cssi/SciSciGPT-SciSciNet huggingface dataset:
CSVideoNet: A Real-Time End-to-End Learning Framework for High-Frame-Rate Video Compressive Sensing

This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS). Unlike prior works that perform reconstruction using iterative optimization-based approaches, we propose a noniterative model, named "CSVideoNet", which directly learns the inverse mapping of CS and reconstructs the original input in a single forward propagation. To overcome the limitations of existing CS cameras, we propose a multi-rate CNN and a synthesizing RNN to improve the trade-o. between compression ratio (CR) and spatial-temporal resolution of the reconstructed videos. the experiment results demonstrate that CSVideoNet signi.cantly outperforms state-of-the-art approaches. Without any pre/post-processing, we achieve a 25dB Peak signal-to-noise ratio (PSNR) recovery quality at 100x CR, with a frame rate of 125 fps on a Titan X GPU. Due to the feedforward and high-data-concurrency natures of CSVideoNet, it can take advantage of GPU acceleration to achieve three orders of magnitude speed-up over conventional iterative-based approaches. We share the source code at https://github.com/PSCLab-ASU/CSVideoNet.
Magic-BLAST, an accurate RNA-seq aligner for long and short reads

Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline. Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.
New Weighted Partition Theorems with the Emphasis on the Smallest Part of Partitions

We use the q-binomial theorem, the q-Gauss sum, and the \({}_2\phi _1 \rightarrow {}_2\phi _2\) transformation of Jackson to discover and prove many new weighted partition identities. These identities involve unrestricted partitions, overpartitions, and partitions with distinct even parts. The smallest part of a partition plays an important role in our analysis. This work was motivated in part by the research of Krishna Alladi.
TernGrad: ternary gradients to reduce communication in distributed deep learning

High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary gradients to accelerate distributed deep learning in data parallelism. Our approach requires only three numerical levels {-1,0,1}, which can aggressively reduce the communication time. We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients. Guided by the bound, we propose layer-wise ternarizing and gradient clipping to improve its convergence. Our experiments show that applying TernGrad on AlexNet doesn't incur any accuracy loss and can even improve accuracy. The accuracy loss of GoogLeNet induced by TernGrad is less than 2% on average. Finally, a performance model is proposed to study the scalability of TernGrad. Experiments show significant speed gains for various deep neural networks. Our source code is available1.
On the volume conjecture for cables of knots

We establish the volume conjecture for (m, 2)-cables of the figure 8 knot, when m is odd. For (m, 2)-cables of general knots where m is even, we show that the limit in the volume conjecture depends on the parity of the color (of the Kashaev invariant). There are many cases when the volume conjecture for cables of the figure 8 knot is false if one considers all the colors, but holds true if one restricts the colors to a subset of the set of positive integers.
Distribution-Free Predictive Inference for Regression

AbstractWe develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a prediction band for the response variable using any estimator of the regression function. The resulting prediction band preserves the consistency properties of the original estimator under standard assumptions, while guaranteeing finite-sample marginal coverage even when these assumptions do not hold. We analyze and compare, both empirically and theoretically, the two major variants of our conformal framework: full conformal inference and split conformal inference, along with a related jackknife method. These methods offer different tradeoffs between statistical accuracy (length of resulting prediction intervals) and computational efficiency. As extensions, we develop a method for constructing valid in-sample prediction intervals called rank-one-out conformal inference, which has essentially the same computational efficiency a...
Matrix Coefficients and Iwahori-Hecke Algebra Modules

Abstract We establish a connection between certain unique models, or equivalently unique functionals, for representations of p -adic groups and linear characters of their corresponding Hecke algebras. This allows us to give a uniform evaluation of the image of spherical and Iwahori-fixed vectors in the unramified principal series for this class of models. We provide an explicit alternator expression for the image of spherical vectors under these functionals in terms of the representation theory of the dual group.
Two oscillators in a common heat bath

We show that the case of two oscillators in a common heat bath cannot be reduced to an effective one-body problem. In addition, there is an interaction between the oscillators, even at zero temperature, due to the fluctuations caused in both oscillators by the zero-point oscillations of the electromagnetic field.
Local cohomology with support in ideals of maximal minors

Abstract Suppose that k is a field of characteristic zero, X is an r × s matrix of indeterminates, where r ≤ s , and R = k [ X ] is the polynomial ring over k in the entries of X . We study the local cohomology modules H I i ( R ) , where I is the ideal of R generated by the maximal minors of X . We identify the indices i for which these modules vanish, compute H I i ( R ) at the highest nonvanishing index, i = r ( s − r ) + 1 , and characterize all nonzero ones as submodules of certain indecomposable injective modules. These results are consequences of more general theorems regarding linearly reductive groups acting on local cohomology modules of polynomial rings.
Measures on Boolean algebras

We present a necessary and sufficient condition for a Boolean algebra to carry a finitely additive measure.
Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning.

Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) and multiple (e.g., constrained spherical deconvolution, CSD) fiber population orientations per voxel. However, existing techniques generally exhibit low reproducibility across MRI scanners. Herein, we propose a data-driven technique using a neural network design which exploits two categories of data. First, training data were acquired on three squirrel monkey brains using ex-vivo DW-MRI and histology of the brain. Second, repeated scans of human subjects were acquired on two different scanners to augment the learning of the network proposed. To use these data, we propose a new network architecture, the null space deep network (NSDN), to simultaneously learn on traditional observed/truth pairs (e.g., MRI-histology voxels) along with repeated observations without a known truth (e.g., scan-rescan MRI). The NSDN was tested on twenty percent of the histology voxels that were kept completely blind to the network. NSDN significantly improved absolute performance relative to histology by 3.87% over CSD and 1.42% over a recently proposed deep neural network approach. More-over, it improved reproducibility on the paired data by 21.19% over CSD and 10.09% over a recently proposed deep approach. Finally, NSDN improved generalizability of the model to a third in-vivo human scanner (which was not used in training) by 16.08% over CSD and 10.41% over a recently proposed deep learning approach. This work suggests that data-driven approaches for local fiber reconstruction are more reproducible, informative, precise and offer a novel, practical method for determining these models.
On Multi-Source Networks: Enumeration, Rate Region Computation, and Hierarchy

Recent algorithmic developments have enabled computers to automatically determine and prove the capacity regions of small hypergraph networks under network coding. A structural theory relating network coding problems of different sizes is developed to make the best use of this newfound computational capability. A formal notion of network minimality is developed, which removes components of a network coding problem that are inessential to its core complexity. Equivalence between different network coding problems under relabeling is formalized via group actions, an algorithm which can directly list single representatives from each equivalence class of minimal networks up to a prescribed network size is presented. This algorithm, together with rate region software, is leveraged to create a database containing the rate regions for all minimal network coding problems with five or fewer sources and edges, a collection of 744119 equivalence classes representing more than 9 million networks. In order to best learn from this database, and to leverage it to infer rate regions and their characteristics of networks at scale, a hierarchy between different network coding problems is created with a new theory of combinations and embedding operators.
Efficient active learning of sparse halfspaces

We study the problem of efficient PAC active learning of homogeneous linear classifiers (halfspaces) in R^d , where the goal is to learn a halfspace with low error, using as few label queries as possible. Given the extra assumption that there is a t-sparse halfspace that performs well on the data (t ≪ d), we would like our active learning algorithm to be attribute efficient, i.e. to have label requirements sublinear in d. In this paper, we provide a computationally efficient algorithm that achieves this goal. Under certain distributional assumptions on the data, our algorithm achieves a label complexity of O(t polylog(d, 1/eps)). In contrast, existing algorithms in this setting are either computationally inefficient, or subject to label requirements polynomial in d or 1/eps.
Online optimization in dynamic environments: Improved regret rates for strongly convex problems

In this paper, we address tracking of a time-varying parameter with unknown dynamics. We formalize the problem as an instance of online optimization in a dynamic setting. Using online gradient descent, we propose a method that sequentially predicts the value of the parameter and in turn suffers a loss. The objective is to minimize the accumulation of losses over the time horizon, a notion that is termed dynamic regret. While existing methods focus on convex loss functions, we consider strongly convex functions so as to provide better guarantees of performance. We derive a regret bound that captures the path-length of the time-varying parameter, defined in terms of the distance between its consecutive values. In other words, the bound represents the natural connection of tracking quality to the rate of change of the parameter. We provide numerical experiments to complement our theoretical findings.
Generalized Degrees of Freedom of the Symmetric $K$ User Interference Channel Under Finite Precision CSIT

The generalized degrees of freedom (GDoF) characterization of the symmetric $K$ user interference channel is obtained under finite precision channel state information at the transmitters (CSIT). The symmetric setting is where each cross channel is capable of carrying $\alpha $ DoF, while each direct channel is capable of carrying 1 DoF. Remarkably, under finite precision CSIT the symmetric $K$ user interference channel loses all the GDoF benefits of interference alignment. The GDoF per user diminish with the number of users everywhere except in the very strong (optimal for every receiver to decode all messages) and very weak (optimal to treat all interference as noise) interference regimes. The result stands in sharp contrast to prior work on the symmetric setting under perfect CSIT, where the GDoF per user remain undiminished due to interference alignment. The result also stands in contrast to prior work on a subclass of asymmetric settings under finite precision CSIT, i.e., the topological interference management problem, where interference alignment plays a crucial role and provides substantial GDoF benefits.
The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use

Despite their diversity, languages around the world share a consistent set of properties and distributional regularities. For example, the distribution of word frequencies, the distribution of syntactic dependency lengths, and the presence of ambiguity are all remarkably consistent across languages. We discuss a framework for studying how these system-level properties emerge from local, in-the-moment interactions of rational, pragmatic speakers and listeners. To do so, we derive a novel objective function for measuring the communicative efficiency of linguistic systems in terms of the interactions of speakers and listeners. We examine the behavior of this objective in a series of simulations focusing on the communicative function of ambiguity in language. These simulations suggest that rational pragmatic agents will produce communicatively efficient systems and that interactions between such agents provide a framework for examining efficient properties of language structure and use more broadly.
On Maximizing Task Throughput in IoT-Enabled 5G Networks Under Latency and Bandwidth Constraints

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory test-bed and comparing with the state-of-the-art algorithm.
Sorted Top-k in Rounds

We consider the sorted top-$k$ problem whose goal is to recover the top-$k$ items with the correct order out of $n$ items using pairwise comparisons. In many applications, multiple rounds of interaction can be costly. We restrict our attention to algorithms with a constant number of rounds $r$ and try to minimize the sample complexity, i.e. the number of comparisons. When the comparisons are noiseless, we characterize how the optimal sample complexity depends on the number of rounds (up to a polylogarithmic factor for general $r$ and up to a constant factor for $r=1$ or 2). In particular, the sample complexity is $\Theta(n^2)$ for $r=1$, $\Theta(n\sqrt{k} + n^{4/3})$ for $r=2$ and $\tilde{\Theta}\left(n^{2/r} k^{(r-1)/r} + n\right)$ for $r \geq 3$. We extend our results of sorted top-$k$ to the noisy case where each comparison is correct with probability $2/3$. When $r=1$ or 2, we show that the sample complexity gets an extra $\Theta(\log(k))$ factor when we transition from the noiseless case to the noisy case. We also prove new results for top-$k$ and sorting in the noisy case. We believe our techniques can be generally useful for understanding the trade-off between round complexities and sample complexities of rank aggregation problems.
Design Parameters for a Small-Gauge Fragmatome.

Purpose Manufacturers of surgical instrumentation have increasingly sought to decrease the size of ophthalmic surgical instruments. We have used finite element modeling to model the stress and strain present in a fragmatome as a function of driving frequency and fragmatome dimensions. Methods Finite element calculations using the COMSOL Multiphysics system v3.5 were used to elucidate the influence of wall thickness, length, and excitation frequency on a titanium fragmatome tube with outer diameters of 20, 23, 25, and 27 gauge. Results By coupling structural mechanics, fluid mechanics, and acoustical physics, we were able to determine the eigenfrequencies (resonant frequencies) as well as parameters in which the von Mises stress in a fragmatome tube exceeds the yield strength, leading to destruction of the instrument. Conclusion Solid fragmatomes have far fewer possible failure modes than fragmatomes with a standard wall thickness. Eigenfrequency analysis and finite element calculations can be critical in predicting potentially catastrophic designs in modern surgical instruments. Translational Relevance Instruments developed for microsurgical applications cannot always simply be scaled down versions of conventional instruments. Such an approach can lead to potentially dangerous intraoperative failures, such as a fragmatome shattering inside the eye. Modern engineering techniques are increasingly necessary to investigate potential instrument failure mechanisms and to optimize device performance in a design in silico before in vivo testing.
Temperature dependence of nylon and PTFE triboelectrification

Experiments pressing two materials together show that static electricity accumulates when surface water lets ions move from one surface to another.
Development of a vanillate biosensor for the vanillin biosynthesis pathway in E. coli

The engineered de novo vanillin biosynthesis pathway constructed in Escherichia coli is industrially relevant but limited by the reaction catalyzed by catechol O-methyltransferase, which is intended to catalyze the conversion of protocatechuate to vanillate. To identify alternative O-methyltransferases, we constructed a vanillate sensor based on the Caulobacter crescentus VanR-VanO system. Using an E. coli promoter library, we achieved greater than 14-fold dynamic range in our best rationally constructed sensor. We found that this construct and an evolved variant demonstrate remarkable substrate selectivity, exhibiting no detectable response to the regioisomer byproduct isovanillate and minimal response to structurally similar pathway intermediates. We then harnessed the evolved biosensor to conduct rapid bioprospecting of natural catechol O-methyltransferases and identified three previously uncharacterized but active O-methyltransferases. Collectively, these efforts enrich our knowledge of how biosensing can aid metabolic engineering and constitute the foundation for future improvements in vanillin pathway productivity.
Distortions in Production Networks

How does an economy’s production structure determine its macroeconomic response to sectoral distortions? We study a static, multisector framework in which production is organized in an input-output network and production decisions are distorted. Sectoral distortions manifest at the aggregate level via two channels: total factor productivity (TFP) and the labor wedge. We show that near efficiency, distortions have zero first-order effects on TFP and nonzero first-order effects on the labor wedge, and that a sufficient statistic for the latter are the Domar weights. We thereby provide a Hulten-like theorem for the aggregate effects of sectoral distortions. A quantitative application of the model to the 2008–09 financial crisis suggests that the U.S. input-output structure amplified financial distortions by roughly a factor of two during the crisis.
An anti-CRF antibody suppresses the HPA axis and reverses stress-induced phenotypes

Hypothalamic-pituitary-adrenal (HPA) axis dysfunction contributes to numerous human diseases and disorders. We developed a high-affinity monoclonal antibody, CTRND05, targeting corticotropin-releasing factor (CRF). In mice, CTRND05 blocks stress-induced corticosterone increases, counteracts effects of chronic variable stress, and induces other phenotypes consistent with suppression of the HPA axis. CTRND05 induces skeletal muscle hypertrophy and increases lean body mass, effects not previously reported with small-molecule HPA-targeting pharmacologic agents. Multiorgan transcriptomics demonstrates broad HPA axis target engagement through altering levels of known HPA-responsive transcripts such as Fkbp5 and Myostatin and reveals novel HPA-responsive pathways such as the Apelin-Apelin receptor system. These studies demonstrate the therapeutic potential of CTRND05 as a suppressor of the HPA axis and serve as an exemplar of a potentially broader approach to target neuropeptides with immunotherapies, as both pharmacologic tools and novel therapeutics.
Development of Radiation-Hard Scintillators and Wavelength-Shifting Fibers

Future collider detectors impose unprecedented challenges on the radiation hardness of their detector components. We have performed R&D to develop radiation-hard active media for such detectors, calorimeters in particular. Among the options we have studied, quartz plates with thin radiation-hard coatings, intrinsically radiation-hard scintillators and radiation-hard wavelength-shifting fibers can be listed.Here we describe the recent advances in these developments and dicsuss recent and projected measurements.
Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits

The public access to noisy intermediate-scale quantum (NISQ) computers facilitated by IBM, Rigetti, D - Wave, etc., has propelled the development of quantum applications that may offer quantum supremacy in the future large-scale quantum computers. Parameterized quantum circuits (P QC) have emerged as a major driver for the development of quantum routines that potentially improve the circuit’s resilience to the noise. PQC’s have been applied in both generative (e.g. generative adversarial network) and discriminative (e.g. quantum classifier) tasks in the field of quantum machine learning. PQC’s have been also considered to realize high fidelity quantum gates with the available imperfect native gates of a target quantum hardware. Parameters of a P QC are determined through an iterative training process for a target noisy quantum hardware. However, temporal variations in qubit quality metrics affect the performance of a P QC. Therefore, the circuit that is trained without considering temporal variations exhibits poor fidelity over time. In this paper, we present training methodologies for P QC in a completely classical environment that can improve the fidelity of the trained P QC on a target NISQ hardware by as much as 21.91%.
Innate and Acquired Quinine‐Resistant Alcohol, but not Saccharin, Drinking in Crossed High Alcohol Preferring Mice

Background Alcohol consumption despite aversive consequences is often a key component of an alcoholism diagnosis. Free-choice alcohol consumption despite bitter quinine adulteration in rodents has been seen following several months of free-choice drinking, but there has been little study of whether prolonged access to other palatable substances such as saccharin yields quinine resistance. Selectively bred crossed high-alcohol-preferring (cHAP) mice average blood alcohol levels of over 250 mg/dl during free-choice access, considerably higher than other models. We hypothesized that higher intakes would yield more rapid development of quinine-resistant alcohol (QRA) drinking and quinine-resistant saccharin (QRS) drinking. Methods All experiments used male and female cHAP mice. Experiment 1 compared mice with either 0 or 5 weeks of alcohol drinking history, testing varying (0.032, 0.10, 0.32 g/l) quinine concentrations in ethanol. Experiment 2 examined whether innate QR may exist, comparing animals with a 1 or zero day of drinking history. Experiment 3 examined the effect of varying histories (0, 2, or 5 weeks) of free-choice 10% alcohol drinking on QR alcohol consumption at high quinine concentrations. Finally, Experiment 4 investigated the development of QRS drinking. Results We found that we could not detect a history effect in commonly used quinine concentrations, indicating that cHAP mice are innately quinine resistant to 0.10 g/l quinine. However, we were able to determine that a 2-week drinking history was sufficient to induce QRA drinking in cHAP mice at extremely high quinine concentrations (0.74 and 0.32 g/l). However, the history effect was specific to QRA, a saccharin drinking history, did not yield QRS drinking. Conclusions These data suggest that an alcohol drinking history induces maladaptive behaviors, such as drinking in spite of negative consequences, a pattern not seen with saccharin. Furthermore, a strong genetic predisposition to drink may promote an innate aversion resistance compared with commonly used inbred strains.
Efficient and modular CRISPR-Cas9 vector system for Physcomitrella patens

CRISPR-Cas9 has been shown to be a valuable tool in recent years, allowing researchers to precisely edit the genome using an RNA-guided nuclease to initiate double-strand breaks. Until recently, classical RAD51-mediated homologous recombination has been a powerful tool for gene targeting in the moss Physcomitrella patens. However, CRISPR-Cas9-mediated genome editing in P. patens was shown to be more efficient than traditional homologous recombination (Plant Biotechnology Journal, 15, 2017, 122). CRISPR-Cas9 provides the opportunity to efficiently edit the genome at multiple loci as well as integrate sequences at precise locations in the genome using a simple transient transformation. To fully take advantage of CRISPR-Cas9 genome editing in P. patens, here we describe the generation and use of a flexible and modular CRISPR-Cas9 vector system. Without the need for gene synthesis, this vector system enables editing of up to 12 loci simultaneously. Using this system, we generated multiple lines that had null alleles at four distant loci. We also found that targeting multiple sites within a single locus can produce larger deletions, but the success of this depends on individual protospacers. To take advantage of homology-directed repair, we developed modular vectors to rapidly generate DNA donor plasmids to efficiently introduce DNA sequences encoding for fluorescent proteins at the 5' and 3' ends of gene coding regions. With regard to homology-directed repair experiments, we found that if the protospacer sequence remains on the DNA donor plasmid, then Cas9 cleaves the plasmid target as well as the genomic target. This can reduce the efficiency of introducing sequences into the genome. Furthermore, to ensure the generation of a null allele near the Cas9 cleavage site, we generated a homology plasmid harboring a "stop codon cassette" with downstream near-effortless genotyping.
Visual Working Memory Directly Alters Perception

Visual working memory (VWM), the ability to temporarily maintain and manipulate information, underlies a variety of critical high-level behaviours from directing attention1-4 to making complex decisions5. Here we show that its impact extends to even the most basic levels of perceptual processing, directly interacting with and even distorting the physical appearance of visual features. This interference results from and can be predicted by the recruitment of posterior perceptual cortices to maintain information in VWM6-9, which causes an overlap with the neuronal populations supporting perceptual processing10-15. Across three sets of experiments, we demonstrated bidirectional interference between VWM and low-level perception. Specifically, for both maintained colours and orientations, presenting a distractor created bias in VWM representation depending on the similarity between incoming and maintained information, consistent with the known tuning curves for these features. Moreover, holding an item in mind directly altered the appearance of new stimuli, demonstrated by changes in psychophysical discrimination thresholds. Thus, as a consequence of sharing the early visual cortices, what you see and what you are holding in mind are intertwined at even the most fundamental stages of processing.
Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization

In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner learn better. The challenges arise from three perspectives. First, the state-of-the-art pool-based super teaching method applies mixed-integer non-linear programming (MINLP) which does not scale well to very large data sets. Second, it is desirable to restrict data access of the teachers to only their own data during the collaboration stage to mitigate privacy leaks. Finally, the teaching collaboration should be communication-efficient since large communication overheads can cause synchronization delays between teachers.To address these challenges, we formulate the collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk. We theoretically demonstrate the necessity of collaboration between teachers for improving the learner’s learning. Furthermore, we show that the proposed method enjoys a similar property as the Oracle property of adaptive Lasso. Empirical study illustrates that our teaching method can deliver significantly more accurate teaching results with high speed, while the non-collaborative MINLP-based super teaching becomes prohibitively expensive to compute.
Hydrodynamics and rheology of a vesicle doublet suspension

Effects of membrane-membrane adhesion on vesicle hydrodynamics are studied theoretically (using lubrication theory) and numerically (using time-adaptive boundary integral simulations). Novel vesicle dynamics are quantified to help design future microfluidic experiments to probe membrane adhesion.
Trust Region Based Adversarial Attack on Neural Networks

Deep Neural Networks are quite vulnerable to adversarial perturbations. Current state-of-the-art adversarial attack methods typically require very time consuming hyper-parameter tuning, or require many iterations to solve an optimization based adversarial attack. To address this problem, we present a new family of trust region based adversarial attacks, with the goal of computing adversarial perturbations efficiently. We propose several attacks based on variants of the trust region optimization method. We test the proposed methods on Cifar-10 and ImageNet datasets using several different models including AlexNet, ResNet-50, VGG-16, and DenseNet-121 models. Our methods achieve comparable results with the Carlini-Wagner (CW) attack, but with significant speed up of up to $37\times$, for the VGG-16 model on a Titan Xp GPU. For the case of ResNet-50 on ImageNet, we can bring down its classification accuracy to less than 0.1\% with at most $1.5\%$ relative $L_\infty$ (or $L_2$) perturbation requiring only $1.02$ seconds as compared to $27.04$ seconds for the CW attack. We have open sourced our method which can be accessed at~\cite{TRcode}.
Mental Health Diagnoses in Veterans Referred for Outpatient Geriatric Psychiatric Care at a Veterans Affairs Medical Center.

INTRODUCTION Nearly half of the U.S. veterans are over 65 years of age. Older veterans are at higher risk for mental health (MH) conditions, which are associated with increased mortality and health care costs. Given the deficit of specialty-trained geriatric providers, we are conducting a Quality Improvement initiative to improve MH services for older veterans at Minneapolis Veterans Affairs Health Care System. Our first step is to understand the demographic and diagnostic characteristics of veterans referred for geriatric MH specialty treatment. MATERIALS AND METHOD We conducted a retrospective chart review of demographics and psychiatric diagnoses in veterans seen for outpatient geriatric MH intake between May 1, 2011 and April 30, 2016. We used chi-square and Spearman's rho tests to examine age, diagnoses, and service-time era variables. RESULTS 1,059 veterans were evaluated, average age of 73.5 years. Depressive (47%), neurocognitive (42%), and anxiety disorders (22%) were the most common MH conditions. Vietnam veterans showed higher prevalence of depressive (56%), post-traumatic stress (11%), and alcohol use (10%) disorders. World War II veterans showed higher prevalence of neurocognitive disorders (71%). Neurocognitive disorder prevalence was significantly correlated with age. CONCLUSIONS Prevalence and comorbidity of major MH conditions is high in veterans referred for geriatric MH services. Future work will examine challenges faced by non-specialty providers in caring for older veterans, with the goal of developing targeted educational and clinical interventions to better address aging veterans' MH needs.
Asymptotic orthogonalization of subalgebras in II$_1$ factors

Let $M$ be a II$_1$ factor with a von Neumann subalgebra $Q\subset M$ that has infinite index under any projection in $Q'\cap M$ (e.g., $Q$ abelian; or $Q$ an irreducible subfactor with infinite Jones index). We prove that given any separable subalgebra $B$ of the ultrapower II$_1$ factor $M^\omega$, for a non-principal ultrafilter $\omega$ on $\Bbb N$, there exists a unitary element $u\in M^\omega$ such that $uBu^*$ is orthogonal to $Q^\omega$.
Covered Interest Rate Parity Deviations in External Emerging Market Sovereign Debt

Non-US dollar denominated external emerging market debt issuance in euros, yen, and sterling has grown substantially in recent years. This article is the first study to explore how non-dollar external emerging market bonds violate covered interest rate parity relative to their dollar-denominated external emerging market debt counterpart bonds for a given country. Such mispricing in the post-Great Recession era creates arbitrage opportunities for investors and suggests that emerging market country policymakers could create fiscal savings by instead issuing external sovereign debt in dollars more cheaply (versus non-dollar developed world currencies like euro, yen, and sterling) and swapping the proceeds to non-dollar currencies with currency forward and spot transactions. Such hypothetical fiscal savings from switching to dollar funding collectively are estimated to be more than $1 billion annually. TOPICS:Fixed income and structured finance, exchanges/markets/clearinghouses, emerging markets Key Findings • This article is the first study to explore how non-dollar external emerging market bonds violate covered interest rate parity relative to their dollar-denominated external emerging market debt counterpart bonds for a given country. • Such mispricing creates investment opportunities and suggests that by instead more cheaply issuing external sovereign debt in dollars (versus non-dollar developed world currencies like euro, yen, and sterling) and swapping the proceeds to non-dollar currencies, emerging market country policymakers could create substantial fiscal savings from switching to dollar funding.
Mining Maximal Induced Bicliques using Odd Cycle Transversals

Many common graph data mining tasks take the form of identifying dense subgraphs (e.g. clustering, clique-finding, etc). In biological applications, the natural model for these dense substructures is often a complete bipartite graph (biclique), and the problem requires enumerating all maximal bicliques (instead of just identifying the largest or densest). The best known algorithm in general graphs is due to Dias et al., and runs in time O(M |V|^4 ), where M is the number of maximal induced bicliques (MIBs) in the graph. When the graph being searched is itself bipartite, Zhang et al. give a faster algorithm where the time per MIB depends on the number of edges in the graph. In this work, we present a new algorithm for enumerating MIBs in general graphs, whose run time depends on how "close to bipartite" the input is. Specifically, the runtime is parameterized by the size k of an odd cycle transversal (OCT), a vertex set whose deletion results in a bipartite graph. Our algorithm runs in time O(M |V||E|k^2 3^(k/3) ), which is an improvement on Dias et al. whenever k <= 3log_3(|V|). We implement our algorithm alongside a variant of Dias et al.'s in open-source C++ code, and experimentally verify that the OCT-based approach is faster in practice on graphs with a wide variety of sizes, densities, and OCT decompositions.
Is Pretraining Necessary for hyperspectral image classification

We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pretraining effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset. This approach effectively resolves the architectural issue that arises when transferring meaningful information between the source and the target networks. To answer the second question, we carried out several ablation experiments. Based on the experimental results, a network trained from scratch performs as good as a network fine-tuned from a pre-trained network. However, we observed that pre-training the network has its own advantage in achieving better performances when deeper networks are required.
DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.
Idiopathic thrombocytopenic purpura.

The acute form of ITP may be drug-induced or may follow infection or exposure to chemicals. The chronic form represents a self-perpetuating immunologic (auto-immune) disorder, at times associated with chronic lymphatic leukemia and other disorders. In some cases, it may be a prodrome of systemic lupus, to which it seems to be closely related. Therapy of acute ITP is “expectant,” with use of platelet transfusions and corticosteroids paramount; splenectomy should almost never be done. Chronic ITP may be treated for a few months or even years with corticosteroids; in about 25 per cent of cases splenectomy ultimately will be necessary.
Variance-based sensitivity analysis for time-dependent processes

Abstract The global sensitivity analysis of time-dependent processes requires history-aware approaches. We develop for that purpose a variance-based method that leverages the correlation structure of the problems under study and employs surrogate models to accelerate the computations. The errors resulting from fixing unimportant uncertain parameters to their nominal values are analyzed through a priori estimates. We illustrate our approach on a harmonic oscillator example and on a nonlinear dynamic cholera model.
Nominal GDP versus price level targeting: An empirical evaluation

Abstract In response to the ongoing discussion in the literature of the appropriate framework for monetary policy, we compare two of the most frequently discussed alternatives to inflation targeting—targeting either the level of nominal GDP or the price level—within the context of a simple vector autoregressive (VAR) model. Our approach can be considered a constrained-discretion approach. The model is estimated using quarterly data over the period 1979:4-2003:4, a period in which the economy was buffeted by substantial supply and demand shocks. The paths of the federal funds rate, nominal GDP, real GDP, and the price level under nominal GDP and price level targeting are simulated over the 2004:1-2006:4 period. We evaluate nominal GDP and price level targeting by computing the values of simple loss functions. The loss function values indicate that closely targeting the path of nominal GDP based on 4.5% desired growth in nominal GDP produces noticeably lower losses in the simulation period than either price level targeting or a continuation of the implicit flexible inflation targeting monetary policy that characterized the estimation period.
Lobe, edge, and arc transitivity of graphs of connectivity 1

We give necessary and sufficient conditions for lobe-transitivity of locally finite and locally countable graphs whose connectivity equals 1. We show further that, given any biconnected graph Λ and a “code” assigned to each orbit of Aut(Λ) , there exists a unique lobe-transitive graph Γ of connectivity 1 whose lobes are copies of Λ and is consistent with the given code at every vertex of Γ . These results lead to necessary and sufficient conditions for a graph of connectivity 1 to be edge-transitive and to be arc-transitive. Countable graphs of connectivity 1 the action of whose automorphism groups is, respectively, vertex-transitive, primitive, regular, Cayley, and Frobenius had been previously characterized in the literature.
Validation of an app-based portable spirometer in adolescents with asthma.

Objective measurements of asthma impairment could aid teens in recognition of changes in asthma status over time. Ready access to a conventional spirometer is not realistic outside of the clinical ...
Using the movie Joy to teach innovation and entrepreneurship

Film and video clips have been used in the classroom to bring economic concepts to life. The authors use the 2015 film Joy to animate Joseph Schumpeter’s The Theory of Economic Development, a found...
Compete with others? No, thanks. With myself? Yes, please!

Abstract We study the willingness to compete against self and others in an experiment with over 650 participants, using a modified version of the Niederle and Vesterlund (2007) design. We show that introducing a possibility to self-compete, in addition to the standard other-competition option, increases the proportion of participants who compete by more than 60 percent, indicating that self-competition attracts many of those who would otherwise have stayed out of competitions altogether. This holds for both men and women. Moreover, men and women prefer self-competitions to other-competitions, especially when they have to select one of the two types of competitions. We also document that self-competitions are perceived as more controllable than other-competitions.
There goes the neighborhood does tipping exist amongst income groups

Abstract The ‘tipping point’ model of segregation Schelling (1971) has rarely been tested for socioeconomic variables besides race. Using US Census data from 1970 to 2010, I find evidence for tipping behavior amongst income groups. This is strongest when the population is split at the 10th percentile of income. In this case, average tipping points range from 7 to 12%. When the proportion of people below the 10th percentile in a neighborhood exceeds said tipping point, the decrease in those above the 10th percentile will be from 5 to 14%. Robustness checks indicate that these effects are not misinterpreted racial tipping points, nor a result of peoples’ income percentile changing over their lifetime. Effects are strongest in neighborhoods with more school-aged children, suggesting that income tipping is driven by concerns that the provision of local public goods could drop from an influx of lower class residents.
Gaussian Surrogate Dimension Reduction for Efficient Reliability-Based Design Optimization

Reliability-based design optimization (RBDO) is commonly performed using surrogates to approximate the response due to deterministic and stochastic design variables, as well as random parameters, b...
Characterizing the Performance of Executing Many-tasks on Summit

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.