators Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) regulate osteochondroprogenitor activation during endochondral bone development. However, recent observations raise important distinctions between the signaling mechanisms used to control bone morphogenesis and repair. Here, we tested the hypothesis that YAP and TAZ regulate osteochondroprogenitor activation during endochondral bone fracture healing in mice. Constitutive YAP and/or TAZ deletion from Osterix-expressing cells impaired both cartilage callus formation and subsequent mineralization. However, this could be explained either by direct defects in osteochondroprogenitor differentiation after fracture or by developmental deficiencies in the progenitor cell pool before fracture. Consistent with the second possibility, we found that developmental YAP/TAZ deletion produced long bones with impaired periosteal thickness and cellularity. Therefore, to remove the contributions of developmental history, we next generated adult onset-inducible knockout mice (using Osx-CretetOff ) in which YAP and TAZ were deleted before fracture but after normal development. Adult onset-induced YAP/TAZ deletion had no effect on cartilaginous callus formation but impaired bone formation at 14 days post-fracture (dpf). Earlier, at 4 dpf, adult onset-induced YAP/TAZ deletion impaired the proliferation and expansion of osteoblast precursor cells located in the shoulder of the callus. Further, activated periosteal cells isolated from this region at 4 dpf exhibited impaired osteogenic differentiation in vitro upon YAP/TAZ deletion. Finally, confirming the effects on osteoblast function in vivo, adult onset-induced YAP/TAZ deletion impaired bone formation in the callus shoulder at 7 dpf before the initiation of endochondral ossification. Together, these data show that YAP and TAZ promote the expansion and differentiation of periosteal osteoblast precursors to accelerate bone fracture healing. © 2020 American Society for Bone and Mineral Research (ASBMR).
The Round Complexity of Secure Computation Against Covert Adversaries.

We investigate the exact round complexity of secure multiparty computation (MPC) against covert adversaries who may attempt to cheat, but do not wish to be caught doing so. Covert adversaries lie in between semi-honest adversaries who follow protocol specification and malicious adversaries who may deviate arbitrarily.