cssi/SciSciGPT-SciSciNet huggingface dataset:
Clipper: a low-latency online prediction serving system

Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks and applications. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluate Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. Finally, we compare Clipper to the Tensorflow Serving system and demonstrate that we are able to achieve comparable throughput and latency while enabling model composition and online learning to improve accuracy and render more robust predictions.
Guarded Computational Type Theory

Nakano's later modality can be used to specify and define recursive functions which are causal or synchronous; in concert with a notion of clock variable, it is possible to also capture the broader class of productive (co)programs. Until now, it has been difficult to combine these constructs with dependent types in a way that preserves the operational meaning of type theory and admits a hierarchy of universes Ui.We present an operational account of guarded dependent type theory with clocks called CTT, featuring a novel clock intersection connective {k ÷ clk} → A that enjoys the clock irrelevance principle, as well as a predicative hierarchy of universes Ui which does not require any indexing in clock contexts. CTT is simultaneously a programming language with a rich specification logic, as well as a computational metalanguage that can be used to develop semantics of other languages and logics.
Mixed monotonicity of partial first-in-first-out traffic flow models

In vehicle traffic networks, congestion on one outgoing link of a diverging junction often impedes flow to other outgoing links, a phenomenon known as the first-in-first-out (FIFO) property. Simplified traffic models that do not account for the FIFO property result in monotone dynamics for which powerful analysis techniques exist. FIFO models are in general not monotone, but have been shown to be mixed monotone—a generalization of monotonicity that enables similarly powerful analysis techniques. In this paper, we study traffic flow models for which the FIFO property is only partial, that is, flows at diverging junctions exhibit a combination of FIFO and non-FIFO phenomena. We show that mixed monotonicity extends to this wider class of models and establish conditions that guarantee convergence to an equilibrium.
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models

We investigate the problem of learning discrete, undirected graphical models in a differentially private way. We show that the approach of releasing noisy sufficient statistics using the Laplace mechanism achieves a good trade-off between privacy, utility, and practicality. A naive learning algorithm that uses the noisy sufficient statistics "as is" outperforms general-purpose differentially private learning algorithms. However, it has three limitations: it ignores knowledge about the data generating process, rests on uncertain theoretical foundations, and exhibits certain pathologies. We develop a more principled approach that applies the formalism of collective graphical models to perform inference over the true sufficient statistics within an expectation-maximization framework. We show that this learns better models than competing approaches on both synthetic data and on real human mobility data used as a case study.
A neural model for generating natural language summaries of program subroutines

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.
Novel structures in Stanley sequences

Abstract Given a set of integers with no 3-term arithmetic progression, one constructs a Stanley sequence by choosing integers greedily without forming such a progression. This paper offers two main contributions to the theory of Stanley sequences. First, we describe all known Stanley sequences with closed-form expressions as solutions to constraints in modular arithmetic, defining the modular and pseudomodular Stanley sequences. Second, we introduce the basic Stanley sequences, whose elements arise as the sums of finite subsets of a basis sequence. Applications of our results include the construction of Stanley sequences with arbitrarily large gaps between terms, answering a weak version of a problem by Erdős et al. Finally, we generalize several results about Stanley sequences to p -free sequences, where p is any odd prime.
The Conway polynomial and amphicheiral knots

According to work of Hartley and Kawauchi in 1979 and 1980, the Conway Polynomial of all negative amphicheiral knots and strongly positive amphicheiral knots factors as ϕ(z)ϕ(−z) for some ϕ(z) ∈ ℤ[z]. Moreover, a 2012 example due to Ermotti, Hongler and Weber shows that this is not true for general amphicheiral knots. On the other hand, in 2006, the first author made a conjecture equivalent to saying that the Conway polynomial of all amphicheiral knots splits as ϕ(z)ϕ(−z) in the ring ℤ4[z]. In this paper, we establish this conjecture for all periodically amphicheiral knots built from braids, where the period preserves the braid structure. We also give counterexamples to conjectures on the leading coefficient of the Conway polynomial of an amphicheiral knot due to Stoimenow.
Confounding-Robust Policy Improvement

We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding in the data-generating process. Unlike previous approaches that assume unconfoundedness, i.e., no unobserved confounders affected both treatment assignment and outcomes, we calibrate policy learning for realistic violations of this unverifiable assumption with uncertainty sets motivated by sensitivity analysis in causal inference. Our framework for confounding-robust policy improvement optimizes the minimax regret of a candidate policy against a baseline or reference "status quo" policy, over an uncertainty set around nominal propensity weights. We prove that if the uncertainty set is well-specified, robust policy learning can do no worse than the baseline, and only improve if the data supports it. We characterize the adversarial subproblem and use efficient algorithmic solutions to optimize over parametrized spaces of decision policies such as logistic treatment assignment. We assess our methods on synthetic data and a large clinical trial, demonstrating that confounded selection can hinder policy learning and lead to unwarranted harm, while our robust approach guarantees safety and focuses on well-evidenced improvement.
Equivalence of quantum metrics with a common domain

Abstract We characterize Lipschitz morphisms between quantum compact metric spaces as those *-morphisms which preserve the domain of certain noncommutative analogues of Lipschitz seminorms, namely lower semi-continuous Lip-norms. As a corollary, lower semi-continuous Lip-norms with a shared domain are in fact equivalent. We then note that when a family of lower semi-continuous Lip-norms are uniformly equivalent, then they give rise to totally bounded classes of quantum compact metric spaces, and we apply this observation to several examples of perturbations of quantum metric spaces. We also construct the noncommutative generalization of the Lipschitz distance between quantum compact metric spaces.
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. In this paper, we study how to address three critical challenges for this task: the cross-modal grounding, the ill-posed feedback, and the generalization problems. First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL). Particularly, a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and trajectories, and a reasoning navigator is employed to perform cross-modal grounding in the local visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model significantly outperforms previous methods by 10% on SPL and achieves the new state-of-the-art performance. To improve the generalizability of the learned policy, we further introduce a Self-Supervised Imitation Learning (SIL) method to explore unseen environments by imitating its own past, good decisions. We demonstrate that SIL can approximate a better and more efficient policy, which tremendously minimizes the success rate performance gap between seen and unseen environments (from 30.7% to 11.7%).
Thyroid hormone modulation during zebrafish development recapitulates evolved diversity in danionin jaw protrusion mechanics

Protrusile jaws are a highly useful innovation that has been linked to extensive diversification in fish feeding ecology. Jaw protrusion can enhance the performance of multiple functions, such as suction production and capturing elusive prey. Identifying the developmental factors that alter protrusion ability will improve our understanding of fish diversification. In the zebrafish protrusion arises postmetamorphosis. Fish metamorphosis typically includes significant changes in trophic morphology, accompanies a shift in feeding niche and coincides with increased thyroid hormone production. We tested whether thyroid hormone affects the development of zebrafish feeding mechanics. We found that it affected all developmental stages examined, but that effects were most pronounced after metamorphosis. Thyroid hormone levels affected the development of jaw morphology, feeding mechanics, shape variation, and cranial ossification. Adult zebrafish utilize protrusile jaws, but an absence of thyroid hormone impaired development of the premaxillary bone, which is critical to jaw protrusion. Premaxillae from early juvenile zebrafish and hypothyroid adult zebrafish resemble those from adults in the genera Danionella, Devario, and Microdevario that show little to no jaw protrusion. Our findings suggest that evolutionary changes in how the developing skulls of danionin minnows respond to thyroid hormone may have promoted diversification into different feeding niches.
Differential variation and expression analysis

We propose an empirical Bayes approach using a three-component mixture model, the L2N model, that may be applied to detect both differential expression (mean) and variation. It consists of two log-normal components (L2) for the differentially expressed (dispersed) features (one component for under-expressed [dispersed] and the other for over-expressed [dispersed] features), and a single normal component (N) for the null features (i.e., non-differentially expressed [dispersed] features). Simulation results show that L2N can capture asymmetries in the numbers of over- and under- expressed (dispersed) features (e.g., genes) when they exist, can provide a better fit to data in which the distributions of the null and non-null features are not well-separated, but can also perform well under symmetry and separation. Thus the L2N model is particularly appealing when no a priori biological knowledge about the mixture density is available. The L2N model is implemented in an R package called DVX, for Differential Variation and eXpression analysis. The package also includes an implementation of differential expression analysis via the limma package, and a differential variation and expression analysis using a three-way normal mixture model. DVX is a user-friendly, graphical interface implemented via the 'Shiny' package, so that a user is not required to have R programming knowledge. It offers a set of diagnostics plots, data transformation tools, and report generation in Microsoft Excel- and Word-compatible formats. The package is available on the web, at https://haim-bar.uconn.edu/software/DVX/ .
$\mathcal{N}=(0,2)$ SYK, Chaos and Higher-Spins

We study a 2-dimensional SYK-like model with $$ \mathcal{N}=\left(0,\ 2\right) $$ supersymmetry. The model describes N chiral supermultiplets and M Fermi supermultiplets with a (q + 1)- field interaction. We solve the model analytically and numerically in the N ≫ 1, M ≫ 1 limit with $$ \mu \equiv \frac{M}{N} $$ being a free parameter. Two distinct higher-spin symmetries emerge when the μ parameter approaches the two ends of its range. This is verified by the appearance of conserved higher-spin operators and the vanishing of chaotic behaviors in the two limits. Therefore this model provides a manifest realization of the widely believed connection between SYK-like models and higher-spin theories. In addition, as the parameter μ varies we find the largest Lyapunov exponent of this model to be slightly larger than that in models with non-chiral supersymmetry.
Chance Constrained Motion Planning for High-Dimensional Robots

This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertainty and imperfect state information. Given process and observation noise models, it can find feasible trajectories which satisfy a user-specified bound over the probability of collision. Leveraging our previous work in deterministic motion planning which integrated trajectory optimization into a sparse roadmap framework, p-Chekov shows superiority in its planning speed for high-dimensional tasks. P-Chekov incorporates a linear-quadratic Gaussian motion planning approach into the estimation of the robot state probability distribution, applies quadrature theories to waypoint collision risk estimation, and adapts risk allocation approaches to assign allowable probabilities of failure among waypoints. Unlike other existing risk-aware planners, p-Chekov can be applied to high-DOF robotic planning tasks without the convexification of the environment. The experiment results in this paper show that this p-Chekov system can effectively reduce collision risk and satisfy user-specified chance constraints in typical real-world planning scenarios for high-DOF robots.
Shear-Driven Chemical Decomposition of Boron Carbide

We report, for the first time, the observation of a shear-induced decomposition of boron carbide into B50C2 and nanocrystalline graphite at pressures from 1.0 to −3.5 GPa. It is proved that shear u...
Consumer Demand for Ethical Products and the Role of Cultural Worldviews: The Case of Direct-Trade Coffee

Abstract Ethically-labeled products – those that address environmental and human-welfare issues – are increasingly prevalent in consumer decision-making. This research estimates the value consumers place on direct trade coffee, a relatively new and complex ethical product. Direct trade coffee is defined by having three attributes that differentiate it from standard coffee: (i) price premiums are paid directly to farmers; (ii) harvesting practices are sustainable; and (iii) the quality of the product is enhanced. The first two attributes of direct trade coffee lead to social benefits while the third is strictly a private benefit. Using a discrete choice experiment, we find that consumers are willing to pay significant premiums for each of the three attributes and are willing to pay slightly more for those attributes with social benefits. We also find evidence that consumers’ willingness to pay for different attributes of an ethical product varies significantly based on their cultural worldviews.
Concentration of random graphs and application to community detection

Author(s): Le, CM; Levina, E; Vershynin, R | Abstract: Random matrix theory has played an important role in recent work on statistical network analysis. In this paper, we review recent results on regimes of concentration of random graphs around their expectation, showing that dense graphs concentrate and sparse graphs concentrate after regularization. We also review relevant network models that may be of interest to probabilists considering directions for new random matrix theory developments, and random matrix theory tools that may be of interest to statisticians looking to prove properties of network algorithms. Applications of concentration results to the problem of community detection in networks are discussed in detail.
The rise of Public History: an international perspective

This article explores the birth and development of public history and presents the different criteria of its internationalization from the 1970s to the more recent creation of the International Fed...
An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing

Abstract Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on a quality-cost-aware task allocation problem in multi-attribute social sensing applications. The goal is to identify a task allocation strategy (i.e., decide when and where to collect sensing data) to achieve an optimized tradeoff between the data quality and the sensing cost. While recent progress has been made to tackle similar problems, three important challenges have not been well addressed: (i) “online task allocation”: the task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables in social sensing; (ii) “multi-attribute constrained optimization”: minimizing the overall sensing error given the dependencies and constraints of multiple attributes of the measured variables is a non-trivial problem to solve; (iii) “nonuniform task allocation cost”: the task allocation cost in social sensing often has a nonuniform distribution which adds additional complexity to the optimized task allocation problem. This paper develops a Quality-Cost-Aware Online Task Allocation (QCO-TA) scheme to address the above challenges using a principled online reinforcement learning framework. We evaluate the QCO-TA scheme through a real-world social sensing application and the results show that our scheme significantly outperforms the state-of-the-art baselines in terms of both sensing accuracy and cost.
Viewing Earth's surface as a soft-matter landscape

Earth’s surface is composed of a staggering diversity of particulate–fluid mixtures: dry to wet, dilute to dense, colloidal to granular and attractive to repulsive particles. This material variety is matched by the range of relevant stresses and strain rates, from laminar to turbulent flows and steady to intermittent forcing, leading to anything from rapid and catastrophic landslides to the slow relaxation of soil and rocks over geologic timescales. From a physics point of view, virtually all Earth and planetary landscapes are composed of soft matter, in the sense that they are both deformable and sensitive to collective effects. Geophysical materials, however, often involve compositions and flow geometries that have not yet been examined in physics. In this Review, we explore how a soft-matter physics perspective has helped to illuminate, and even predict, the rich dynamics of earth materials and their associated landscapes. We also highlight phenomena of geophysical flows that challenge, and will hopefully inspire, work on more fundamental aspects of soft matter. Earth and planetary landscapes are composed of soft matter: amorphous materials that deform in response to broad-spectrum excitations, from fluid turbulence to plate tectonics. This Review surveys complex behaviours of earth materials that challenge existing physics frameworks and may inspire new approaches.
Girth six cubic graphs have Petersen minors

We prove that every 3-regular graph with no circuit of length less than six has a subgraph isomorphic to a subdivision of the Petersen graph.
Structural Basis of H2B Ubiquitination-Dependent H3K4 Methylation by COMPASS

Summary The COMPASS (complex of proteins associated with Set1) complex represents the prototype of the SET1/MLL family of methyltransferases that controls gene transcription by H3K4 methylation (H3K4me). Although H2B monoubiquitination (H2Bub) is well known as a prerequisite histone mark for COMPASS activity, how H2Bub activates COMPASS remains unclear. Here, we report the cryoelectron microscopy (cryo-EM) structures of an extended COMPASS catalytic module (CM) bound to the H2Bub and free nucleosome. The COMPASS CM clamps onto the nucleosome disk-face via an extensive interface to capture the flexible H3 N-terminal tail. The interface also sandwiches a critical Set1 arginine-rich motif (ARM) that autoinhibits COMPASS. Unexpectedly, without enhancing COMPASS-nucleosome interaction, H2Bub activates the enzymatic assembly by packing against Swd1 and alleviating the inhibitory effect of the Set1 ARM upon fastening it to the acidic patch. By delineating the spatial configuration of the COMPASS-H2Bub-nucleosome assembly, our studies establish the structural framework for understanding the long-studied H2Bub-H3K4me histone modification crosstalk.
Habit planes of twins in a deformed Mg alloy determined from three-dimensional microstructure analysis

Abstract Serial sectioning and electron backscatter diffraction were used to image the three-dimensional microstructures of Mg alloy AZ31B deformed in tension and compression along sample directions that promote twinning. These measurements were used to statistically determine the habit planes of twin boundaries within the samples. For the sample deformed in tension, the boundary population is dominated by the so-called extension twin (lattice disorientation of 86° about 2 1 ¯ 1 ¯ 0 with symmetric 01 1 ¯ 2 boundary planes) and boundaries formed by the intersection of these defects (lattice disorientation of 60° about 01 1 ¯ 0 with 11 2 ¯ 1 and 01 1 ¯ 1 boundary planes). The sample deformed in compression, however, was different. Fewer twin variants were activated and this led to many fewer intersections and intervariant boundaries. Furthermore, while the dominant boundary also had the lattice disorientation of 86° about 2 1 ¯ 1 ¯ 0 , consistent with an extension twin, the dominant habit planes have the asymmetric 0 1 ¯ 11 and 01 1 ¯ 3 orientations. The difference in the habit planes was attributed to the presence or absence of twin intersections. When many twins intersect, the twins form plate-shaped grains, bounded by two parallel 01 1 ¯ 2 interfaces. Without these intersections, the twins are wedge-shaped, with the bounding 0 1 ¯ 11 and 01 1 ¯ 3 interfaces meeting at a sharp edge.
Application of an ANFIS Algorithm to Sensor Data Processing

Some sensors require frequent recalibrations; therefore, calibration cost must be limited. In this paper, a new calibration technique is presented. It is a two-phase method which is based on adaptive neuro-fuzzy networks, and it shows superior performances with respect to traditional algorithms, requiring fewer calibration points and less computational power in the recalibration phase. Feasibility has been demonstrated with a pyroelectric biaxial positioning system, reaching performance to the limit of the adopted test bench, on the order of 20 mum with respect to a whole sensible area of 7 mm times 7 mm
When Polarization Trumps Civic Virtue: Partisan Conflict and the Subversion of Democracy by Incumbents

When Polarization Trumps Civic Virtue: Partisan Conflict and the Subversion of Democracy by Incumbents
A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management

Every activity in disaster management demands accurate and up-to-date information to allow a quick, easy, and cost-effective response to reduce the possible loss of lives and properties. It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks such as Twitter allow people to share information in real-time. However, gathering of valuable information requires a series of operations such as (1) processing each tweet for the text classification, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classification of tweets. These are three primary challenges in developing an effective rescue scheduling operation using social media data. In this paper, first, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using data from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classification methods based on Precision, Recall, F1-score, and Accuracy, and is highly effective to determine the priority of a tweet. Furthermore, to evaluate the effectiveness and robustness of the proposed classification model a merged dataset comprises of 4 different datasets from CrisisNLP and another 15 different disasters data from CrisisLex are used. Finally, we develop an adaptive multi-task hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities.
A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance—thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.
Online Charge Scheduling for Electric Vehicles in Autonomous Mobility on Demand Fleets

In this paper, we study an online charge scheduling strategy for fleets of autonomous-mobility-on-demand electric vehicles (AMoD EVs). We consider the case where vehicles complete trips and then enter a between-ride state throughout the day, with their information becoming available to the fleet operator in an online fashion. In the between-ride state, the vehicles must be scheduled for charging and then routed to their next passenger pick-up locations. Additionally, due to the unknown daily sequences of ride requests, the problem cannot be solved by any offline approach. As such, we study an online welfare maximization heuristic based on primal-dual methods that allocates limited fleet charging resources and rebalances the vehicles while avoiding congestion at charging facilities and pick-up locations. We discuss a competitive ratio result comparing the performance of our online solution to the clairvoyant offline solution and provide numerical results highlighting the performance of our heuristic.
Direct Air Capture of CO2 with Aqueous Amino Acids and Solid Bis-iminoguanidines (BIGs)

We report a bench-scale direct air capture (DAC) process comprising CO2 absorption with aqueous amino acid salts (i.e., potassium glycinate, potassium sarcosinate), followed by room-temperature reg...
Principles for Successful Cognitive Interventions

This essay is a distillations of decades of efforts at cognitive intervention by many educators. Three likely outcomes for cognitive interventions are described. Recommendations for when interventions scan most effectively be conducted, and what children are most likely to respond most favorably are also advanced. Finally, the general nature of the subject matter of successful cognitive interventions, and parameters that are most likely to produce good internalization and application of whatever is conveyed by the intervention are also described.
Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs

The last decade has seen a shift in the computer systems industry where heterogeneous computing has become prevalent. Graphics Processing Units (GPUs) are now present in supercomputers to mobile phones and tablets. GPUs are used for graphics operations as well as general-purpose computing (GPGPUs) to boost the performance of compute-intensive applications. However, the percentage of undisclosed characteristics beyond what vendors provide is not small. In this paper, we introduce a very low overhead and portable analysis for exposing the latency of each instruction executing in the GPU pipeline(s) and the access overhead of the various memory hierarchies found in GPUs at the micro-architecture level. Furthermore, we show the impact of the various optimizations the CUDA compiler can perform over the various latencies. We perform our evaluation on seven different high-end NVIDIA GPUs from five different generations/architectures: Kepler, Maxwell, Pascal, Volta, and Turing. The results in this paper can help architects to have an accurate characterization of the latencies of these GPUs, which will help in modeling the hardware accurately. Also, software developers can perform informed optimizations to their applications.
Quantum Computing with Octonions

There are two schools of “measurement-only quantum computation”. The first (Phys. Rev. Lett. 86(22), 5188–5191 (2001)) using prepared entanglement (cluster states) and the second (Phys. Rev. Lett. 101(1), 010501 (2008)) using collections of anyons which, according to how they were produced, also have an entanglement pattern. We abstract the common principle behind both approaches and find the notion of a graph or even continuous family of equiangular projections. This notion is the leading character in the paper. The largest continuous family, in a sense made precise in Corollary 4.2, is associated with the octonions and this example leads to a universal computational scheme. Adiabatic quantum computation also fits into this rubric as a limiting case: nearby projections are nearly equiangular, so as a gapped ground state space is slowly varied, the corrections to unitarity are small.
Single-molecule turnover dynamics of actin and membrane coat proteins in clathrin-mediated endocytosis

Actin dynamics generate forces to deform the membrane and overcome the cell's high turgor pressure during clathrin-mediated endocytosis (CME) in yeast, but precise molecular details are still unresolved. Our previous models predicted that actin filaments of the endocytic meshwork continually polymerize and disassemble, turning over multiple times during an endocytic event, similar to other actin systems. We applied single-molecule speckle tracking in live fission yeast to directly measure molecular turnover within CME sites for the first time. In contrast with the overall ~20 s lifetimes of actin and actin-associated proteins in endocytic patches, we detected single-molecule residence times around 1 to 2 s, and similarly high turnover rates of membrane-associated proteins in CME. Furthermore, we find heterogeneous behaviors in many proteins' motions. These results indicate that endocytic proteins turn over up to five times during the formation of an endocytic vesicle, and suggest revising quantitative models of force production.
Modified commutators are not sufficient to determine a quantum gravity minimal length scale

Abstract In quantum gravity it is generally thought that a modified commutator of the form [ x ˆ , p ˆ ] = i ħ ( 1 + β p 2 ) is sufficient to give rise to a minimum length scale. We test this assumption and find that different pairs of modified operators can lead to the same modified commutator and yet give different or even no minimal length. The conclusion is that the modification of the operators is the main factor in determining whether there is a minimal length. This fact - that it is the specific form of the modified operators which determine the existence or not of a minimal length scale - can be used to keep or reject specific modifications of the position and momentum operators in theory of quantum gravity.
Did Soviet elderly employment respond to financial incentives? Evidence from pension reforms

Abstract This study answers the open question of whether workers respond to financial incentives in a command economy. To do this, I evaluate pension reforms in Soviet Russia in 1964 and 1969 that allowed pensioners to receive a greater share of their pensions if they worked, resulting in a progressive elimination of benefit reduction rates. Variation in group eligibility and variation in benefit reduction rates in eastern and western regions allow for the use of several difference-in-differences frameworks. I collect and digitize novel data from the Soviet archives on pensioner employment, constructing the first database of the Soviet old-age labor market. I find that Soviet pensioners are responsive to financial incentives. By 1969, after the benefit reduction rate fell from an average of 47.8% to 24.1%, pensioner employment rates rose by 5.7 percentage points, representing a 47% increase. Finally, I provide illustrative estimates of the employment elasticity with respect to the average net-of-tax rate that range from 0.6 to 1.4.
Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks

Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Here we ask if a neural network can recover both neural representations and, if the architecture is unconstrained and optimized, also the anatomical properties of neural circuits. We demonstrate this in a system where the connectivity and the functional organization have been characterized, namely, the head direction circuit of the rodent and fruit fly. We trained recurrent neural networks (RNNs) to estimate head direction through integration of angular velocity. We found that the two distinct classes of neurons observed in the head direction system, the Ring neurons and the Shifter neurons, emerged naturally in artificial neural networks as a result of training. Furthermore, connectivity analysis and in-silico neurophysiology revealed structural and mechanistic similarities between artificial networks and the head direction system. Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.
Poles of triple product L-functions involving monomial representations

In this paper, we study the order of the pole of the triple tensor product L-functions L(s,π1 × π2 × π3,⊗3) for cuspidal automorphic representations πi of GLni(&#x1d538;F) in the setting where one of the π...
Apprenticeship Learning via Frank-Wolfe

We consider the applications of the Frank-Wolfe (FW) algorithm for Apprenticeship Learning (AL). In this setting, we are given a Markov Decision Process (MDP) without an explicit reward function. Instead, we observe an expert that acts according to some policy, and the goal is to find a policy whose feature expectations are closest to those of the expert policy. We formulate this problem as finding the projection of the feature expectations of the expert on the feature expectations polytope – the convex hull of the feature expectations of all the deterministic policies in the MDP. We show that this formulation is equivalent to the AL objective and that solving this problem using the FW algorithm is equivalent well-known Projection method of Abbeel and Ng (2004). This insight allows us to analyze AL with tools from convex optimization literature and derive tighter convergence bounds on AL. Specifically, we show that a variation of the FW method that is based on taking “away steps” achieves a linear rate of convergence when applied to AL and that a stochastic version of the FW algorithm can be used to avoid precise estimation of feature expectations. We also experimentally show that this version outperforms the FW baseline. To the best of our knowledge, this is the first work that shows linear convergence rates for AL.
Atmospheric Density Uncertainty Quantification for Satellite Conjunction Assessment

Conjunction assessment requires knowledge of the uncertainty in the predicted orbit. Errors in the atmospheric density are a major source of error in the prediction of low Earth orbits. Therefore, accurate estimation of the density and quantification of the uncertainty in the density is required. Most atmospheric density models, however, do not provide an estimate of the uncertainty in the density. In this work, we present a new approach to quantify uncertainties in the density and to include these for calculating the probability of collision Pc. For this, we employ a recently developed dynamic reduced-order density model that enables efficient prediction of the thermospheric density. First, the model is used to obtain accurate estimates of the density and of the uncertainty in the estimates. Second, the density uncertainties are propagated forward simultaneously with orbit propagation to include the density uncertainties for Pc calculation. For this, we account for the effect of cross-correlation in position uncertainties due to density errors on the Pc. Finally, the effect of density uncertainties and cross-correlation on the Pc is assessed. The presented approach provides the distinctive capability to quantify the uncertainty in atmospheric density and to include this uncertainty for conjunction assessment while taking into account the dependence of the density errors on location and time. In addition, the results show that it is important to consider the effect of cross-correlation on the Pc, because ignoring this effect can result in severe underestimation of the collision probability.
Skeletons of stable maps II: superabundant geometries

We implement new techniques involving Artin fans to study the realizability of tropical stable maps in superabundant combinatorial types. Our approach is to understand the skeleton of a fundamental object in logarithmic Gromov–Witten theory—the stack of prestable maps to the Artin fan. This is used to examine the structure of the locus of realizable tropical curves and derive three principal consequences. First, we prove a realizability theorem for limits of families of tropical stable maps. Second, we extend the sufficiency of Speyer’s well-spacedness condition to the case of curves with good reduction. Finally, we demonstrate the existence of liftable genus 1 superabundant tropical curves that violate the well-spacedness condition.
Coordination and Dynamic Promotion Strategies in Crowdfunding with Network Externalities

Crowdfunding, a peer‐to‐peer fundraising mechanism, solicits capital from individual backers to support entrepreneurial projects. Entrepreneurs set a funding target and deadline; the project will be funded only if it reaches this funding target by the deadline. Backers individually decide whether to contribute, but their total contributions collectively determine whether the project will be successfully funded. This study models the dynamics of backers’ contributions in the presence of success uncertainty and analyzes managerial promotion strategies to maximize the likelihood of funding success. Two opposing forces affect backer decisions: backers are more likely to back a project that has already reached a greater fraction of its funding goal (positive externalities), but the backing propensity declines over time (negative deadline effects). These two competing forces give rise to a time‐dependent critical threshold of funding that a project must attain to achieve successful funding. We evaluate actionable promotion strategies (when to promote the project and how much promotion effort to spend) for entrepreneurs to dynamically manage their crowdfunding campaigns.
High-resolution home location prediction from tweets using deep learning with dynamic structure

Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for applications such as disaster response and public health. However, conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, the large user-base and real-time nature of social media data have been leveraged to address this problem. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. In this paper, we develop a deep-learning solution that deals with the sparsity and noise of social media data. We obtained over 90% accuracy for large subsets on a commonly used dataset. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets.
The Generalized Lasso for Sub-Gaussian Measurements With Dithered Quantization

In the problem of structured signal recovery from high-dimensional linear observations, it is commonly assumed that full-precision measurements are available. Under this assumption, the recovery performance of the popular Generalized Lasso (G-Lasso) is by now well-established. In this paper, we extend these types of results to the practically relevant settings with quantized measurements. We study two extremes of the quantization schemes, namely, uniform and one-bit quantization; the former imposes no limit on the number of quantization bits, while the second only allows for one bit. In the presence of a uniform dithering signal and when measurement vectors are sub-gaussian, we show that the same algorithm (i.e., the G-Lasso) has favorable recovery guarantees for both uniform and one-bit quantization schemes. Our theoretical results, shed light on the appropriate choice of the range of values of the dithering signal and accurately capture the error dependence on the problem parameters. For example, our error analysis shows that the G-Lasso with one-bit uniformly dithered measurements leads to only a logarithmic rate loss compared to the full-precision measurements.
Exact solutions of (0,2) Landau-Ginzburg models

In this paper we study the low energy physics of Landau-Ginzburg models with $$ \mathcal{N} $$ = (0, 2) supersymmetry. We exhibit a number of classes of relatively simple LG models where the conformal field theory at the low energy fixed point can be explicitly identified. One interesting class of fixed points can be thought of as “heterotic” minimal models. Other examples include $$ \mathcal{N} $$ = (0, 2) renormalization group flows that end up at $$ \mathcal{N} $$ = (2, 2) minimal models and models with non-abelian symmetry.
Order and frustration in chiral liquid crystals

This article reviews the complex ordered structures induced by chirality in liquid crystals. In general, chirality favours a twist in the orientation of liquid-crystal molecules. In some cases, as in the cholesteric phase, this favoured twist can be achieved without any defects. More often, the favoured twist competes with applied electric or magnetic fields or with geometric constraints, leading to frustration. In response to this frustration, the system develops ordered structures with periodic arrays of defects. The simplest example of such a structure is the lattice of domains and domain walls in a cholesteric phase under a magnetic field. More complex examples include defect structures formed in two-dimensional films of chiral liquid crystals. The same considerations of chirality and defects apply to three-dimensional structures, such as the twist-grain-boundary and moire phases.
Eisenstein series whose Fourier coefficients are zeta functions of binary Hermitian forms

In this paper we investigate a result of Ueno on the modularity of generating series associated to the zeta functions of binary Hermitian forms previously studied by Elstrodt et al. We improve his result by showing that the generating series are Eisenstein series. As a consequence we obtain an explicit formula for the special values of zeta functions associated with binary Hermitian forms.
HolDCSim: A Holistic Simulator for Data Centers*

A comprehensive data center simulation infrastructure, which models major hardware and system components and offers interfaces to manage the interplay between computation and communication resources, is critical in advancing future research for more effective performance and energy optimization in these environments. In this paper, we present HolDCSim, a light-weight, holistic, extensible, event-driven data center simulation platform that effectively models both server and network architectures. HolDCSim can be used in a variety of data center system studies including job/task scheduling, resource provisioning, global and local server farm power management, and performance analysis. We demonstrate the design of our simulation infrastructure, and illustrate the usefulness of our framework with a case study that analyzes server-network performance and energy efficiency. We also perform validation studies for our simulator on a physical testbed with Intel Xeon-based processors and Cisco network switches.