cssi/SciSciGPT-SciSciNet huggingface dataset:
Hacking nonverbal communication between pedestrians and vehicles in virtual reality

We use an immersive virtual reality environment to explore the intricate social cues that underlie non-verbal communication involved in a pedestrian’s crossing decision. We “hack” non-verbal communication between pedestrian and vehicle by engineering a set of 15 vehicle trajectories, some of which follow social conventions and some that break them. By subverting social expectations of vehicle behavior we show that pedestrians may use vehicle kinematics to infer social intentions and not merely as the state of a moving object. We investigate human behavior in this virtual world by conducting a study of 22 subjects, with each subject experiencing and responding to each of the trajectories by moving their body, legs, arms, and head in both the physical and the virtual world. Both quantitative and qualitative responses are collected and analyzed, showing that, in fact, social cues can be engineered through vehicle trajectory manipulation. In addition, we demonstrate that immersive virtual worlds which allow the pedestrian to move around freely, provide a powerful way to understand both the mechanisms of human perception and the social signaling involved in pedestrian-vehicle interaction.
Laser optical pumping of potassium in a high magnetic field using linearly polarized light

Abstract It is shown experimentally that in a high magnetic field a potassium vapor can be optically pumped to a high electron spin polarization by light polarized parallel to the magnetic field and incident normal to the magnetic field. The polarization of the K vapor is measured both by observing the fluorescence and by the Faraday effect. This method of optical pumping may be useful for spin polarized targets.
A Mean Field Game Approach to Scheduling in Cellular Systems

There has been much work done on designing cellular scheduling algorithms. These algorithms are set up in the manner of a direct mechanism in which the queues reveal their states (backlog), and the scheduler chooses an allocation. In policies such as longest queue first (LQF), scheduling can be shown to yield short queue lengths. However, these algorithms are reliant on truthful declarations of state. Our goal in this article is to determine if a Vickrey–Clarke–Groves (VCG)-type mechanism (a second price auction) that elicits truthful value will also possess the desired LQF-like behavior in a system of dynamically evolving queues. Our approach is to use the concept of a mean field game under which at each time instant, a queue chooses its bid as a best response to its belief that the bids of the others will be drawn independently from a common bid distribution. If this best response is itself a sample from the belief bid distribution, a mean field equilibrium (MFE) is said to exist. We show the existence of the MFE and find it using its structure that the results of the allocation policy are the same as LQF. Thus, the desired LQF-like behavior arises naturally under the second-price auction mechanism. We also present results on the accuracy of the model as the number of agents (queues) becomes large. Finally, using simulations with a large number of queues, we show that computation of the MFE is straightforward.
Chapter 2 – Body-Texture Interactions

Publisher Summary The properties of texture and viscosity are perceived by the human senses. To understand texture and viscosity it is necessary to know how the human body interacts with food. Mastication is a process in which pieces of food are ground into a fine state, mixed with saliva, and brought to body temperature in readiness for transfer to the stomach, where most of the digestion occurs. After some residence time in the stomach the food passes to the small intestine, where digestion continues, and from there the nutrients are absorbed into the bloodstream and distributed throughout the body. Pulverization of food is the main function of mastication, but it also imparts pleasurable sensations that fill a basic human need. Mastication usually reduces particle size by two to three orders of magnitude before passing to the stomach, where another approximately 20 orders of magnitude of size reduction are accomplished by chemical and biochemical action. If food cannot be reduced to particles of the order of a few multiples of 10–22 g, it is not absorbed and utilized but is excreted. This chapter gives a simple description of the human masticatory apparatus. Since texture is perceived by the human senses, one needs to understand how the body interacts with different foods because this is the foundation on which is built an understanding of what is needed in objective and subjective tests for texture.
Mimicking Portfolios with Conditioning Information

Mimicking portfolios have long been useful in asset pricing research. In most empirical applications, the portfolio weights are assumed to be fixed over time, while in theory they may be functions of the economic state. This paper derives and characterizes mimick ing portfolios in the presence of predetermined state variables, or conditioning informa tion. The results generalize and integrate multifactor minimum variance efficiency (Fama ( 1996)) with conditional and unconditional mean-variance efficiency (Hansen and Richard (1987), Ferson and Siegel (2001)). Empirical examples illustrate the potential importance of time-varying mimicking portfolio weights and highlight challenges in their application.
Effects of Fiscal Consolidation in the Czech Republic

This article uses the IMF’s Global Integrated Monetary and Fiscal Model (GIMF) to assess the impact of fiscal consolidation on the Czech economy. Its contribution is threefold. First, it provides estimates of dynamic fiscal multipliers for a variety of fiscal instruments (tax and expenditure), consolidation durations, assumptions about credibility, and monetary policy responses. Second, the article evaluates the impact on the economy of tightening measures envisaged in the 2011 budget. Third, the article considers alternative packages for consolidation beyond 2011 to achieve the government’s balanced budget target by 2016 and identifies which forms of adjustment are more growth-friendly.
Purine-responsive expression of the Leishmania donovani NT3 purine nucleobase transporter is mediated by a conserved RNA stem-loop

The ability to modulate gene expression in response to changes in the host environment is essential for survival of the kinetoplastid parasite Leishmania. Unlike most eukaryotes, gene expression in kinetoplastids is predominately regulated posttranscriptionally. Consequently, RNA-binding proteins and mRNA-encoded sequence elements serve as primary determinants of gene regulation in these organisms; however, few have defined roles in specific stress response pathways. Leishmania species cannot synthesize purines de novo and must scavenge these essential nutrients from the host. Leishmania have evolved a robust stress response to withstand sustained periods of purine scarcity during their life cycle. The purine nucleobase transporter LdNT3 is among the most substantially up-regulated proteins in purine-starved Leishmania donovani parasites. Here we report that the posttranslational stability of the LdNT3 protein is unchanged in response to purine starvation. Instead, LdNT3 up-regulation is primarily mediated by a 33-nucleotide-long sequence in the LdNT3 mRNA 3′ UTR that is predicted to adopt a stem–loop structure. Although this sequence is highly conserved within the mRNAs of orthologous transporters in multiple kinetoplastid species, putative stem–loops from L. donovani and Trypanosoma brucei nucleobase transporter mRNAs were not functionally interchangeable for purine-responsive regulation. Through mutational analysis of the element, we demonstrate that species specificity is attributable to just three variant bases within the predicted loop. Finally, we provide evidence that the abundance of the trans-acting factor that binds the LdNT3 stem–loop in vivo is substantially higher than required for regulation of LdNT3 alone, implying a potential role in regulating other purine-responsive genes.
Can Taxes Improve Congestion on All Networks

We ask if it is possible to positively influence social behavior with no risk of unintentionally incentivizing pathological behavior. In network routing problems, if network traffic is composed of many individual agents, it is known that self-interested behavior among the agents can lead to suboptimal network congestion. We study situations in which a system planner charges monetary tolls for the use of network links in an effort to incentivize efficient routing choices by the users, but in which the users’ sensitivity to tolls is heterogeneous and unknown. We seek locally computed tolls that are guaranteed not to incentivize worse network routing than in the uninfluenced case. Our main result is to show that if networks are sufficiently complex and populations sufficiently diverse, perverse incentives cannot be systematically avoided: Any taxation mechanism that improves outcomes on one network must necessarily degrade them on another. Nonetheless, for the simple class of parallel networks, nonperverse taxes do exist; we fully characterize all such taxation mechanisms, showing that they are a generalized version of traditional marginal-cost tolls.
Directed Search with Multiple Job Applications

We develop an equilibrium directed search model of the labor market where workers can simultaneously apply for multiple jobs. Our main theoretical contribution is to integrate the portfolio choice problem faced by workers into an equilibrium framework. All equilibria of our model exhibit wage dispersion. Consistent with stylized facts, the density of wages is decreasing and higher wage firms receive more applications per vacancy. Unlike most models of directed search, the equilibria are not constrained efficient.
Binary Synaptic Connections Based on Memory Switching in a-Si:H for Artificial Neural Networks

Nonvolatile, associative electronic memory based on neural network models promises high (−109 bits/cm2 ) information storage density since the information is stored in a matrix of only two-terminal, passive interconnections (synapses). An electronic memory switch at each interconnect would be ideal as a programmable synapse. The massive parallelism in the architecture, however, requires that the ‘ON’ state of a synaptic connection must be unusually ‘weak’ (i.e., highly resistive). For example, a binary synapse should be 106Ω in its ‘ON’ state and >109Ω in the ‘OFF’ state for a 1024 × 1024 matrix (−256 K bits of programmable read only memory, PROM). The small deliverable switching energy dictated by the resistive ‘ON’ state requirement is a new constraint for switching in thin films. Memory switching in hydrogenated amorphous silicon (a-Si:H) along with ballast (current limiting) resistors patterned from resistivity-tailored, amorphous Ge-metal alloys are investigated for a binary PROM matrix. A lμm2 area of a-Si:H could be switched from a −1010Ω (OFF state) to −105Ω (ON state) by a voltage pulse of lpsec duration, with the switching energy of −1 nanojoule, delivered through a 106Ω ballast resistor. Programmable, read-only, 1600 synapse (40x40) test arrays of uniform connection strengths (variation ±2%) and a-Si:H switching elements have been fabricated. Suitability of the memory switching in a-Si:H for high-density neural networks is discussed.
Utilizing aLIGO glitch classifications to validate gravitational-wave candidates

Advanced LIGO data contains numerous noise transients, or "glitches", that have been shown to reduce the sensitivity of matched filter searches for gravitational waves from compact binaries. These glitches increase the rate at which random coincidences occur, which reduces the significance of identified gravitational-wave events. The presence of these transients has precipitated extensive work to establish that observed gravitational wave events are astrophysical in nature. We discuss the response of the PyCBC search for gravitational waves from stellar mass binaries to various common glitches that were observed during Advanced LIGO's first and second observing runs. We show how these transients can mimic waveforms from compact binary coalescences and quantify the likelihood that a given class of glitches will create a trigger in the search pipeline. We explore the specific waveform parameters that are most similar to different glitch classes and demonstrate how knowledge of these similarities can be used when evaluating the significance of gravitational-wave candidates.
On the Design of Co-operating Blockchains for IoT

Enabling blockchain technology into IoT can help to achieve a proper distributed consensus based IoT system that overcomes disadvantages of today’s centralized infrastructures, such as, among others, high cloud server maintenance costs, weakness for supporting time-critical IoT applications, security and trust issues. However, meeting requirements posed by IoT in blockchain domain is not an easy endeavour. [1] proposes Hybrid-IoT, as a step towards decentralizing IoT with the help of blockchain technology. Hybrid-IoT consists of multiple PoW sub-blockchains to achieve distributed consensus among IoT devices and an inter-connector framework, to execute transactions between sub-blockchains. In this paper, we take the first step towards designing an inter-connector for multiple blockchains for IoT that is specifically tailored for the Hybrid-IoT architecture. We also provide a detailed security discussion, in order to identify threats and we provide discussion on how to cope with threats.
Gelfand-Naimark-Stone duality for normal spaces and insertion theorems

Abstract Gelfand-Naimark-Stone duality provides an algebraic counterpart of compact Hausdorff spaces in the form of uniformly complete bounded archimedean l-algebras. In [5] we extended this duality to completely regular spaces. In this article we use this extension to characterize normal, Lindelof, and locally compact Hausdorff spaces. Our approach gives a different perspective on the classical theorems of Katětov-Tong and Stone-Weierstrass.
Has Global Health Law Risen to Meet the COVID-19 Challenge? Revisiting the International Health Regulations to Prepare for Future Threats

Global health law is essential in responding to the infectious disease threats of a globalizing world, where no single country, or border, can wall off disease
Probing Multi-way Chromatin Interaction with Hypergraph Representation Learning

Advances in high-throughput mapping of 3D genome organization have enabled genome-wide characterization of chromatin interactions. However, proximity ligation based mapping approaches for pairwise chromatin interaction such as Hi-C cannot capture multi-way interactions, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution.
Variations of Lehmer's Conjecture for Ramanujan's tau-function

Abstract We consider natural variants of Lehmer's unresolved conjecture that Ramanujan's tau-function never vanishes. Namely, for n > 1 we prove that τ ( n ) ∉ { ± 1 , ± 3 , ± 5 , ± 7 , ± 691 } . This result is an example of general theorems (see Theorems 1.2 and 1.3 of [2] ) for newforms with trivial mod 2 residual Galois representation. Ramanujan's well-known congruences for τ ( n ) allow for the simplified proof in these special cases. We make use of the theory of Lucas sequences, the Chabauty–Coleman method for hyperelliptic curves, and facts about certain Thue equations.
Associations of functional, psychosocial, and medical factors with cognitive impairment in older, chemotherapy-naïve patients with early breast cancer.

12114Background: Cognitive decline related to cancer and its treatments is a common concern among patients receiving treatment for cancer. Routine cognitive screening in oncology practice has been ...
A Pairing-Based DAA Scheme Further Reducing TPM Resources.

Direct Anonymous Attestation (DAA) is an anonymous signature scheme designed for anonymous attestation of a Trusted Platform Module (TPM) while preserving the privacy of the device owner. Since TPM has limited bandwidth and computational capability, one interesting feature of DAA is to split the signer role between two entities: a TPM and a host platform where the TPM is attached. Recently, Chen proposed a new DAA scheme that is more efficient than previous DAA schemes. In this paper, we construct a new DAA scheme requiring even fewer TPM resources. Our DAA scheme is about 5 times more efficient than Chen’s scheme for the TPM implementation using the Barreto-Naehrig curves. In addition, our scheme requires much smaller size of software code that needs to be implemented in the TPM. This makes our DAA scheme ideal for the TPM implementation. Our DAA scheme is efficient and provably secure in the random oracle model under the strong Diffie-Hellman assumption and the decisional Diffie-Hellman assumption.
Universally Composable Adaptive Oblivious Transfer.

In an oblivious transfer (OT) protocol, a Sender with messages M1, . . . ,MN and a Receiver with indices σ1, . . . , σk ∈ [1, N ] interact in such a way that at the end the Receiver obtains Mσ1 , . . . ,Mσk without learning anything about the other messages and the Sender does not learn anything about σ1, . . . , σk. In an adaptive protocol, the Receiver may obtain Mσi−1 before deciding on σi. Efficient adaptive OT protocols are interesting both as a building block for secure multiparty computation and for enabling oblivious searches on medical and patent databases. Historically, adaptive OT protocols were analyzed with respect to a “half-simulation” definition which Naor and Pinkas showed to be flawed. In 2007, Camenisch, Neven, and shelat, and subsequent other works, demonstrated efficient adaptive protocols in the full-simulation model. These protocols, however, all use standard rewinding techniques in their proofs of security and thus are not universally composable. Recently, Peikert, Vaikuntanathan and Waters presented universally composable (UC) non-adaptive OT protocols (for the 1-out-of-2 variant). However, it is not clear how to preserve UC security while extending these protocols to the adaptive k-outof-N setting. Further, any such attempt would seem to require O(N) computation per transfer for a database of size N . In this work, we present an efficient and UC-secure adaptive k-out-of-N OT protocol, where after an initial commitment to the database, the cost of each transfer is constant. Our construction is secure under bilinear assumptions in the standard model.
Protecting Geolocation Privacy of Photo Collections.

People increasingly share personal information, including their photos and photo collections, on social media. This information, however, can compromise individual privacy, particularly as social media platforms use it to infer detailed models of user behavior, including tracking their location. We consider the specific issue of location privacy as potentially revealed by posting photo collections, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. One means to limit associated inadvertent geolocation privacy disclosure is by carefully pruning select photos from photo collections before these are posted publicly. We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning. We first demonstrate the complexity both by showing that a natural greedy algorithm can be arbitrarily bad and by proving that the problem is NP-Hard. We then exhibit an important tractable special case, as well as a more general approach based on mixed-integer linear programming. Through extensive experiments on real photo collections, we demonstrate that our approaches are indeed highly effective at preserving geolocation privacy.
Reverse Engineering Configurations of Neural Text Generation Models

Recent advances in neural text generation modeling have resulted in a number of societal concerns related to how such approaches might be used in malicious ways. It is therefore desirable to develop a deeper understanding of the fundamental properties of such models. The study of artifacts that emerge in machine generated text as a result of modeling choices is a nascent research area. To this end, the extent and degree to which these artifacts surface in generated text is still unclear. In the spirit of better understanding generative text models and their artifacts, we propose the new task of distinguishing which of several variants of a given model generated some piece of text. Specifically, we conduct an extensive suite of diagnostic tests to observe whether modeling choices (e.g., sampling methods, top-k probabilities, model architectures, etc.) leave detectable artifacts in the text they generate. Our key finding, which is backed by a rigorous set of experiments, is that such artifacts are present and that different modeling choices can be inferred by looking at generated text alone. This suggests that neural text generators may actually be more sensitive to various modeling choices than previously thought.
Optimal Sampling for Generalized Linear Models Under Measurement Constraints

Under “measurement constraints,” responses are expensive to measure and initially unavailable on most of records in the dataset, but the covariates are available for the entire dataset. Our goal is...
Extremely Dense Point Correspondences Using a Learned Feature Descriptor

High-quality 3D reconstructions from endoscopy video play an important role in many clinical applications, including surgical navigation where they enable direct video-CT registration. While many methods exist for general multi-view 3D reconstruction, these methods often fail to deliver satisfactory performance on endoscopic video. Part of the reason is that local descriptors that establish pair-wise point correspondences, and thus drive reconstruction, struggle when confronted with the texture-scarce surface of anatomy. Learning-based dense descriptors usually have larger receptive fields enabling the encoding of global information, which can be used to disambiguate matches. In this work, we present an effective self-supervised training scheme and novel loss design for dense descriptor learning. In direct comparison to recent local and dense descriptors on an in-house sinus endoscopy dataset, we demonstrate that our proposed dense descriptor can generalize to unseen patients and scopes, thereby largely improving the performance of Structure from Motion (SfM) in terms of model density and completeness. We also evaluate our method on a public dense optical flow dataset and a small-scale SfM public dataset to further demonstrate the effectiveness and generality of our method. The source code is available at https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch.
Financing Higher Education through Equity, Not Debt: The Case for Income Share Agreements

Student loan debt in the United States is $1.6 trillion and rising. The public debate concerning the human capital value vs. the social capital value of higher education has been shifting toward th...
The Power of Batching in Multiple Hypothesis Testing

One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve significantly higher power of discovery, while the latter allow making decisions sequentially as well as adaptively formulating hypotheses based on past observations. Using existing methodology, it is unclear how one could trade off the benefits of these two broad families of algorithms, all the while preserving their formal FDR guarantees. To this end, we introduce $\text{Batch}_{\text{BH}}$ and $\text{Batch}_{\text{St-BH}}$, algorithms for controlling the FDR when a possibly infinite sequence of batches of hypotheses is tested by repeated application of one of the most widely used offline algorithms, the Benjamini-Hochberg (BH) method or Storey's improvement of the BH method. We show that our algorithms interpolate between existing online and offline methodology, thus trading off the best of both worlds.
US vs. Euro Area: Who drives cross-border bank lending to EMs?

This paper analyzes the drivers of cross-border bank lending to 49 Emerging Markets (EMs) during the period 1990Q1-2014Q4, by assessing the impact of monetary, financial and real sector shocks in both the US and the euro area. The literature has traditionally highlighted the influence of US monetary policy on driving cross-border bank flows, and more recently the importance of both US and Euro Area (EA) financial/banking sectors' related variables. Our contribution is the simultaneous analysis of the role of these US and EA drivers, as well as their interactions with real sector shocks. We corroborate the negative impact of US monetary policy tightening on cross-border lending to EMs, but we find that EA monetary policy seems to have an impact mostly on Emerging Europe, reflecting the fact that cross-border lending to most other EM regions is dollar denominated. We also find that real sector shocks in both the US and EA trigger an increase in cross-border lending, but less in EA when modeling the financial sector. Finally, for financial sector shocks, such as those associated with a decrease in bank leverage, our results indicate a broad-based overall contraction of cross-border lending if the shock originates in the US, and heterogenous effects across borrowing regions if the shock originates in the EA.
A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of connectivity in each analysis. Here, we propose an appro...
Finite generation of the cohomology of some skew group algebras

We prove that some skew group algebras have Noetherian cohomology rings, a property inherited from their component parts. The proof is an adaptation of Evens’ proof of finite generation of group cohomology. We apply the result to a series of examples of finite-dimensional Hopf algebras in positive characteristic.
Near-Optimal Algorithms for Minimax Optimization

This paper resolves a longstanding open question pertaining to the design of near-optimal first-order algorithms for smooth and strongly-convex-strongly-concave minimax problems. Current state-of-the-art first-order algorithms find an approximate Nash equilibrium using $\tilde{O}(\kappax+\kappay)$~\citep{Tseng-1995-Linear} or $\tilde{O}(\min\{\kappax\sqrt{\kappay}, \sqrt{\kappax}\kappay\})$~\citep{Alkousa-2019-Accelerated} gradient evaluations, where $\kappax$ and $\kappay$ are the condition numbers for the strong-convexity and strong-concavity assumptions. A gap still remains between these results and the best existing lower bound $\tilde{\Omega}(\sqrt{\kappax\kappay})$~\citep{Ibrahim-2019-Lower, Zhang-2019-Lower}. This paper presents the first algorithm with $\tilde{O}(\sqrt{\kappax\kappay})$ gradient complexity, matching the lower bound up to logarithmic factors. Our algorithm is designed based on an accelerated proximal point method and an accelerated solver for minimax proximal steps. It can be easily extended to the settings of strongly-convex-concave, convex-concave, nonconvex-strongly-concave, and nonconvex-concave functions. This paper also presents algorithms that match or outperform all existing methods in these settings in terms of gradient complexity, up to logarithmic factors.
Strain Rate and Pressure Effects on Multi-branched Counterflow Flames

Abstract This study presents methane-air counterflow simulations, in computationally efficient similar form, allowing combustible mixtures to flow from one or both directions in order to learn more about multi-branched propagating flame structures (e.g., a triple flame). These structures with both premixed and non-premixed flames are commonly seen in more practical combustion analyses. A range of realistic mass mixture fractions and asymmetric chemical rate laws are examined while avoiding the commonly forced unreal symmetric behavior with one-step second-order kinetics. Moreover, a survey of critical parameters is performed varying pressure and normal strain rate to define the flame structure and detect different characters. Three flames can co-exist if the strain rate is low enough and the pressure is high enough. However, at higher strain rate and/or lower pressure, only one or two flames might be obtained. Negative regions of heat release rate are observed and linked to potential endothermic reactions. With a rich premixed mixture at low strain rates and pressures, high exothermic reactions producing CO2 and H2O, and consuming CO and H2 cause a heat-release-rate peak. Unexpected character of the lean and rich premixed flames is observed, leading to the conclusion that these flames are diffusion controlled.
Monopsony in Movers: The Elasticity of Labor Supply to Firm Wage Policies

We provide new estimates of the separations elasticity, a proximate determinant of the labor supply facing a firm with respect to hourly wage, using matched Oregon employer-employee data. Existing estimates using individual wage variation may be biased by mismeasured wages and use of wage variation unrelated to firm choices. We estimate the impact of the firm component of wage variation on separations using both firm fixed effects estimated from a wage equation as well as a matched IV event study around employment transitions between firms. Separations are a declining function of firm wage policies: we find that the implied firm-level labor supply elasticities generated are around 4, consistent with recent experimental and quasi-experimental evidence, and that they are approximately 3 to 4 times larger that those using individual wages. Further, we find lower separations elasticities for low wage workers, high turnover sectors, and periods of economic downturn but with little heterogeneity by urban status or labor market concentration. We conclude that monopsonistic competition is pervasive, and largely independent of forces driving classical monopsony.
Multireference Description of Nickel–Aryl Homolytic Bond Dissociation Processes in Photoredox Catalysis

Multireference electronic structure calculations consistent with known experimental data have elucidated a novel mechanism for photo-triggered Ni(II)-C homolytic bond dissociation in Ni 2,2'-bipyridine (bpy) photoredox catalysts. Previously, a thermally assisted dissociation from the lowest energy triplet ligand field excited state was proposed and supported by density functional theory (DFT) calculations that reveal a barrier of ∼30 kcal mol-1. In contrast, multireference ab initio calculations suggest that this process is disfavored, with barrier heights of ∼70 kcal mol-1, and highlight important ligand noninnocent and multiconfigurational contributions to excited state relaxation and bond dissociation processes that are not captured with DFT. In the multireference description, photo-triggered Ni(II)-C homolytic bond dissociation occurs via initial population of a singlet Ni(II)-to-bpy metal-to-ligand charge transfer (1MLCT) excited state, followed by intersystem crossing and aryl-to-Ni(III) charge transfer, overall a formal two-electron transfer process driven by a single photon. This results in repulsive triplet excited states from which spontaneous homolytic bond dissociation can occur, effectively competing with relaxation to the lowest energy nondissociative triplet Ni(II) ligand field excited state. These findings guide important electronic structure considerations for the experimental and computational elucidation of the mechanisms of ground and excited state cross-coupling catalysis mediated by Ni heteroaromatic complexes.
Analysis of a submicroampere laser diode

Summary form only given. Laser resonators with microscopic dimensions approaching the optical wavelength exhibit fundamentally new phenomena. Within microcavity laser diodes, reduced threshold currents are perhaps the most conspicuous token predicted for this regime. Here we discuss the operation of an oxide-confined vertical-cavity surface-emitting laser (VCSEL) featuring a submicroampere threshold current. Several measurements including polarization, linewidth, optical beam profile, and light-current characteristics confirm that the device is operating as a laser at currents less than 1 /spl mu/A. Spectroscopic and far-field measurements are used to analyze the laser's modal structure and operation.
Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection.

In this paper, we investigate algorithms for anomaly detection. Previous anomaly detection methods focus on modeling the distribution of non-anomalous data provided during training. However, this does not necessarily ensure the correct detection of anomalous data. We propose a new Regularized Cycle Consistent Generative Adversarial Network (RCGAN) in which deep neural networks are adversarially trained to better recognize anomalous samples. This approach is based on leveraging a penalty distribution with a new definition of the loss function and novel use of discriminator networks. It is based on a solid mathematical foundation, and proofs show that our approach has stronger guarantees for detecting anomalous examples compared to the current state-of-the-art. Experimental results on both real-world and synthetic data show that our model leads to significant and consistent improvements on previous anomaly detection benchmarks. Notably, RCGAN improves on the state-of-the-art on the KDDCUP, Arrhythmia, Thyroid, Musk and CIFAR10 datasets.
Non-Coherent Detection and Bit Error Rate for an Ambient Backscatter Link in Time-Selective Fading

This paper focuses on the non-coherent detection in ambient backscatter communication, which is highly appealing for systems where the trade-off between signaling overhead and the actual data transmission is very critical. Modeling the time-selective fading channel as a first-order autoregressive (AR) process, we propose a new receiver architecture based on the direct averaging of the received signal samples for detection, which departs significantly from the energy averaging-based receivers considered in the literature. For the proposed setup, we characterize the exact asymptotic bit error rate (BER) for both single-antenna (SA) and multi-antenna (MA) receivers, and demonstrate the robustness of the new architecture to timing errors. Our results demonstrate that while the direct-link (DL) interference from the ambient power source leads to a BER floor in the SA receiver, the MA receiver can remove this interference by estimating the angle of arrival (AoA) of the DL. The analysis further quantifies the effect of improved angular resolution on the BER as a function of the number of receive antennas. A key intermediate result of our analysis is the derivation of a new concentration result for a general sum sequence that is central to the derivation of the conditional distributions of the received signal.
LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts

We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. Line charts are commonly used to visualize a series of data samples. When the number of samples is large, or the data are noisy, smoothing can be applied to make the signal more apparent. However, there are a wide variety of smoothing techniques available, and the effectiveness of each depends upon both nature of the data and the visual analytics task at hand. To date, the visualization community lacks a summary work for analyzing and classifying the various smoothing methods available. In this paper, we establish a framework, based on 8 measures of the line smoothing effectiveness tied to 8 low-level visual analytics tasks. We then analyze 12 methods coming from 4 commonly used classes of line chart smoothing-rank filters, convolutional filters, frequency domain filters, and subsampling. The results show that while no method is ideal for all situations, certain methods, such as G aussian filters and TOPOLOGY-based subsampling, perform well in general. Other methods, such as low-pass CUTOFF filters and D ouglas-peucker subsampling, perform well for specific visual analytics tasks. Almost as importantly, our framework demonstrates that several methods, including the commonly used UNIFORM subsampling, produce low-quality results, and should, therefore, be avoided, if possible.
Learning graph representations of biochemical networks and its application to enzymatic link prediction.

MOTIVATION The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, enzymatic link prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions cataloged in the KEGG database as a graph. ELP is innovative over prior works in using graph embedding to learn molecular representations that capture not only molecular and enzymatic attributes but also graph connectivity. RESULTS We explore transductive (test nodes included in the training graph) and inductive (test nodes not part of the training graph) learning models. We show that ELP achieves high AUC when learning node embeddings using both graph connectivity and node attributes. Further, we show that graph embedding improves link prediction by 30% in area under curve over fingerprint-based similarity approaches and by 8% over support vector machines. We compare ELP against rule-based methods. We also evaluate ELP for predicting links in pathway maps and for reconstruction of edges in reaction networks of four common gut microbiota phyla: actinobacteria, bacteroidetes, firmicutes and proteobacteria. To emphasize the importance of graph embedding in the context of biochemical networks, we illustrate how graph embedding can guide visualization. AVAILABILITY AND IMPLEMENTATION The code and datasets are available through https://github.com/HassounLab/ELP.
Migratable AI: Effect of identity and information migration on users' perception of conversational AI agents

Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We validated the system by designing a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability and social presence. Our results suggest that identity migration had a positive effect on trust, competence and social presence, while information migration had a positive effect on trust, competence and likeability. Overall, users report highest trust, competence, likeability and social presence towards the conversational agent when both identity and information were migrated across embodiments.
Heterogeneous Tiebout Communities with Private Production and Anonymous Crowding

Abstract This paper provides a general equilibrium model where jurisdictions offer not only public goods, but also job opportunities. In a context of multiple types of consumers, labor complementarities, and anonymous crowding, heterogeneous populated communities form in equilibrium with an endogenous wage system that is labor-type and jurisdiction-type dependent. Equilibrium jurisdiction structures depend on the relative scarcity of labor types, unlike the situation in Berglas' (1976) partial equilibrium analysis. For a large economy, we prove that equilibrium exists and that the set of equilibria is equivalent to the core.
Précis of philosophy within its proper bounds

The goal of Philosophy Within Its Proper Bounds (Machery 2017) is to curb philosophers’ flights of fancy and to reorient contemporary philosophy toward humbler, but more productive questions. A lar...
CNN-based Speed Detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors

In recent years, there have been a surge in ubiquitous technologies such as smartwatches and fitness trackers that can track the human physical activities effortlessly. These devices have enabled common citizens to track their physical fitness and encourage them to lead a healthy lifestyle. Among various exercises, walking and running are the most common ones people do in everyday life, either through commute, exercise, or doing household chores. If done at the right intensity, walking and running are sufficient enough to help individual reach the fitness and weight-loss goals. Therefore, it is important to measure walking/ running speed to estimate the burned calories along with preventing them from the risk of soreness, injury, and burnout. Existing wearable technologies use GPS sensor to measure the speed which is highly energy inefficient and does not work well indoors. In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and gyroscope sensory data from the wrist-worn device to detect the speed with high precision. Data from 15 participants were collected while they were walking/running at different speeds on a treadmill. Our speed detection algorithm achieved 4.2% and 9.8% MAPE (Mean Absolute Error Percentage) value using 70-15-15 train-test-evaluation split and leave-one-out cross-validation evaluation strategy respectively.
Item Response Theory Models for Polytomous Response Data

Many measurement tools are constructed of items that can be scored in several categories. For example, essay items, or other constructed response items, or personality and attitude inventories composed of Likert-type items. Several item response models have been developed to deal with items of this type. The models are extensions of the dichotomous items presented in this volume. The five models discussed are the Graded Response Model, the Rating Scale Model, the Partial Credit Model, the Generalized Partial Credit Model, and the Nominal Response Model. Descriptions of the models are provided, along with graphical representations, and descriptions of how to interpret the parameters. The entry concludes with a list of articles that have applied these models in different contexts. Keywords: polytomous IRT models; nominal response model; graded response model; partial credit model; generalized partial credit model
Learning Intonation Pattern Embeddings for Arabic Dialect Identification.

This article presents a full end-to-end pipeline for Arabic Dialect Identification (ADI) using intonation patterns and acoustic representations. Recent approaches to language and dialect identification use linguistic-aware deep architectures that are able to capture phonetic differences amongst languages and dialects. Specifically, in ADI tasks, different combinations of linguistic features and acoustic representations have been successful with deep learning models. The approach presented in this article uses intonation patterns and hybrid residual and bidirectional LSTM networks to learn acoustic embeddings with no additional linguistic information. Results of the experiments show that intonation patterns for Arabic dialects provide sufficient information to achieve state-of-the-art results on the VarDial 17 ADI dataset, outperforming single-feature systems. The pipeline presented is robust to data sparsity, in contrast to other deep learning approaches that require large quantities of data. We conjecture on the importance of sufficient information as a criterion for optimality in a deep learning ADI task, and more generally, its application to acoustic modeling problems. Small intonation patterns, when sufficient in an information-theoretic sense, allow deep learning architectures to learn more accurate speech representations.
Concerning the Wave equation on Asymptotically Euclidean Manifolds

We obtain KSS, Strichartz and certain weighted Strichartz estimates for the wave equation on (ℝ d , g), d ≥ 3, when the metric g is non-trapping and approaches the Euclidean metric like 〈x〉−ρ with ρ > 0. Using the KSS estimate, we prove almost global existence for quadratically semilinear wave equations with small initial data for ρ > 1 and d = 3. Also, we establish the Strauss conjecture when the metric is radial with ρ > 1 for d = 3.
CONSTRAINTS ON THE INITIAL-FINAL MASS RELATION FROM WIDE DOUBLE WHITE DWARFS

We present observational constraints on the initial-final mass relation (IFMR) using wide double white dwarfs (DWDs). We identify 65 new candidate wide DWDs within the Sloan Digital Sky Survey, bringing the number of candidate wide DWDs to 142. We then engage in a spectroscopic follow-up campaign and collect existing spectra for these objects; using these spectra, we derive masses and cooling ages for 54 hydrogen (DA) WDs in DWDs. We also identify one new DA/DB pair, four candidate DA/DC pairs, four candidate DA/DAH pairs, and one new candidate triple degenerate system. Because wide DWDs are co-eval and evolve independently, the difference in the pre-WD lifetimes should equal the difference in the WD cooling ages. We use this to develop a Bayesian hierarchical framework and construct a likelihood function to determine the probability that any particular IFMR fits a sample of wide DWDs. We then define a parametric model for the IFMR and find the best parameters indicated by our sample of DWDs. We place robust constraints on the IFMR for initial masses of 2--4 \Msun. The WD masses produced by our model for stars within this mass range differ from those predicted by semi-empirical fits to open cluster WDs. Within this mass range, where there are few constraining open cluster WDs and disagreements in the cluster ages, wide DWDs may provide more reliable constraints on the IFMR. Expanding this method to the many wide DWDs expected to be discovered by Gaia may transform our understanding of the IFMR.
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called “CoaCor”), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.1
On the Smoothness of the Partition Function for Multiple Schramm-Loewner Evolutions

We consider the measure on multiple chordal Schramm–Loewner evolution ( $$\textit{SLE}_\kappa $$ ) curves. We establish a derivative estimate and use it to give a direct proof that the partition function is $$C^2$$ if $$\kappa < 4$$ .