cssi/SciSciGPT-SciSciNet huggingface dataset:
Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

A restricted Boltzmann machine (RBM) is a generative model that could be used in effectively balancing a cybersecurity dataset because the synthetic data a RBM generates follows the probability distribution of the training data. RBM training can be performed using contrastive divergence (CD) and quantum annealing (QA). QA-based RBM training is fundamentally different from CD and requires samples from a quantum computer. We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.
Analysis of Incoterms as Usage Under Article 9 of the CISG

This Article defines usage, as that term is used in the UN Convention on Contracts for the International Sale of Goods (CISG), in order to consider whether the ICC’s definitions for common delivery terms set forth in the ICC publication Incoterms should be characterized as usage for purposes of the CISG. This Article then describes the misguided approach that to date has been taken by U.S. courts when analyzing the role of Incoterms as usage for contracts governed by the CISG. Finally, this Article proposes a method for proper analysis of Incoterms under the CISG, including the role that Article 9 of the CISG should play in the analysis.
Leading nonlinear tidal effects and scattering amplitudes

We present the two-body Hamiltonian and associated eikonal phase, to leading post-Minkowskian order, for infinitely many tidal deformations described by operators with arbitrary powers of the curvature tensor. Scattering amplitudes in momentum and position space provide systematic complementary approaches. For the tidal operators quadratic in curvature, which describe the linear response to an external gravitational field, we work out the leading post-Minkowskian contributions using a basis of operators with arbitrary numbers of derivatives which are in one-to-one correspondence with the worldline multipole operators. Explicit examples are used to show that the same techniques apply to both bodies interacting tidally with a spinning particle, for which we find the leading contributions from quadratic in curvature tidal operators with an arbitrary number of derivatives, and to effective field theory extensions of general relativity. We also note that the leading post-Minkowskian order contributions from higher-dimension operators manifest double-copy relations. Finally, we comment on the structure of higher-order corrections.
How Distortions Alter the Impacts of International Trade in Developing Countries

Substantial research in development economics has highlighted the presence of weak institutions, market failures, and distortions in developing countries. Yet much of the knowledge generated in international trade comes from workhorse models that abstract from these frictions. This review summarizes the recent literature that assesses how these characteristics interact (or may interact) with trade reforms, resulting in different impacts in developing countries relative to what we would expect in developed countries. We discuss understudied areas that warrant further research.
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators

While maximizing deep neural networks' (DNNs') acceleration efficiency requires a joint search/design of three different yet highly coupled aspects, including the networks, bitwidths, and accelerators, the challenges associated with such a joint search have not yet been fully understood and addressed. The key challenges include (1) the dilemma of whether to explode the memory consumption due to the huge joint space or achieve sub-optimal designs, (2) the discrete nature of the accelerator design space that is coupled yet different from that of the networks and bitwidths, and (3) the chicken and egg problem associated with network-accelerator co-search, i.e., co-search requires operation-wise hardware cost, which is lacking during search as the optimal accelerator depending on the whole network is still unknown during search. To tackle these daunting challenges towards optimal and fast development of DNN accelerators, we propose a framework dubbed Auto-NBA to enable jointly searching for the Networks, Bitwidths, and Accelerators, by efficiently localizing the optimal design within the huge joint design space for each target dataset and acceleration specification. Our Auto-NBA integrates a heterogeneous sampling strategy to achieve unbiased search with constant memory consumption, and a novel joint-search pipeline equipped with a generic differentiable accelerator search engine. Extensive experiments and ablation studies validate that both Auto-NBA generated networks and accelerators consistently outperform state-of-the-art designs (including co-search/exploration techniques, hardware-aware NAS methods, and DNN accelerators), in terms of search time, task accuracy, and accelerator efficiency. Our codes are available at: this https URL.
Do Foreign Component Auditors Harm Financial Reporting Quality? A Subsidiary-Level Analysis of Foreign Component Auditor Use*

We hypothesize and find that financial reporting quality at the foreign subsidiaries of U.S. multinational corporations (MNCs) is higher when the MNC’s principal auditor engages a component auditor to audit the foreign subsidiary on its behalf. An important innovation of this study is that we focus on comparing financial reporting quality of equivalent subsidiaries with and without component auditor work. Our approach contrasts with extant studies that examine the consequences of variation in the total amount of component auditor work at the MNC level. Our results are important for two reasons. First, we provide an alternative view on the consequences of component auditor use compared to the emerging literature in this area, which typically finds a negative association between the extent of component auditor use and financial reporting quality at the MNC level. Thus, we show that a different research design, conducted at the level at which component auditors actually perform their work, yields different inferences. Second, we demonstrate that using component auditors on U.S. MNC group audits is an avenue through which U.S. auditing institutions can affect financial reporting quality in foreign locations. We also reconcile our subsidiary-level results to the MNC-level by introducing a new MNC-level component auditor “coverage” variable. Overall, we highlight that the best way to audit a foreign subsidiary is likely to be with a component auditor in the local country, which informs the debate surrounding recently proposed PCAOB guidance.
Sample-optimal and efficient learning of tree Ising models

We show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance є from an optimal O(n lnn/є2) samples, where O(·) hides an absolute constant which, importantly, does not depend on the model being learned—neither its tree nor the magnitude of its edge strengths, on which we place no assumptions. Our guarantees hold, in fact, for the celebrated Chow-Liu algorithm [1968], using the plug-in estimator for estimating mutual information. While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is є-close to the true one in total variation distance, a guarantee called “proper learning.” Our guarantees do not follow from known results for the Chow-Liu algorithm and the ensuing literature on learning graphical models, including the very recent renaissance of algorithms on this learning challenge, which only yield asymptotic consistency results, or sample-suboptimal and/or time-inefficient algorithms, unless further assumptions are placed on the model, such as bounds on the “strengths” of the model’s edges. While we establish guarantees for a widely known and simple algorithm, the analysis that this algorithm succeeds and is sample-optimal is quite complex, requiring a hierarchical classification of the edges into layers with different reconstruction guarantees, depending on their strength, combined with delicate uses of the subadditivity of the squared Hellinger distance over graphical models to control the error accumulation.
A Screen of Covalent Inhibitors In Mycobacterium Tuberculosis Identifies Serine Hydrolases Involved in Lipid Metabolism as Potential Therapeutic Targets

The increasing incidence of antibiotic-resistant Mycobacterium tuberculosis infections is a global health threat necessitating the development of new antibiotics. Serine hydrolases (SHs) are a promising class of targets because of their importance for the synthesis of the mycobacterial cell envelope. We screened a library of small molecules containing serine-reactive electrophiles and identified narrow spectrum inhibitors of M. tuberculous growth. Using these lead molecules, we performed competitive activity-based protein profiling and identified multiple SH targets, including enzymes with uncharacterized functions. Lipidomic analyses of compound-treated cultures revealed an accumulation of free lipids and a substantial decrease in lipooligosaccharides, linking SH inhibition to defects in cell envelope biogenesis. Mutant analysis revealed a path to resistance via the synthesis of mycocerates, but not through mutations to target enzymes. Our results suggest that simultaneous inhibition of multiple SH enzymes is likely to be an effective therapeutic strategy for the treatment of M. tuberculosis infections.
High Power Flex-Propellant Arcjet Performance

A MW-class electrothermal arcjet based on a water-cooled, wall-stabilized, constricted arc discharge configuration was subjected to extensive performance testing using hydrogen and simulated ammonia propellants with the deliberate aim of advancing technology readiness level for potential space propulsion applications. The breadboard design incorporates alternating conductor/insulator wafers to form a discharge barrel enclosure with a 2.5-cm internal bore diameter and an overall length of approximately 1 meter. Swirling propellant flow is introduced into the barrel, and a DC arc discharge mode is established between a backplate tungsten cathode button and a downstream ringanode/ spin-coil assembly. The arc-heated propellant then enters a short mixing plenum and is accelerated through a converging-diverging graphite nozzle. This innovative design configuration differs substantially from conventional arcjet thrusters, in which the throat functions as constrictor and the expansion nozzle serves as the anode, and permits the attainment of an equilibrium sonic throat (EST) condition. During the test program, applied electrical input power was varied between 0.5-1 MW with hydrogen and simulated ammonia flow rates in the range of 4-12 g/s and 15-35 g/s, respectively. The ranges of investigated specific input energy therefore fell between 50-250 MJ/kg for hydrogen and 10-60 MJ/kg for ammonia. In both cases, observed arc efficiencies were between 40-60 percent as determined via a simple heat balance method based on electrical input power and coolant water calorimeter measurements. These experimental results were found to be in excellent agreement with theoretical chemical equilibrium predictions, thereby validating the EST assumption and enabling the utilization of standard TDK nozzle expansion analyses to reliably infer baseline thruster performance characteristics. Inferred specific impulse performance accounting for recombination kinetics during the expansion process implied nearly frozen flow in the nozzle and yielded performance ranges of 800-1100 sec for hydrogen and 400-600 sec for ammonia. Inferred thrust-to-power ratios were in the range of 30-10 lbf/MWe for hydrogen and 60-20 lbf/MWe for ammonia. Successful completion of this test series represents a fundamental milestone in the progression of high power arcjet technology, and it is hoped that the results may serve as a reliable touchstone for the future development of MW-class regeneratively-cooled flex-propellant plasma rockets.
Nongray radiative heat transfer in high- temperature nonisothermal CO2-N2 mixtures

Nongray equilibrium radiative heat transfer in viscous radiating shock layer around blunt body entering high temperature nonisothermal carbon dioxide-nitrogen atmosphere
Contextual Inverse Optimization: Offline and Online Learning.

We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision-maker has information available from past periods and needs to make one decision, while in the online setting, the decision-maker optimizes decisions dynamically over time based a new set of feasible actions and contextual functions in each period. For the offline setting, we characterize the optimal minimax policy, establishing the performance that can be achieved as a function of the underlying geometry of the information induced by the data. In the online setting, we leverage this geometric characterization to optimize the cumulative regret. We develop an algorithm that yields the first regret bound for this problem that is logarithmic in the time horizon.
Normalization for cubical type theory

We prove normalization for (univalent, Cartesian) cubical type theory, closing the last major open problem in the syntactic metatheory of cubical type theory. Our normalization result is reduction-free, in the sense of yielding a bijection between equivalence classes of terms in context and a tractable language of β/η-normal forms. As corollaries we obtain both decidability of judgmental equality and the injectivity of type constructors.
Hemlock: Compact and Scalable Mutual Exclusion

We present Hemlock, a novel mutual exclusion locking algorithm that is extremely compact, requiring just one word per thread plus one word per lock, but which still provides local spinning in most circumstances, high throughput under contention, and low latency in the uncontended case. Hemlock is context-free -- not requiring any information to be passed from a lock operation to the corresponding unlock -- and FIFO. The performance of Hemlock is competitive with and often better than the best scalable spin locks.
Learning Interaction Kernels in Stochastic Systems of Interacting Particles from Multiple Trajectories

We consider stochastic systems of interacting particles or agents, with dynamics determined by an interaction kernel, which only depends on pairwise distances. We study the problem of inferring this interaction kernel from observations of the positions of the particles, in either continuous or discrete time, along multiple independent trajectories. We introduce a nonparametric inference approach to this inverse problem, based on a regularized maximum likelihood estimator constrained to suitable hypothesis spaces adaptive to data. We show that a coercivity condition enables us to control the condition number of this problem and prove the consistency of our estimator, and that in fact it converges at a near-optimal learning rate, equal to the min–max rate of one-dimensional nonparametric regression. In particular, this rate is independent of the dimension of the state space, which is typically very high. We also analyze the discretization errors in the case of discrete-time observations, showing that it is of order 1/2 in terms of the time spacings between observations. This term, when large, dominates the sampling error and the approximation error, preventing convergence of the estimator. Finally, we exhibit an efficient parallel algorithm to construct the estimator from data, and we demonstrate the effectiveness of our algorithm with numerical tests on prototype systems including stochastic opinion dynamics and a Lennard-Jones model.
Tangle decompositions of alternating link complements

Decomposing knots and links into tangles is a useful technique for understanding their properties. The notion of prime tangles was introduced by Kirby and Lickorish; Lickorish proved that by summing prime tangles one obtains a prime link. In a similar spirit, summing two prime alternating tangles will produce a prime alternating link if summed correctly with respect to the alternating property. Given a prime alternating link, we seek to understand whether it can be decomposed into two prime tangles, each of which is alternating. We refine results of Menasco and Thistlethwaite to show that if such a decomposition exists, either it is visible in an alternating link diagram or the link is of a particular form, which we call a pseudo-Montesinos link.
VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency

We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous back-ground sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speaker’s lip movements and the sounds they generate, we propose to leverage the speaker’s face appearance as an additional prior to isolate the corresponding vocal qualities they are likely to produce. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audio-visual speech separation and enhancement, and generalizes well to challenging real-world videos of diverse scenarios. Our video results and code: http://vision.cs.utexas.edu/projects/VisualVoice/.
The Age of Correlated Features in Supervised Learning based Forecasting

In this paper, we analyze the impact of information freshness on supervised learning based forecasting. In these applications, a neural network is trained to predict a time-varying target (e.g., solar power), based on multiple correlated features (e.g., temperature, humidity, and cloud coverage). The features are collected from different data sources and are subject to heterogeneous and time-varying ages. By using an information-theoretic approach, we prove that the minimum training loss is a function of the ages of the features, where the function is not always monotonic. However, if the empirical distribution of the training data is close to the distribution of a Markov chain, then the training loss is approximately a non-decreasing age function. Both the training loss and testing loss depict similar growth patterns as the age increases. An experiment on solar power prediction is conducted to validate our theory. Our theoretical and experimental results suggest that it is beneficial to (i) combine the training data with different age values into a large training dataset and jointly train the forecasting decisions for these age values, and (ii) feed the age value as a part of the input feature to the neural network.
Grid-Adapted FUN3D Computations for the Second High-Lift Prediction Workshop

Contributions of the unstructured Reynolds-averaged Navier–Stokes code FUN3D to the 2nd AIAA Computational Fluid Dynamics High-Lift Prediction Workshop are described, and detailed comparisons are made with experimental data. Using workshop-supplied grids, FUN3D results for the simplified high-lift configuration are compared with results from the structured code CFL3D. Using the same turbulence model, both codes compare reasonably well in terms of total forces and moment, and the maximum lift is similarly over-predicted for both codes compared to experiment. By including more representative geometry features such as slat and flap support brackets and slat pressure tube bundles, FUN3D captures the general effects of the Reynolds number variation, but under-predicts maximum lift on workshop-supplied grids in comparison with the experimental data due to excessive separation. However, when output-based, off-body grid adaptation in FUN3D is employed, results improve considerably. In particular, when the geometr...
Guaranteeing Maximin Shares: Some Agents Left Behind

The maximin share (MMS) guarantee is a desirable fairness notion for allocating indivisible goods. While MMS allocations do not always exist, several approximation techniques have been developed to ensure that all agents receive a fraction of their maximin share. We focus on an alternative approximation notion, based on the population of agents, that seeks to guarantee MMS for a fraction of agents. We show that no optimal approximation algorithm can satisfy more than a constant number of agents, and discuss the existence and computation of MMS for all but one agent and its relation to approximate MMS guarantees. We then prove the existence of allocations that guarantee MMS for $\frac{2}{3}$ of agents, and devise a polynomial time algorithm that achieves this bound for up to nine agents. A key implication of our result is the existence of allocations that guarantee $\text{MMS}^{\lceil{3n/2}\rceil}$, i.e., the value that agents receive by partitioning the goods into $\lceil{\frac{3}{2}n}\rceil$ bundles, improving the best known guarantee of $\text{MMS}^{2n-2}$. Finally, we provide empirical experiments using synthetic data.
An empirical analysis of long-term Brazilian interest rates.

This paper empirically models the dynamics of Brazilian government bond (BGB) yields based on monthly macroeconomic data in the context of the evolution of Brazilâs key macroeconomic variables. The results show that the current short-term interest rate has a decisive influence on BGBsâ long-term interest rates after controlling for various key macroeconomic variables, such as inflation and industrial production or economic activity. These findings support John Maynard Keynesâs claim that the central bankâs actions influence the long-term interest rate on government bonds mainly through the short-term interest rate. These findings have important policy implications for Brazil. This paper relates the findings of the estimated models to ongoing debates in fiscal and monetary policies.
Absorption-Based Diamond Spin Microscopy on a Plasmonic Quantum Metasurface

We propose a resonant diamond plasmonic metasurface coupled with nitrogen-vacancy ensembles as a quantum imaging surface and report a sensitivity below 1 nT/Hz1/2 per µm2 of sensing area.
Noncommutative Tensor Triangular Geometry and the Tensor Product Property for Support Maps

The problem of whether the cohomological support map of a finite dimensional Hopf algebra has the tensor product property has attracted a lot of attention following the earlier developments on representations of finite group schemes. Many authors have focussed on concrete situations where positive and negative results have been obtained by direct arguments. In this paper we demonstrate that it is natural to study questions involving the tensor product property in the broader setting of a monoidal triangulated category. We give an intrinsic characterization by proving that the tensor product property for the universal support datum is equivalent to complete primeness of the categorical spectrum. From these results one obtains information for other support data, including the cohomological one. Two theorems are proved giving compete primeness and non-complete primeness in certain general settings. As an illustration of the methods, we give a proof of a recent conjecture of Negron and Pevtsova on the tensor product property for the cohomological support maps for the small quantum Borel algebras for all complex simple Lie algebras.
A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation

Abstract We introduce a new symmetric treatment of anisotropic viscous terms in the viscoelastic wave equation. An appropriate memory variable treatment of stress-strain convolution terms, result into a symmetric system of first order linear hyperbolic partial differential equations, which we discretize using a high-order discontinuous Galerkin finite element method. The accuracy of the resulting numerical scheme is verified using convergence studies against analytical plane wave solutions and analytical solutions of the viscoelastic wave equation. Computational experiments are shown for various combinations of homogeneous and heterogeneous viscoelastic media in two and three dimensions.
Talk, Don’t Write: A Study of Direct Speech-Based Image Retrieval

Speech-based image retrieval has been studied as a proxy for joint representation learning, usually without emphasis on retrieval itself. As such, it is unclear how well speech-based retrieval can work in practice -- both in an absolute sense and versus alternative strategies that combine automatic speech recognition (ASR) with strong text encoders. In this work, we extensively study and expand choices of encoder architectures, training methodology (including unimodal and multimodal pretraining), and other factors. Our experiments cover different types of speech in three datasets: Flickr Audio, Places Audio, and Localized Narratives. Our best model configuration achieves large gains over state of the art, e.g., pushing recall-at-one from 21.8% to 33.2% for Flickr Audio and 27.6% to 53.4% for Places Audio. We also show our best speech-based models can match or exceed cascaded ASR-to-text encoding when speech is spontaneous, accented, or otherwise hard to automatically transcribe.
Experimental study of atmospheric pressure single-pulse nanosecond discharge in pin-to-pin configuration

We present an experimental study of nanosecond high-voltage discharges in a pin-to-pin electrode configuration at atmospheric conditions operating in a single-pulse mode (no memory effects). Discharge parameters were measured using microwave Rayleigh scattering, laser Rayleigh scattering, optical emission spectroscopy enhanced with a nanosecond probing pulse, and fast photography. Spark and corona discharge regimes were studied for electrode gap sizes of 2–10 mm and a discharge pulse duration of 90 ns. The spark regime was observed for gaps <6 mm using discharge pulse energies of 0.6–1 mJ per mm of the gap length. Higher electron number densities, total electron number per gap length, discharge currents, and gas temperatures were observed for smaller electrode gaps and larger pulse energies, reaching maximal values of about 7.5 × 1015 cm−3, 3.5 × 1011 electrons/mm, 22 A, and 4000 K (at 10 μs after the discharge), respectively, for a 2 mm gap and 1 mJ/mm discharge pulse energy. An initial breakdown was followed by a secondary breakdown occurring about 30–70 ns later and was associated with ignition of a cathode spot and transition to a cathodic arc. A majority of the discharge pulse energy was deposited into the gas before the secondary breakdown (85%–89%). The electron number density after the ns-discharge pulse decayed with a characteristic time scale of 150 ns governed by dissociative recombination and electron attachment to oxygen mechanisms. For the corona regime, substantially lower pulse energies (∼0.1 mJ/mm), peak conduction current (1–2 A), electron numbers (3–5 × 1010 electrons per mm), and gas temperatures (360 K) were observed.
Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

Low-altitude urban flight planning for small unmanned aircraft systems (UASs) accurate vehicle kinodynamics, environment maps, and risk models to assure that flight plans consider the urban landsca...
Predictors of Adherence to Nicotine Replacement Therapy (Nicotine Patch) Among Homeless Persons Enrolled in a Randomized Controlled Trial Targeting Smoking Cessation

INTRODUCTION Adherence to smoking cessation treatment is generally low, especially among socio-economically disadvantaged groups including individuals experiencing homelessness and those with mental illnesses. Despite the high smoking rates in homeless populations (~70%) no study to date has systematically examined predictors of adherence to nicotine replacement therapy (NRT) in this population. OBJECTIVE The aim of this secondary analysis was to identify predictors of adherence to NRT in a smoking cessation trial conducted among homeless smokers. METHODS Secondary analysis of data from a randomized controlled trial enrolling 430 persons who were homeless and current cigarette smokers. Participants were assigned to one of the two study conditions to enhance smoking cessation: Motivational Interviewing (MI; 6 sessions of MI + 8 weeks of NRT) or Standard Care (Brief advice to quit+ 8 weeks of NRT). The primary outcome for the current analysis was adherence to NRT at end of treatment (8 weeks following randomization). Adherence was defined as a total score of zero on a modified Morisky adherence scale). Demographic and baseline psychosocial, tobacco-related, and substance abuse measures were compared between those who did and did not adhere to NRT. RESULTS After adjusting for confounders, smokers who were depressed at baseline (OR=0.58, 95% CI, 0.38-0.87, p=0.01), had lower confidence to quit (OR=1.10, 95% CI, 1.01-1.19, p=0.04), were less motivated to adhere (OR=1.04, 95% CI, 1.00-1.07, p=0.04), and were less likely to be adherent to NRT. Further, age of initial smoking was positively associated with adherence status (OR= 0.83, 95% CI, 0.69-0.99, p=0.04). CONCLUSION These results suggest that smoking cessation programs conducted in this population may target increased adherence to NRT by addressing both depression and motivation to quit. TRIAL REGISTRATION clinicaltrials.gov: NCT00786149.
RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data.

SUMMARY Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. AVAILABILITY AND IMPLEMENTATION Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS. SUPPLEMENTARY INFORMATION There is no supplementary data.
The Curse of Correlations for Robust Fingerprinting of Relational Databases

Database fingerprinting have been widely adopted to prevent unauthorized sharing of data and identify the source of data leakages. Although existing schemes are robust against common attacks, like random bit flipping and subset attack, their robustness degrades significantly if attackers utilize the inherent correlations among database entries. In this paper, we first demonstrate the vulnerability of existing database fingerprinting schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and the integration of them. To provide robust fingerprinting against the identified correlation attacks, we then develop mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes. The proposed mitigation techniques also preserve the utility of the fingerprinted database considering different utility metrics. We empirically investigate the impact of the identified correlation attacks and the performance of mitigation techniques using real-world relational databases. Our results show (i) high success rates of the identified correlation attacks against existing fingerprinting schemes (e.g., the integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of the proposed mitigation techniques (e.g., with the mitigation techniques, the integrated correlation attack can only distort 3% fingerprint bits).
The Chromatic Quasisymmetric Class Function of a Digraph

We introduce a quasisymmetric class function associated with a group acting on a double poset or on a directed graph. The latter is a generalization of the chromatic quasisymmetric function of a digraph introduced by Ellzey, while the former is a generalization of a quasisymmetric function introduced by Grinberg. We prove representation-theoretic analogues of classical and recent results, including F-positivity, and combinatorial reciprocity theorems. We deduce results for orbital quasisymmetric functions, and study a generalization of the notion of strongly flawless sequences.
Projector-Guided Non-Holonomic Mobile 3D Printing

Fused deposition modeling (FDM) using mobile robots instead of the gantry-based 3D printer enables additive manufacturing at a larger scale with higher speed. This introduces challenges including accurate localization, control of the printhead, and design of a stable mobile manipulator with low vibrations and proper degrees of freedom. We proposed and developed a low-cost non-holonomic mobile 3D printing system guided by a projector via learning-based visual servoing. It requires almost no manual calibration of the system parameters. Using a regular top-down projector without any expensive external localization device for pose feedback, this system enabled mobile robots to accurately follow pre-designed millimeter-level printing trajectories with speed control. We evaluate the system in terms of its trajectory accuracy and printing quality compared with original 3D designs. We further demonstrated the potential of this system using two such mobile robots to collaboratively print a 3D object with dimensions of 80 cm × 30 cm size, which exceeds the limitation of common desktop FDM 3D printers.
Sensitivity of void mediated failure to geometric design features of porous metals

Abstract Material produced by current metal additive manufacturing processes is susceptible to variable performance due to imprecise control of internal porosity, surface roughness, and conformity to designed geometry. Using a double U-notched specimen, we investigate the interplay of nominal geometry and porosity in determining ductile failure characteristics during monotonic tensile loading. We simulate the effects of distributed porosity on plasticity and damage using a statistical model based on populations of pores visible in computed tomography scans and additional sub-threshold voids required to match experimental observations of deformation and failure. We interpret the simulation results from a physical viewpoint and provide a statistical model of the probability of failure near stress concentrations. We provide guidance for designs where material defects could cause unexpected failures depending on the relative importance of these defects with respect to features of the nominal geometry.
Faithfully flat descent of almost perfect complexes in rigid geometry

Abstract We prove a version of faithfully flat descent in rigid analytic geometry, for almost perfect complexes and without finiteness assumptions on the rings involved. This extends results of Drinfeld for vector bundles.
Sample selection bias in evaluation of prediction performance of causal models

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However prediction performance does depend on the selection of training and test sets. In particular biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren [Kemmeren et al., 2014]. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance on Kemmeren and compare models on this new set. In this setting, the causal model tested have similar performance to standard association based estimators such as Lasso. Finally we compare the performance of causal estimators in simulation studies which reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.
Tensor Normal Training for Deep Learning Models

Despite the predominant use of first-order methods for training deep learning models, second-order methods, and in particular, natural gradient methods, remain of interest because of their potential for accelerating training through the use of curvature information. Several methods with non-diagonal preconditioning matrices, including KFAC and Shampoo, have been proposed and shown to be effective. Based on the so-called tensor normal (TN) distribution, we propose and analyze a brand new approximate natural gradient method, Tensor Normal Training (TNT), which like Shampoo, only requires knowledge on the shape of the training parameters. By approximating the probabilistically based Fisher matrix, as opposed to the empirical Fisher matrix, our method uses the layer-wise covariance of the sampling based gradient as the pre-conditioning matrix. Moreover, the assumption that the sampling-based (tensor) gradient follows a TN distribution, ensures that its covariance has a Kronecker separable structure, which leads to a tractable approximation to the Fisher matrix. Consequently, TNT's memory requirements and per-iteration computational costs are only slightly higher than those for first-order methods. In our experiments, TNT exhibited superior optimization performance to KFAC and Shampoo, and to state-of-the-art first-order methods. Moreover, TNT demonstrated its ability to generalize as well as these first-order methods, using fewer epochs.
Filter Feature Selection for Unsupervised Clustering of Designer Drugs Using DFT Simulated IR Spectra Data

The rapid emergence of novel psychoactive substances (NPS) poses new challenges and requirements for forensic testing/analysis techniques. This paper aims to explore the application of unsupervised...
Online Learning and Decision-Making under Generalized Linear Model with High-Dimensional Data

We propose a minimax concave penalized multi-armed bandit algorithm under generalized linear model (G-MCP-Bandit) for a decision-maker facing high-dimensional data in an online learning and decision-making process. We demonstrate that the G-MCP-Bandit algorithm asymptotically achieves the optimal cumulative regret in the sample size dimension T, O(log T), and further attains a tight bound in the covariate dimension d, O(log d). In addition, we develop a linear approximation method, the 2-step weighted Lasso procedure, to identify the MCP estimator for the G-MCP-Bandit algorithm under non-iid samples. Under this procedure, the MCP estimator matches the oracle estimator with high probability and converges to the true parameters with the optimal convergence rate. Finally, through experiments based on synthetic data and two real datasets (warfarin dosing dataset and Tencent search advertising dataset), we show that the G-MCP-Bandit algorithm outperforms other benchmark algorithms, especially when there is a high level of data sparsity or the decision set is large.
Operating system protection for fine-grained programs

We present an operating system-level security model for controlling fine-grained programs, such as downloaded executable content, and compare this security model's implementation to that of language-based security models. Language-based security has well-known limitations, such as the lack of complete mediation (e.g., for compiled programs or race condition attacks) and faulty self-protection (effective security is unproven). Operating system-level models are capable of complete mediation and self-protection, but some researchers argue that operating system-level security models are unlikely to supplant such language-based models because they lack portability and performance. In this paper, we detail an operating system-level security model built on the Lava Nucleus, a minimal, fast µ-kernel operating system. We show how it can enforce security requirements for fine-grained programs and show that its performance overhead (with the additional security) can be virtually negligible when compared to language-based models. Given the sufficient performance and security, the portability issue should become moot because other vendors will have to meet the higher security and performance expectations of their customers.
An evaluation of amphotericin B in vitro and in vivo in mice against Coccidioides immitis and Candida albicans, and preliminary observations concerning the administration of amphotericin B to man.

Two new and closely related antifungal antibiotics, Amphotericin A and Amphotericin B, have recently been isolated from an unidentified species of streptomyces first obtained from the soil of the Orinoco River region of Venezuela. (1, 2, 3, 4) In vitro these antibiotics inhibit a wide variety of non-pathogenic and pathogenic fungi in concentrations of 0.5 to 12.5 jug/ml. (1) They are not, however, effective against bacteria at levels as high as 50 Mg/ml. Amphotericin A is effective against a wider variety of fungi but Amphotericin B is several times more active against many pathogenic fungi including Candida albicans, Cryptococcus neoformans, Blastomyces dermatitidis, Blastomyces brasiliensis, Sporotrichum schenckii and Histoplasma capsulatum. The toxicity of these antibiotics is relatively low when administered intraperitoneally in mice. (2) Both, in mice, favorably influence the course of experimental infections produced by Candida albicans, Cryptococcus neoformans, Histoplasma capsulatum and Coccidioides immitis. (2, 3) This effect was observed regardless of whether the intraperitoneal, subcutaneous or oral route was employed for administration of these drugs. Amphotericin B was more effective regardless of the route of administration. In a preliminary in vivo experiment (2) Amphotericin B was found to show a protective and in some instances a biological curative effect when administered daily, either orally or intraperitoneally, to mice experimentally infected with Coccidioides immitis and Candida albicans.
C-2 neurectomy during atlantoaxial instrumented fusion in the elderly: patient satisfaction and surgical outcome.

Object The originally described technique of atlantoaxial stabilization using C-1 lateral mass and C-2 pars screws includes a C-2 neurectomy to provide adequate hemostasis and visualization for screw placement, enable adequate joint decortication and arthrodesis, and prevent new-onset postoperative C-2 neuralgia. However, inclusion of a C-2 neurectomy for this procedure remains controversial, likely due in part to a lack of studies that have specifically addressed whether it affects patient outcome. The authors' objective was to assess the surgical and clinical impact of routine C-2 neurectomy performed with C1–2 segmental instrumented arthrodesis in a consecutive series of elderly patients with C1–2 instability. Methods Forty-four consecutive patients (mean age 71 years) underwent C1–2 instrumented fusion, including C-1 lateral mass screw insertion. Bilateral C-2 neurectomies were performed. Standardized clinical assessments were performed both pre- and postoperatively. Numbness or discomfort in a C-2 di...
Fairly Redistributing Failed Server Load in a Distributed System

We recently proposed a novel method for large-object replication and load balancing. Our method is particularly well-suited to data grids, data warehousing providers, and hosting of dynamic web sites. The method attempts to distribute object request load fairly to servers according to server capacity so that the likelihood of them overloading, and hence failing, is reduced. Unfortunately, server failures cannot be eliminated entirely. When a server fails, the load carried by that server must be absorbed by the rest of the system. Unless this load is distributed fairly across the remaining servers, they may also overload, creating a cascade of failures and reduced quality of service. In this paper, we propose an efficient method for fairly redistributing the load of a failed server or set of failed servers within our replication system. We also report on experimental results that verify the validity of our approach.