cssi/SciSciGPT-SciSciNet huggingface dataset:
Large Scale SfM with the Distributed Camera Model

We introduce the distributed camera model, a novel model for Structure-from-Motion (SfM). This model describes image observations in terms of light rays with ray origins and directions rather than pixels. As such, the proposed model is capable of describing a single camera or multiple cameras simultaneously as the collection of all light rays observed. We show how the distributed camera model is a generalization of the standard camera model and we describe a general formulation and solution to the absolute camera pose problem that works for standard or distributed cameras. The proposed method computes a solution that is up to 8 times more efficient and robust to rotation singularities in comparison with gDLS[21]. Finally, this method is used in an novel large-scale incremental SfM pipeline where distributed cameras are accurately and robustly merged together. This pipeline is a direct generalization of traditional incremental SfM, however, instead of incrementally adding one camera at a time to grow the reconstruction the reconstruction is grown by adding a distributed camera. Our pipeline produces highly accurate reconstructions efficiently by avoiding the need for many bundle adjustment iterations and is capable of computing a 3D model of Rome from over 15,000 images in just 22 minutes.
On the universal central extension of hyperelliptic current algebras

Let $p(t)\in\mathbb C[t]$ be a polynomial with distinct roots and nonzero constant term. We describe, using Faa de Bruno's formula and Bell polynomials, the universal central extension in terms of generators and relations for the hyperelliptic current Lie algebras $\mathfrak g\otimes R$ whose coordinate ring is of the form $R=\mathbb C[t,t^{-1},u\,|\, u^2=p(t)]$.
Remembrance: The Unbearable Sentience of Being Digital

We introduce a world vision in which data is endowed with memory. In this data-centric systems paradigm, data items can be enabled to retain all or some of their previous values. We call this ability remembrance and posit that it empowers significant leaps in the security, availability, and general operational dimensions of systems. With the explosion in cheap, fast memories and storage, large-scale remembrance will soon become practical. Here, we introduce and explore the advantages of such a paradigm and the challenges in making it a reality.
The stable moduli space of flat connections over a surface

We compute the homotopy type of the moduli space of flat, unitary connections over aspherical surfaces, after stabilizing with respect to the rank of the underlying bundle. Over the orientable surface M^g, we show that this space has the homotopy type of the infinite symmetric product of M^g, generalizing a well-known fact for the torus. Over a non-orientable surface, we show that this space is homotopy equivalent to a disjoint union of two tori, whose common dimension corresponds to the rank of the first (co)homology group of the surface. Similar calculations are provided for products of surfaces, and show a close analogy with the Quillen-Lichtenbaum conjectures in algebraic K-theory. The proofs utilize Tyler Lawson's work in deformation K-theory, and rely heavily on Yang-Mills theory and gauge theory.
The Grone-Merris Conjecture

In spectral graph theory, the Grone-Merris Conjecture asserts that the spectrum of the Laplacian matrix of a finite graph is majorized by the conjugate degree sequence of this graph. We give a complete proof for this conjecture.
Dusty plasma cavities: Probe-induced and natural.

The dust particles contained in a complex plasma can form two-dimensional crystals, and the authors experimentally study cavities formed in these crystals. They show that cavities that are produced by positively and negatively charged probes, and those that are naturally occurring, are due to three different mechanisms.
Characteristic cohomology of the infinitesimal period relation

The infinitesimal period relation (also known as Griffiths' transversality) is the system of partial differential equations constraining variations of Hodge structure. This paper presents a study of the characteristic cohomology associated with that system of pde.
Backpropagating through Structured Argmax using a SPIGOT

We introduce structured projection of intermediate gradients (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e.g., parsing) in intermediate layers. SPIGOT requires no marginal inference, unlike structured attention networks and reinforcement learning-inspired solutions. Like so-called straight-through estimators, SPIGOT defines gradient-like quantities associated with intermediate nondifferentiable operations, allowing backpropagation before and after them; SPIGOT’s proxy aims to ensure that, after a parameter update, the intermediate structure will remain well-formed. We experiment on two structured NLP pipelines: syntactic-then-semantic dependency parsing, and semantic parsing followed by sentiment classification. We show that training with SPIGOT leads to a larger improvement on the downstream task than a modularly-trained pipeline, the straight-through estimator, and structured attention, reaching a new state of the art on semantic dependency parsing.
A randomized covering-packing duality between source-coding and channel-coding

A randomized covering-packing duality between source and channel coding will be discussed by considering the source coding problem of coding a source with a certain distortion level and by considering a channel which communicates the source within a certain distortion level. An operational view of source-channel separation for communication with a fidelity criterion will be discussed in brief.
MinJoin: Efficient Edit Similarity Joins via Local Hash Minima

We study the problem of computing similarity joins under edit distance on a set of strings. Edit similarity joins is a fundamental problem in databases, data mining and bioinformatics. It finds important applications in data cleaning and integration, collaborative filtering, genome sequence assembly, etc. This problem has attracted significant attention in the past two decades. However, all previous algorithms either cannot scale well to long strings and large similarity thresholds, or suffer from imperfect accuracy. In this paper we propose a new algorithm for edit similarity joins using a novel string partition based approach. We show mathematically that with high probability our algorithm achieves a perfect accuracy, and runs in linear time plus a data-dependent verification step. Experiments on real world datasets show that our algorithm significantly outperforms the state-of-the-art algorithms for edit similarity joins, and achieves perfect accuracy on all the datasets that we have tested.
Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment

Much current study of legged locomotion has rightly focused on foot traction forces, including on granular media. Future legged millirobots will need to go through terrain, such as brush or other vegetation, where the body contact forces significantly affect locomotion. In this work, a (previously developed) low-cost 6-axis force/torque sensing shell is used to measure the interaction forces between a hexapedal millirobot and a set of compliant beams, which act as a surrogate for a densely cluttered environment. Experiments with a VelociRoACH robotic platform are used to measure lift and drag forces on the tactile shell, where negative lift forces can increase traction, even while drag forces increase. The drag energy and specific resistance required to pass through dense terrains can be measured. Furthermore, some contact between the robot and the compliant beams can lower specific resistance of locomotion. For small, light-weight legged robots in the beam environment, the body motion depends on both legground and body-beam forces. A shell-shape which reduces drag but increases negative lift, such as the half-ellipsoid used, is suggested to be advantageous for robot locomotion in this type of environment.
Venue analytics: a simple alternative to citation-based metrics

We present a method for automatically organizing and evaluating the quality of different publishing venues in Computer Science. Since this method only requires paper publication data as its input, we can demonstrate our method on a large portion of the DBLP dataset, spanning 50 years, with millions of authors and thousands of publishing venues. By formulating venue authorship as a regression problem and targeting metrics of interest, we obtain venue scores for every conference and journal in our dataset. The obtained scores can also provide a per-year model of conference quality, showing how fields develop and change over time. Additionally, these venue scores can be used to evaluate individual academic authors and academic institutions. We show that using venue scores to evaluate both authors and institutions produces quantitative measures that are comparable to approaches using citations or peer assessment. In contrast to many other existing evaluation metrics, our use of large-scale, openly available data enables this approach to be repeatable and transparent.
September 11 and the Rise of Necessity Self-Employment Among Mexican Immigrants

Since the September 11 attacks (9/11), the USA has seen a tightening of immigration policies. Previous studies find that stricter immigration enforcement has the unintended effect of pushing undocumented immigrants into self-employment. This paper builds on the literature to better understand the changes in the types of self-employment among Mexican immigrants triggered by the tightened immigration enforcement after 9/11. Using a difference-in-differences approach, and the recently developed measures by Fairlie and Fossen (Opportunity versus necessity entrepreneurship: two components of business creation, 2018) to distinguish between necessity and opportunity self-employment, we find that both necessity and opportunity self-employment increased among Mexican immigrants after 9/11. However, the effect is most prominent on necessity self-employment, consistent with the hypothesis that they are pushed into self-employment as a survival alternative.
International Financial Connection and Stock Return Comovement

This paper studies whether bilateral international financial connection data help predict bilateral stock return comovement. It is shown that, when the United States is chosen as the benchmark, a larger U.S. portfolio investment asset position on the destination economy predicts a stronger stock return comovement between them. For large economies such as the United States and Germany, the portfolio investment position is also the best predictor among other connection variables. The paper discusses with a simple general equilibrium portfolio model that the empirical pattern is consistent with the behavior of index investors who trade in response to risk-on/risk-off shocks.
Simulating the delay in logic networks for large, high-speed computers

When designing a computer with TTL logic circuits, the delays of logic paths have been estimated by considering the number of gate delays and adding in load and media factors. Such a simplistic approach is not accurate enough for calculating delays when designing high-performance large systems using high-speed, non-saturating circuits such as HCML (Honeywell’s Current Mode Logic). There are several reasons: • The clock (cycle) time is considerably faster for a high speed machine, hence the calculations must be very accurate in order to meet performance goals. • The loading on the driving gate varies with the number of driven gates, hence affecting the rise time of the line (interconnect) voltage. • The geometry of the interconnect (branch points, connectors, various media impedances) has an effect on signal propagation with high-speed edges. • Media delay is a significant percentage of path delay as ICs become faster.
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation

Recent work in variational inference (VI) has used ideas from Monte Carlo estimation to obtain tighter lower bounds on the log-likelihood to be used as objectives for VI. However, there is not a systematic understanding of how optimizing different objectives relates to approximating the posterior distribution. Developing such a connection is important if the ideas are to be applied to inference—i.e., applications that require an approximate posterior and not just an approximation of the log-likelihood. Given a VI objective defined by a Monte Carlo estimator of the likelihood, we use a "divide and couple" procedure to identify augmented proposal and target distributions so that the gap between the VI objective and the log-likelihood is equal to the divergence between these distributions. Thus, after maximizing the VI objective, the augmented variational distribution may be used to approximate the posterior distribution.
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution

Blind deconvolution is the problem of recovering a convolutional kernel $\boldsymbol{a}_0$ a 0 and an activation signal $\boldsymbol{x}_0$ x 0 from their convolution $\boldsymbol{y} = \boldsymbol{a}_0 \circledast \boldsymbol{x}_0$ y = a 0 ⊛ x 0 . This problem is ill-posed without further constraints or priors. This paper studies the situation where the nonzero entries in the activation signal are sparsely and randomly populated. We normalize the convolution kernel to have unit Frobenius norm and cast the sparse blind deconvolution problem as a nonconvex optimization problem over the sphere. With this spherical constraint, every spurious local minimum turns out to be close to some signed shift truncation of the ground truth, under certain hypotheses. This benign property motivates an effective two stage algorithm that recovers the ground truth from the partial information offered by a suboptimal local minimum. This geometry-inspired algorithm recovers the ground truth for certain microscopy problems, also exhibits promising performance in the more challenging image deblurring problem. Our insights into the global geometry and the two stage algorithm extend to the convolutional dictionary learning problem, where a superposition of multiple convolution signals is observed.
PaKman: Scalable Assembly of Large Genomes on Distributed Memory Machines

De novo genome assembly is a fundamental problem in the field of bioinformatics, that aims to assemble the DNA sequence of an unknown genome from numerous short DNA fragments (aka reads) obtained from it. With the advent of high-throughput sequencing technologies, billions of reads can be generated in a matter of hours, necessitating efficient parallelization of the assembly process. While multiple parallel solutions have been proposed in the past, conducting a large-scale assembly at scale remains a challenging problem because of the inherent complexities associated with data movement, and irregular access footprints of memory and I/O operations. In this paper, we present a novel algorithm, called PaKman, to address the problem of performing large-scale genome assemblies on a distributed memory parallel computer. Our approach focuses on improving performance through a combination of novel data structures and algorithmic strategies for reducing the communication and I/O footprint during the assembly process. PaKman presents a solution for the two most time-consuming phases in the full genome assembly pipeline, namely, k-mer counting and contig generation. A key aspect of our algorithm is its graph data structure, which comprises fat nodes (or what we call "macro-nodes") that reduce the communication burden during contig generation. We present an extensive performance and qualitative evaluation of our algorithm, including comparisons to other state-of-the-art parallel assemblers. Our results demonstrate the ability to achieve near-linear speedups on up to 8K cores (tested); outperform state-of-the-art distributed memory and shared memory tools in performance while delivering comparable (if not better) quality; and reduce time to solution significantly. For instance, PaKman is able to generate a high-quality set of assembled contigs for complex genomes such as the human and wheat genomes in a matter of minutes on 8K cores.
Standardizing the determination and interpretation of Pcrit in fishes.

ABSTRACT The critical oxygen tension (Pcrit) for fishes is the oxygen level below which the rate of oxygen consumption (ṀO2) becomes dependent upon ambient oxygen partial pressure (PO2). We compare multiple curve-fitting approaches to estimate Pcrit of the Gulf killifish, Fundulus grandis, during closed and intermittent-flow respirometry. Fitting two line segments of ṀO2 versus PO2 produced high and variable estimates of Pcrit, as did nonlinear regression using a hyperbolic (Michaelis–Menten) function. Using nonlinear regression fit to an exponential (modified Weibull) function, or linear regression of ṀO2 versus PO2 at low PO2, and determining Pcrit as the PO2 when ṀO2 equals standard metabolic rate (SMR) yielded values that were consistent across fish and among experimental trials. The magnitude of the difference in Pcrit determined by alternative calculation methods exceeded the differences determined in closed and intermittent-flow respirometry, highlighting the need to standardize analytical as well as experimental approaches in determining Pcrit.
Inhibition of tropomyosine receptor kinase B on the migration of human Schwann cell and dispersion of oral tongue squamous cell carcinoma in vitro.

Background Schwann cells (SC) may play an important role in perineural invasion (PNI) by promoting cancer cell dispersion. Brain-derived neurotrophic factor (BDNF) may contribute to these cellular events by activating tropomyosine receptor kinase B (TrkB). This study examines the effect of TrkB inhibition on SC migration and oral cancer cell dispersion in vitro. Methods Human tongue squamous cell carcinoma (SCC-9) and human SCs were cocultured in three different conditioned mediums: control, BDNF, and TrkB inhibitor. Cell migration, cancer cell dispersion, and SC dedifferentiation were measured on time-lapse and immunofluorescence images. Results Cancer cell migration exceeded SC migration in all conditions. TrkB inhibition promoted SC dedifferentiation and significantly increased SC migration, when compared to BDNF conditions. TrkB inhibition also reduced cancer cell dispersion, when compared to control and BDNF-treated cultures. Conclusion SCs may have importance in the pathophysiology of PNI. TrkB inhibition may be a potential avenue for therapeutic intervention.
Deep neural networks for choice analysis: Extracting complete economic information for interpretation

Abstract While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information from DNNs includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives, elasticities, marginal rates of substitution, and heterogeneous values of time. Unlike DCMs, DNNs can automatically learn utility functions and reveal behavioral patterns that are not prespecified by domain experts, particularly when the sample size is large. However, the economic information obtained from DNNs can be unreliable when the sample size is small, because of three challenges associated with the automatic learning capacity: high sensitivity to hyperparameters, model non-identification, and local irregularity. The first challenge is related to the statistical challenge of balancing approximation and estimation errors of DNNs, the second to the optimization challenge of identifying the global optimum in the DNN training, and the third to the robustness challenge of mitigating locally irregular patterns of estimated functions. To demonstrate the strength and challenges, we estimated the DNNs using a stated preference survey from Singapore and a revealed preference data from London, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs. We found that the economic information either aggregated over trainings or population is more reliable than the disaggregate information of the individual observations or trainings, and that larger sample size, hyperparameter searching, model ensemble, and effective regularization can significantly improve the reliability of the economic information extracted from the DNNs. Future studies should investigate the requirement of sample size, better ensemble mechanisms, other regularizations and DNN architectures, better optimization algorithms, and robust DNN training methods to address DNNs three challenges to provide more reliable economic information for DNN-based choice models.
Fuzzy logic interpretation of quadratic networks

Abstract Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed artificial quadratic neural networks consisting of quadratic neurons in potentially many layers. In cellular level, a quadratic function is used to replace the inner product in a traditional neuron, and then undergoes a nonlinear activation. With a single quadratic neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and quadratic counterparts can represent each other and fuzzy logic operations are naturally implemented in quadratic neural networks, it is plausible to explain how a deep neural network works with a quadratic network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual quadratic neurons, and then perform statistical/information-theoretic analyses of exemplary quadratic neural networks.
Thin film synthesis of semiconductors in the Mg-Sb-N materials system

Nitrides feature many interesting properties, such as a wide range of bandgaps suitable for optoelectronic devices including light-emitting diodes (LEDs), and piezoelectric response used in microel...
Credit supply and capital structure adjustments

Using the staggered deregulation of the U.S. banking industry as a series of exogenous shocks, I study the effect of the credit supply on the speed of capital structure adjustment. I find robust evidence that interstate and intrastate banking deregulation are positively associated with leverage adjustments. Specifically, the speeds of adjustment to target leverage are faster in the post-deregulation periods. I also find that the positive effect is driven by firms that are financially constrained, are financially dependent on banks, and have less access to the public debt market and by deregulated banks’ ability to geographically diversify the credit risk.
Micromachined Integrated Quantum Circuit Containing a Superconducting Qubit

The next step in building quantum computers is to develop hardware architectures that allow scalability without sacrificing coherence. Future designs seem headed toward multilayered circuits with embedded three-dimensional structures and superconducting coatings. The authors demonstrate a multilayered integrated quantum circuit, combining a micromachined microwave cavity resonator with a superconducting qubit. They address the mechanism of coupling and its control, engineering considerations that affect coherence times, and challenges in fabrication such as superconductor bonding.
The trouble with Article 25 (and how to fix it): the future of data protection by design and default

In its simplest formulation, data protection by design and default uses technical and organizational measures to achieve data protection goals. Although privacy regulators have endorsed privacy-enhancing technologies or PETs for well over thirty years, Article 25 of the General Data Protection Regulation (GDPR) breaks new ground by transforming this idea into a binding legal obligation. But Article 25 as presently conceived is poorly aligned with privacy engineering methods and related privacy-enhancing technologies (PETs). This is especially true of “hard” PETs that place limited trust in third parties (including data controllers) and instead rely on cryptographic techniques to achieve data minimisation. In order to advance data protection in its own right rather than merely reinforce the general principles of the GDPR, Article 25 must be interpreted as requiring the implementation of privacy engineering and hard PETs. A bold way to achieve this is by mandating that data controllers use available hard PETs for data minimisation. More gradual steps include data protection regulators insisting on a central role for privacy engineering and PETs in public sector projects; issuing guidance on Article 25 in very forceful terms that clearly require the implementation of “state of the art” privacy technology; and using their enforcement powers to reward good examples of privacy engineering rather than to penalize failures. NB: This is a pre-copyedited, preprint version of an article accepted for publication in International Data Privacy Law following peer review. The final and updated version was published in International Data Privacy Law, Volume 10, Issue 1, February 2020, Pages 37–56, https://doi.org/10.1093/idpl/ipz019.
Cooperative Asynchronous Non-Orthogonal Multiple Access With Power Minimization Under QoS Constraints

Recent studies have demonstrated the superiority of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in cooperative communication networks. In this paper, we propose a novel half-duplex cooperative asynchronous NOMA (C-ANOMA) framework with user relaying, where a timing mismatch is intentionally added in the broadcast signal. We derive the expressions for the throughputs of the strong user (acts as relay) which employs the block-wise successive interference cancellation (SIC) and the weak user which combines the symbol-asynchronous signal with the interference-free signal. We analytically prove that in the C-ANOMA systems with a sufficiently large block length, the strong user attains the same throughput to decode its own message while both users can achieve a higher throughput to decode the weak user’s message compared with those in the cooperative NOMA (C-NOMA) systems. Besides, we obtain the optimal timing mismatch when the block length goes to infinity. Furthermore, to exploit the trade-off between the power consumption of the base station and that of the relay user, we solve a weighted sum power minimization problem under quality of services (QoS) constraints. Numerical results show that the C-ANOMA system can consume less power compared with the C-NOMA system to satisfy the same QoS requirements.
Predicting Short-term Risk of Stroke After TIA

Five to 10 percent of patients presenting with TIA will have a stroke within the following week. Because guidelines do not mandate hospitalization for patients who have had a TIA or suspected TIA,4,5 validated clinical prediction rules may be used to identify patients who should definitely be hospitalized for expedited evaluation and patients for whom outpatient evaluation is a reasonable option.
Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

Single-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.
Fifth Indifference: Clarifying the Fifth Circuit's Intent Standard for Damages Under Title II of the Americans with Disabilities Act

The Americans with Disabilities Act prohibits discrimination against people with disabilities. Title II of the ADA applies to public entities. That same Title allows plaintiffs to obtain damages upon a showing that the discrimination was intentional. There are generally two possible standards of intent: (1) deliberate indifference or (2) animus. While most Circuit Courts expressly adopted the deliberate indifference model, the Fifth Circuit has not. Indeed, the Fifth Circuit has not adopted any standard and this has led to confusion. The confusion is not helped, moreover, by the sheer lack of justification offered by a number of the Circuit Courts who have adopted either standard. This Essay seeks to clear that confusion. It offers reasons why deliberate indifference, and not the animus standard, should apply to ADA Title II claims. Further, it explains why no Fifth Circuit precedent should be construed as prohibiting the adoption of the deliberate indifference standard.
A Streaming Analytics Language for Processing Cyber Data

We present a domain-specific language called SAL(the Streaming Analytics Language) for processing data in a semi-streaming model. In particular we examine the use case of processing netflow data in order to identify malicious actors within a network. Because of the large volume of data generated from networks, it is often only feasible to process the data with a single pass, utilizing a streaming (O(polylog n) space requirements) or semi-streaming computing model ( O(n polylog n) space requirements). Despite these constraints, we are able to achieve an average of 0.87 for the AUC of the ROC curve for a set of situations dealing with botnet detection. The implementation of an interpreter for SAL, which we call SAM (Streaming Analytics Machine), achieves scaling results that show improved throughput to 61 nodes (976 cores), with an overall rate of 373,000 netflows per second or 32.2 billion per day. SAL provides a succinct way to describe common analyses that allow cyber analysts to find data of interest, and SAM is a scalable interpreter of the language.
The Fixed Point Property for Posets of Small Width

The fixed point property for finite posets of width 3 and 4 is studied in terms of forbidden retracts. The ranked forbidden retracts for width 3 and 4 are determined explicitly. The ranked forbidden retracts for the width 3 case that are linearly indecomposable are examined to see which are minimal automorphic. Part of a problem of Niederle from 1989 is thus solved.
Linearity of stability conditions

For modules over an artin algebra, a linear stability condition is given by a “central charge” and a nonlinear stability condition is given by the wall-crossing sequence of a “green path.” Finite H...
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning

Although imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which attempts to quantity the confidence of the novice policy as a proxy for safety. Our method, EnsembleDAgger, approximates a Gaussian Process using an ensemble of neural networks. Using the variance as a measure of confidence, we compute a decision rule that captures how much we doubt the novice, thus determining when it is safe to allow the novice to act. With this approach, we aim to maximize the novice’s share of actions, while constraining the probability of failure. We demonstrate improved safety and learning performance compared to other DAgger variants and classic imitation learning on an inverted pendulum and in the MuJoCo HalfCheetah environment.
Supernovae from Rotating Stars

The present paper discusses the main physical effects produced by stellar rotation on presupernovae, as well as observations which confirm these effects and their consequences for presupernova models. Rotation critically influences the mass of the exploding cores, the mass and chemical composition of the envelopes and the types of supernovae, as well as the properties of the remnants and the chemical yields. In the formation of gamma-ray bursts, rotation and the properties of rotating stars appear as the key factor. In binaries, the interaction between axial rotation and tidal effects often leads to interesting and unexpected results. Rotation plays a key role in shaping the evolution and nucleosynthesis in massive stars with very low metallicities (metallicity below about the Small Magellanic Cloud metallicity down to Population III stars). At solar and higher metallicities, the effects of rotation compete with those of stellar winds. In close binaries, the synchronisation process can lock the star at a high rotation rate despite strong mass loss and thus both effects, rotation and stellar winds, have a strong impact. In conclusion, rotation is a key physical ingredient of the stellar models and of presupernova stages, and the evolution both of single stars and close binaries. Moreover, important effects are expected along the whole cosmic history.
Up before Dawn : Experimental Evidence from a Cross-Border Trader Training at the Democratic Republic of Congo–Rwanda Border

Small-scale cross-border trade provides opportunities for economic gains in many developing countries. Yet cross-border traders -- many of whom are women -- face harassment and corruption, which can undermine these potential gains. This paper presents evidence from a randomized controlled trial of a training intervention that provided access to information on procedures, tariffs, and rights to small-scale traders to facilitate border crossings, lower corruption, and reduce gender-based violence along the Democratic Republic of Congo (DRC)–Rwanda border. The training reduces bribe payment by 5 percentage points in the full sample and by 27.5 percentage points on average among compliers. The training also reduces the incidence of gender-based violence by 5.4 percentage points (30.5 percentage points among compliers). The paper assesses competing explanations for the impacts using a game-theoretic model based on Hirschman's Exit, Voice, and Loyalty framework. The effects are achieved through early border crossings at unofficial hours (exit) instead of traders' use of voice mechanisms or reduced rent-seeking from border officials. These results highlight the need to improve governance and establish clear cross-border trade regulations, particularly on the DRC side of the border.
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to well-performing, small-sized networks that are more resilient to hardware faults.
Enumerating linear systems on graphs

The divisor theory of graphs views a finite connected graph G as a discrete version of a Riemann surface. Divisors on G are formal integral combinations of the vertices of G, and linear equivalence of divisors is determined by the discrete Laplacian operator for G. As in the case of Riemann surfaces, we are interested in the complete linear system |D| of a divisor D—the collection of nonnegative divisors linearly equivalent to D. Unlike the case of Riemann surfaces, the complete linear system of a divisor on a graph is always finite. We compute generating functions encoding the sizes of all complete linear systems on G and interpret our results in terms of polyhedra associated with divisors and in terms of the invariant theory of the (dual of the) Jacobian group of G. If G is a cycle graph, our results lead to a bijection between complete linear systems and binary necklaces. Our results also apply to a model in which the Laplacian is replaced by an invertible, integral M-matrix.
Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders

Abstract Background Major Depressive Disorder (MDD) is one of the most common mental illnesses and a leading cause of disability worldwide. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale observational studies investigating MDD, its disease development and its interaction with other health outcomes. While there exist methods to classify patients as clear cases or controls, given specific data requirements, there are presently no simple, generalizable, and validated methods to classify an entire patient population into varying groups of depression likelihood and severity. Methods We have tested a simple, pragmatic electronic phenotype algorithm that classifies patients into one of five mutually exclusive, ordinal groups, varying in depression phenotype. Using data from an integrated health system on 278,026 patients from a 10-year study period we have tested the convergent validity of these constructs using measures of external validation, including patterns of psychiatric prescriptions, symptom severity, indicators of suicidality, comorbidity, mortality, health care utilization, and polygenic risk scores for MDD. Results We found consistent patterns of increasing morbidity and/or adverse outcomes across the five groups, providing evidence for convergent validity. Limitations The study population is from a single rural integrated health system which is predominantly white, possibly limiting its generalizability. Conclusion Our study provides initial evidence that a simple algorithm, generalizable to most EHR data sets, provides categories with meaningful face and convergent validity that can be used for stratification of an entire patient population.
Vietnam's Development Success Story and the Unfinished SDG Agenda

Despite starting as one of the poorest countries in the mid-1980s, Vietnam has achieved rapid developmental progress, reaching lower middle-income status in 2010. In line with rapid economic growth, Vietnam has achieved impressive progress towards the Sustainable Development Goals (SDGs) during this time. This paper sheds light on some elements of Vietnam’s success story, highlighting crucial policies in education and electricity sectors. It undertakes a forward-looking costing exercise that focusses on five sectors – education, health, roads, water, and electricity infrastructure. Achieving the remaining SDGs in Vietnam will be a challenge, with total annual additional spending needs in the 5 subsectors estimated at 7 percent of GDP by 2030.
Input Hard Constrained Optimal Covariance Steering

We address the optimal covariance steering (OCS) problem for stochastic discrete linear systems with additive Gaussian noise under state chance constraints and input hard constraints. Because the system state can be unbounded due to the unbounded noise, the state constraints are formulated as probabilistic (chance) constraints, i.e., the maximum probability of constraint violation is constrained. In contrast, because it is hard to interpret the appropriate control action when the control command violates the constraints, probabilistically formulating the control constraints are difficult, and deterministic hard constraints are preferable. In this work we introduce an OCS approach subject to simultaneous state chance constraints and input hard constraints and validate the approach using numerical simulations.
Continuous Representation of Molecules using Graph Variational Autoencoder.

In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is employed to prune the latent space and help the decoder in generating meaningful adjacency tensor of molecules. Other than the potential applicability in drug design and property prediction, we show the superior performance of this technique in comparison to other similar methods based on the SMILES representation of the molecules with RNN based encoder and decoder.
Divergence-Based Introgression Polarization

Introgressive hybridization results in the transfer of genetic material between species, often with fitness implications for the recipient species. The development of statistical methods for detecting the signatures of historical introgression in whole-genome data has been a major area of focus. Although existing techniques are able to identify the taxa that exchanged genes during introgression using a four-taxon system, most methods do not explicitly distinguish which taxon served as donor and which as recipient during introgression (i.e., polarization of introgression directionality). Existing methods that do polarize introgression are often only able to do so when there is a fifth taxon available and that taxon is sister to one of the taxa involved in introgression. Here, we present divergence-based introgression polarization (DIP), a method for polarizing introgression using patterns of sequence divergence across whole genomes, which operates in a four-taxon context. Thus, DIP can be applied to infer the directionality of introgression when additional taxa are not available. We use simulations to show that DIP can polarize introgression and identify potential sources of bias in the assignment of directionality, and we apply DIP to a well-described hominin introgression event.
Brown's criterion and classifying spaces for families

Abstract Let G be a group and F be a family of subgroups closed under conjugation and subgroups. A model for the classifying space E F G is a G-CW-complex X such that every isotropy group belongs to F , and for all H ∈ F the fixed point subspace X H is contractible. The group G is of type F - F n if it admits a model for E F G with n-skeleton with compact orbit space. The main result of the article provides is a characterization of F - F n analogue to Brown's criterion for FP n . As applications we provide criteria for this type of finiteness properties with respect to families to be preserved by finite extensions, a result that contrast with examples of Leary and Nucinkis. We also recover Luck's characterization of property F _ n in terms of the finiteness properties of the Weyl groups.
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making potentially important decisions in high-regret applications. However, despite GNNs’ impressive performance, it has been observed that carefully crafted perturbations on graph structures (or nodes attributes) lead them to make wrong predictions. Presence of these adversarial examples raises serious security concerns. Most of the existing robust GNN design/training methods are only applicable to white-box settings where model parameters are known and gradient based methods can be used by performing convex relaxation of the discrete graph domain. More importantly, these methods are not efficient and scalable which make them infeasible in time sensitive tasks and massive graph datasets. To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner. On several applications, we show that the proposed techniques are significantly less computationally expensive and, in some cases, more robust than the state-of-the-art methods making them suitable to large-scale problems which were out of the reach of traditional robust training methods.
A Non-Reciprocal Lamb-Wave Delay Line Exploiting Acoustoelectric Effect in Single Crystal Germanium

The paper reports on the use of acoustoelectric amplification in single crystal germanium (Ge) to achieve non-reciprocity in radio frequency (RF) delay lines operating based on Lamb waves. Such delay lines are important in attaining integrated full-duplex wireless communication systems, an integral part of forthcoming 5G applications. Fundamental anti-symmetric (A 0 ) and symmetric (S 0 ) Lamb waves are excited in Ge waveguide using a 200nm-thik aluminum nitride (AlN) piezoelectric transducer with unidirectional IDTs. The generated traveling waves are then amplified through deformation potential coupling between the wave and DC accelerated electrons in Ge. Finite element model (FEM) is used to identify A 0 and S 0 modes at 72 MHz and 267 MHz respectively. Analytical models, based on acoustoelectric interaction in single crystal Ge, is used to predict strong amplification of 18 dB/mm (A 0 ) and 26 dB/mm (S 0 ). Fabricated delay lines operating at 70 MHz (A 0 ) and 226 MHz (S 0 ) demonstrate forward transmission amplification of ∼6 dB and ∼7 dB upon application of 35 V and 45 V, respectively. In particular, a non-reciprocal transmission ratio of 20 dB is measured for the S 0 mode, showcasing the prospects of Ge-based RF delay lines in realization of full-duplex front-end modules.
Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding

The versatility of CRISPR-Cas endonucleases as a tool for biomedical research has led to diverse applications in gene editing, programmable transcriptional control, and nucleic acid detection. Most CRISPR-Cas systems, however, suffer from off-target effects and unpredictable nonspecific binding that negatively impact their reliability and broader applicability. To better evaluate the impact of mismatches on DNA target recognition and binding, we develop a massively parallel CRISPR interference (CRISPRi) assay to measure the binding energy between tens of thousands of CRISPR RNA (crRNA) and target DNA sequences. By developing a general thermodynamic model of CRISPR-Cas binding dynamics, our results unravel a comprehensive map of the energetic landscape of nuclease-dead Cas12a (dCas12a) from Francisella novicida as it inspects and binds to its DNA target. Our results reveal concealed thermodynamic factors affecting dCas12a DNA binding, which should guide the design and optimization of crRNA that limits off-target effects, including the crucial role of an extended protospacer adjacent motif (PAM) sequence and the impact of the specific base composition of crRNA–DNA mismatches. Our generalizable approach should also provide a mechanistic understanding of target recognition and DNA binding when applied to other CRISPR-Cas systems.