cssi/SciSciGPT-SciSciNet huggingface dataset:
A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity

We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor patients. Our method leverages convolutional layers to extract graph-based features from the dynamic connectivity matrices and a long-short term memory (LSTM) attention network to weight the relevant time points during classification. The final stage of our model employs multi-task learning to identify different eloquent subsystems. Our unique training strategy finds a shared representation between the cognitive networks of interest, which enables us to handle missing patient data. We evaluate our method on resting-state fMRI data from 56 brain tumor patients while using task fMRI activations as surrogate ground-truth labels for training and testing. Our model achieves higher localization accuracies than conventional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.
S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types.

SUMMARY Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic data sets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic data sets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these data sets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify discretized signal intensity levels and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics data sets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. AVAILABILITY AND IMPLEMENTATION S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP. SUPPLEMENTARY INFORMATION S3V2_IDEAS_supplementary_materials.docx.
Structural Completeness of a Multi-channel Linear System with Dependent Parameters

It is well known that the "fixed spectrum" {i.e., the set of fixed modes} of a multi-channel linear system plays a central role in the stabilization of such a system with decentralized control. A parameterized multi-channel linear system is said to be "structurally complete" if it has no fixed spectrum for almost all parameter values. Necessary and sufficient algebraic conditions are presented for a multi-channel linear system with dependent parameters to be structurally complete. An equivalent graphical condition is also given for a certain type of parameterization.
Holistic principle for risk aggregation and capital allocation

Risk aggregation and capital allocation are of paramount importance in business, as they play critical roles in pricing, risk management, project financing, performance management, regulatory supervision, etc. The state-of-the-art practice often includes two steps: (i) determine standalone capital requirements for individual business lines and aggregate them at a corporate level; and (ii) allocate the total capital back to individual lines of business or at more granular levels. There are three pitfalls with such a practice, namely, lack of consistency, negligence of cost of capital, and disentanglement of allocated capitals from standalone capitals. In this paper, we introduce a holistic approach that aims to strike a balance of optimality by taking into account competing interests of various stakeholders and conflicting priorities in a corporate hierarchy. While unconventional in its objective, the new approach results in an allocation of diversification benefit, which conforms to the diversification strategy of many risk management frameworks including regulatory capital and economic capital. The holistic capital setting and allocation principle provides a remedy to aforementioned problems with the existing two-step industry practice.
LCFI: A Fault Injection Tool for Studying Lossy Compression Error Propagation in HPC Programs

Error-bounded lossy compression is becoming more and more important to today’s extreme-scale HPC applications because of the ever-increasing volume of data generated because it has been widely used in in-situ visualization, data stream intensity reduction, storage reduction, I/O performance improvement, checkpoint/restart acceleration, memory footprint reduction, etc. Although many works have optimized ratio, quality, and performance for different error-bounded lossy compressors, there is none of the existing works attempting to systematically understand the impact of lossy compression errors on HPC application due to error propagation.In this paper, we propose and develop a lossy compression fault injection tool, called LCFI. To the best of our knowledge, this is the first fault injection tool that helps both lossy compressor developers and users to systematically and comprehensively understand the impact of lossy compression errors on HPC programs. The contributions of this work are threefold: (1) We propose an efficient approach to inject lossy compression errors according to a statistical analysis of compression errors for different state-of-the-art compressors. (2) We build a fault injector which is highly applicable, customizable, easy-to-use in generating top-down comprehensive results, and demonstrate the use of LCFI. (3) We evaluate LCFI on four representative HPC benchmarks with different abstracted fault models and make several observations about error propagation and their impacts on program outputs.
Specifying and executing optimizations for parallel programs

Compiler optimizations, usually expressed as rewrites on program graphs, are a core part of all modern compilers. However, even production compilers have bugs, and these bugs are difficult to detect and resolve. The problem only becomes more complex when compiling parallel programs; from the choice of graph representation to the possibility of race conditions, optimization designers have a range of factors to consider that do not appear when dealing with single-threaded programs. In this paper we present PTRANS, a domain-specific language for formal specification of compiler transformations, and describe its executable semantics. The fundamental approach of PTRANS is to describe program transformations as rewrites on control flow graphs with temporal logic side conditions. The syntax of PTRANS allows cleaner, more comprehensible specification of program optimizations; its executable semantics allows these specifications to act as prototypes for the optimizations themselves, so that candidate optimizations can be tested and refined before going on to include them in a compiler. We demonstrate the use of PTRANS to state, test, and refine the specification of a redundant store elimination optimization on parallel programs.
Product market competition, disclosure framing, and casting in earnings conference calls

Abstract An extensive stream of literature investigates how product market competition, by increasing the proprietary costs of disclosure, influences corporate disclosure policy. We hypothesize that the proprietary costs incurred by firms are associated with how a disclosure is framed and structured. We predict and find that the intensity of competition in the product market is associated with more negative and uncertain earnings conference calls. We also find evidence of firms “casting calls” by emphasizing more pessimistic analysts’ questions. Our results are robust to matched analyses on cash flow uncertainty and to examining the casting of analysts, as opposed to their questions, on the earnings call. Finally, we find consistent results using alternative measures of linguistic structure and in an alternative channel of disclosure with an alternative measure of linguistic framing. Our results demonstrate that firms attempt to mitigate proprietary costs by managing discretionary disclosure structure and framing their conference calls.
Rescuing a black hole in the large-$q$ coupled SYK model

In this paper, we develop a general effective theory for two copies of the Sachdev-Ye-Kitaev (SYK) model with a time-dependent bilinear coupling. For a quantum quench problem with an initial state of the thermofield double state, we show how the evolution of the system is described by a complex reparametrization field with a classical Hamiltonian. We study correlation functions in this system and compare the large-q theory with the bulk low energy effective theory. In particular, we study the special case of a “rescued black hole”, which describes how a time-evolved thermofield double state can evolve to the ground state of a coupled SYK model by a carefully tuned time-dependent coupling. In the low energy region, there is a holographic dual interpretation, which is a geometry that crosses over from an eternal black hole to a global AdS2 vacuum. This family of geometries allow us to access the bulk region that would be the black hole interior without the rescue process. By comparing the large-q and low energy theory, we find that even in the low energy region the deviation from the low energy theory cannot be neglected if the rescue process starts late. This provides evidence that the low energy effective theory of the bulk fails near the inner horizon of the black hole. We note the possibility of a connection to a two-dimensional analog of the higher-dimensional black hole singularity.
Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations

Understanding the structure of knowledge domains is one of the foundational challenges in the science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals and the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful “axes” that encompass knowledge domains, such as an axis from “soft” to “hard” sciences or from “social” to “biological” sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in the science of science, our framework may, in turn, facilitate the study of how knowledge is created and organized.
Explicit heat kernels of a model of distorted Brownian motion on spaces with varying dimension

In this paper, we study a particular model of distorted Brownian motion (dBM) on state spaces with varying dimension. Roughly speaking, the state space of such a process consists of two components: a 3-dimensional component and a 1-dimensional component. These two parts are joined together at the origin. The restriction of dBM on the 3- or 1-dimensional component receives a strong “push” toward the origin. On each component, the “magnitude” of a “push” can be parametrized by a constant γ>0. In this article, using the probabilistic method, we get the exact expressions for the transition density functions of dBM with varying dimension for any 0<t<∞.
Modeling of Dynamical Systems via Successive Graph Approximations

Abstract A non-parametric technique for modeling of systems with unknown nonlinear Lipschitz dynamics is presented. The key idea is to successively utilize measurements to approximate the graph of the state-update function of the system dynamics using envelopes described by quadratic constraints. The proposed approach is then used for computing outer approximations of the state-update function using convex optimization. We highlight the efficacy of the proposed approach via a detailed numerical example.
Linear-Cost Covariance Functions for Gaussian Random Fields

Gaussian random fields (GRF) are a fundamental stochastic model for spatiotemporal data analysis. An essential ingredient of GRF is the covariance function that characterizes the joint Gaussian dis...
Iterated linear optimization

We introduce a fixed point iteration process built on optimization of a linear function over a compact domain. We prove the process always converges to a fixed point and explore the set of fixed points in various convex sets. In particular, we consider elliptopes and derive an algebraic characterization of their fixed points. We show that the attractive fixed points of an elliptope are exactly its vertices. Finally, we discuss how fixed point iteration can be used for rounding the solution of a semidefinite programming relaxation.
On Incentive Compatibility in Dynamic Mechanism Design With Exit Option in a Markovian Environment

This paper studies dynamic mechanism design in a Markovian environment and analyzes a direct mechanism model of a principal-agent framework in which the agent is allowed to exit at any period. We consider that the agent’s private information, referred to as state, evolves over time. The agent makes decisions of whether to stop or continue and what to report at each period. The principal, on the other hand, chooses decision rules consisting of an allocation rule and a set of payment rules to maximize her ex-ante expected payoff. In order to influence the agent’s stopping decision, one of the terminal payment rules is posted-price, i.e., it depends only on the realized stopping time of the agent. This work focuses on the theoretical design regime of the dynamic mechanism design when the agent makes coupled decisions of reporting and stopping. A dynamic incentive compatibility constraint is introduced to guarantee the robustness of the mechanism to the agent’s strategic manipulation. A sufficient condition for dynamic incentive compatibility is obtained by constructing the payment rules in terms of a set of functions parameterized by the allocation rule. The payment rules are then pinned down up to a constant in terms of the allocation rule by deriving a first-order condition. We show cases of relaxations of the principal’s mechanism design problem and provide an approach to evaluate the loss of robustness of the dynamic incentive compatibility when the problem solving is relaxed due to analytical intractability. A case study is used to illustrate the theoretical results.
Efficient Interpolation of Density Estimators

We study the problem of space and time efficient evaluation of a nonparametric estimator that approximates an unknown density. In the regime where consistent estimation is possible, we use a piecewise multivariate polynomial interpolation scheme to give a computationally efficient construction that converts the original estimator to a new estimator that can be queried efficiently and has low space requirements, all without adversely deteriorating the original approximation quality. Our result gives a new statistical perspective on the problem of fast evaluation of kernel density estimators in the presence of underlying smoothness. As a corollary, we give a succinct derivation of a classical result of Kolmogorov---Tikhomirov on the metric entropy of Holder classes of smooth functions.
The Voisin map via families of extensions

Let Y be a cubic fourfold not containing any plane, F(Y) be the variety of lines in Y, Z(Y) be the Lehn-Lehn-Sorger-van Straten hyperkahler eightfold constructed in Lehn et al. (J fur die Reine und Angewandte Math (Crelles J) 2017(731):87–128, 2017). In Voisin (Remarks and questions on Coisotropic subvarieties and 0-cycles of hyper-Kahler varieties. Springer International Publishing, Berlin, 2016), Voisin defined a degree six rational map $$v: F(Y)\times F(Y) \dashrightarrow Z(Y),$$ relating the two hyperkahler varieties F(Y) and Z(Y). In this note, we reinterpret this map v using moduli spaces of Bridgeland stable objects in a triangulated category associated with Y, called a Kuznetsov component of Y. We prove that the Voisin map v can be resolved by blowing up the incident locus of intersecting lines in $$F(Y)\times F(Y)$$ endowed with the reduced scheme structure. As a consequence of our approach, we also show that the above-mentioned blowup is a relative Quot scheme over Z(Y) parameterizing quotients in a heart of the Kuznetsov component of Y.
Solventless sol-gel chemistry through ring-opening polymerization of bridged disilaoxacyclopentanes

Disilaoxacyclopentanes have proven to be excellent precursors to sol-gel type materials. These materials have shown promise as precursors for encapsulation and microelectronics applications. The polymers are highly crosslinked and are structurally similar to traditional sol-gels, but unlike typical sol-gels they are prepared without the use of solvents and water, they have low VOC's and show little shrinkage during processing.
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR

Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech. In the previous study, the model parameters were trained based on the speaker-attributed maximum mutual information (SA-MMI) criterion, with which the joint posterior probability for multi-talker transcription and speaker identification are maximized over training data. Although SA-MMI training showed promising results for overlapped speech consisting of various numbers of speakers, the training criterion was not directly linked to the final evaluation metric, i.e., speaker-attributed word error rate (SA-WER). In this paper, we propose a speaker-attributed minimum Bayes risk (SA-MBR) training method where the parameters are trained to directly minimize the expected SA-WER over the training data. Experiments using the LibriSpeech corpus show that the proposed SA-MBR training reduces the SA-WER by 9.0 % relative compared with the SA-MMI-trained model.1
Branch-and-price for a class of nonconvex mixed-integer nonlinear programs

This work attempts to combine the strengths of two major technologies that have matured over the last three decades: global mixed-integer nonlinear optimization and branch-and-price. We consider a class of generally nonconvex mixed-integer nonlinear programs (MINLPs) with linear complicating constraints and integer linking variables. If the complicating constraints are removed, the problem becomes easy to solve, e.g. due to decomposable structure. Integrality of the linking variables allows us to apply a discretization approach to derive a Dantzig-Wolfe reformulation and solve the problem to global optimality using branch-andprice. It is a remarkably simple idea; but to our surprise, it has barely found any application in the literature. In this work, we show that many relevant problems directly fall or can be reformulated into this class of MINLPs. We present the branch-and-price algorithm and demonstrate its effectiveness (and sometimes ineffectiveness) in an extensive computational study considering multiple large-scale problems of practical relevance, showing that, in many cases, orders-of-magnitude reductions in solution time can be achieved.
Assessing the Role of Calmodulin's Linker Flexibility in Target Binding.

Calmodulin (CaM) is a highly-expressed Ca2+ binding protein known to bind hundreds of protein targets. Its binding selectivity to many of these targets is partially attributed to the protein’s flexible alpha helical linker that connects its N- and C-domains. It is not well established how its linker mediates CaM’s binding to regulatory targets yet. Insights into this would be invaluable to understanding its regulation of diverse cellular signaling pathways. Therefore, we utilized Martini coarse-grained (CG) molecular dynamics simulations to probe CaM/target assembly for a model system: CaM binding to the calcineurin (CaN) regulatory domain. The simulations were conducted assuming a ‘wild-type’ calmodulin with normal flexibility of its linker, as well as a labile, highly-flexible linker variant to emulate structural changes that could be induced, for instance, by post-translational modifications. For the wild-type model, 98% of the 600 simulations across three ionic strengths adopted a bound complex within 2 μs of simulation time; of these, 1.7% sampled the fully-bound state observed in the experimentally-determined crystallographic structure. By calculating the mean-first-passage-time for these simulations, we estimated the association rate to be ka= 8.7 × 108 M−1 s−1, which is similar to the diffusion-limited, experimentally-determined rate of 2.2 × 108 M−1 s−1. Furthermore, our simulations recapitulated its well-known inverse relationship between the association rate and the solution ionic strength. In contrast, although over 97% of the labile linker simulations formed tightly-bound complexes, only 0.3% achieved the fully-bound configuration. This effect appears to stem from a difference in the ensembles of extended and collapsed states which are controlled by the linker flexibility. Therefore, our simulations suggest that variations in the CaM linker’s propensity for alpha helical secondary structure can modulate the kinetics of target binding.
Under Pressure: Intractable Trade Conflicts and Korea - Pneumatic Valves

The Appellate Body (AB) report in Korea–Pneumatic Valves is among the most structurally complex of recent trade remedies reports. The AB weaves a complicated web to explicate the intricacies of the relationship among the relevant provisions on injury determination in anti-dumping disputes in the midst of a contentious and long-running series of disputes between two close regional trading powers. This Article first examines the Korean investigation and its significance in economic terms. Second, it analyzes the AB's conclusions on Article 6.2 of the Dispute Settlement Understanding regarding the panel request and the significance of the AB's treatment. Third, it studies the AB's comments on the panel's anti-dumping calculations where the AB amplifies its prior pronouncements on the elements of injury and causation. Finally, the Article situates this dispute in the broader political debates playing out both at the AB and between the two disputing parties.
Non-relativistic geometry and the equivalence principle

We describe a geometric and symmetry-based formulation of the equivalence principle in non-relativistic physics. It applies both on the classical and quantum levels and states that the Newtonian potential can be eliminated in favor of a curved and time-dependent spatial metric. It is this requirement that forces the gravitational mass to be equal to the inertial mass. We identify the symmetry responsible for the equivalence principle as the remnant of time-reparameterization symmetry of the relativistic theory. We also clarify the transformation properties of the Schroedinger wave-function under arbitrary changes of frame.
Nearly Optimal Robust Mean Estimation via Empirical Characteristic Function

We propose an estimator for the mean of random variables in separable real Banach spaces using the empirical characteristic function. Assuming that the covariance operator of the random variable is bounded in a precise sense, we show that the proposed estimator achieves the optimal sub-Gaussian rate, except for a faster decaying mean-dependent term. Under a mild condition, an iterative refinement procedure can essentially eliminate the mean-dependent term and provide a shift-equivariant estimate. Our results particularly suggests that a certain Gaussian width that appears in the best known rate in the literature might not be necessary. Furthermore, using the boundedness of the characteristic functions, we also show that, except possibly at high signal-to-noise ratios, the proposed estimator is gracefully robust against adversarial “contamination”. Our analysis is overall concise and transparent, thanks to the tractability of the characteristic functions.
Activation of Cytochrome C Peroxidase Function Through Coordinated Foldon Loop Dynamics upon Interaction with Anionic Lipids

Cardiolipin (CL) is a mitochondrial anionic lipid that plays important roles in the regulation and signaling of mitochondrial apoptosis. CL peroxidation catalyzed by the assembly of CL-cytochrome c (cyt c) complexes at the inner mitochondrial membrane is a critical checkpoint. The structural changes in the protein, associated with peroxidase activation by CL and different anionic lipids, are not known at a molecular level. To better understand these peripheral protein-lipid interactions, we compare how phosphatidylglycerol (PG) and CL lipids trigger cyt c peroxidase activation, and correlate functional differences to structural and motional changes in membrane-associated cyt c. Structural and motional studies of the bound protein are enabled by magic angle spinning solid state NMR spectroscopy, while lipid peroxidase activity is assayed by mass spectrometry. PG binding results in a surface-bound state that preserves a nativelike fold, which nonetheless allows for significant peroxidase activity, though at a lower level than binding its native substrate CL. Lipid-specific differences in peroxidase activation are found to correlate to corresponding differences in lipid-induced protein mobility, affecting specific protein segments. The dynamics of omega loops C and D are upregulated by CL binding, in a way that is remarkably controlled by the protein:lipid stoichiometry. In contrast to complete chemical denaturation, membrane-induced protein destabilization reflects a destabilization of select cyt c foldons, while the energetically most stable helices are preserved. Our studies illuminate the interplay of protein and lipid dynamics in the creation of lipid peroxidase-active proteolipid complexes implicated in early stages of mitochondrial apoptosis.
Calibrationless MRI Reconstruction With A Plug-In Denoiser

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI’s clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (HICU) [1] with a plug-in denoiser and demonstrate its feasibility using 2D brain data.
Threshold scattering for the 2D radial cubic-quintic NLS

We consider the cubic-quintic nonlinear Schrodinger equation in two space dimensions. For this model, X. Cheng established scattering for H1 data with mass strictly below that of the ground state f...
Low communication high performance ab initio density matrix renormalization group algorithms

There has been recent interest in the deployment of ab initio density matrix renormalization group (DMRG) computations on high performance computing platforms. Here, we introduce a reformulation of the conventional distributed memory ab initio DMRG algorithm that connects it to the conceptually simpler and advantageous sum of the sub-Hamiltonian approach. Starting from this framework, we further explore a hierarchy of parallelism strategies that includes (i) parallelism over the sum of sub-Hamiltonians, (ii) parallelism over sites, (iii) parallelism over normal and complementary operators, (iv) parallelism over symmetry sectors, and (v) parallelism within dense matrix multiplications. We describe how to reduce processor load imbalance and the communication cost of the algorithm to achieve higher efficiencies. We illustrate the performance of our new open-source implementation on a recent benchmark ground-state calculation of benzene in an orbital space of 108 orbitals and 30 electrons, with a bond dimension of up to 6000, and a model of the FeMo cofactor with 76 orbitals and 113 electrons. The observed parallel scaling from 448 to 2800 central processing unit cores is nearly ideal.
Barriers to small molecule drug discovery for systemic amyloidosis

Inhibition of amyloid fibril formation could benefit patients with systemic amyloidosis. In this group of diseases, deposition of amyloid fibrils derived from normally soluble proteins leads to progressive tissue damage and organ failure. Amyloid formation is a complex process, where several individual steps could be targeted. Several small molecules have been proposed as inhibitors of amyloid formation. However, the exact mechanism of action for a molecule is often not known, which impedes medicinal chemistry efforts to develop more potent molecules. Furthermore, commonly used assays are prone to artifacts that must be controlled for. Here, potential mechanisms by which small molecules could inhibit aggregation of immunoglobulin light-chain dimers, the precursor proteins for amyloid light-chain (AL) amyloidosis, are studied in assays that recapitulate different aspects of amyloidogenesis in vitro. One molecule reduced unfolding-coupled proteolysis of light chains, but no molecules inhibited aggregation of light chains or disrupted pre-formed amyloid fibrils. This work demonstrates the challenges associated with drug development for amyloidosis, but also highlights the potential to combine therapies that target different aspects of amyloidosis.
Improved dynamic algorithms for longest increasing subsequence

We study dynamic algorithms for the longest increasing subsequence (LIS) problem. A dynamic LIS algorithm maintains a sequence subject to operations of the following form arriving one by one: insert an element, delete an element, or substitute an element for another. After each update, the algorithm must report the length of the longest increasing subsequence of the current sequence. Our main contribution is the first exact dynamic LIS algorithm with sublinear update time. More precisely, we present a randomized algorithm that performs each operation in time O(n4/5) and, after each update, reports the answer to the LIS problem correctly with high probability. We use several novel techniques and observations for this algorithm that may find applications in future work. In the second part of the paper, we study approximate dynamic LIS algorithms, which are allowed to underestimate the solution size within a bounded multiplicative factor. In this setting, we give a deterministic (1−o(1))-approximation algorithm with update time O(no(1)). This result improves upon the previous work of Mitzenmacher and Seddighin (STOC’20) that provides an Ω(єO(1/є))-approximation algorithm with update time O(nє) for any є > 0.
TASKILLAN - A simulation to predict the validity of multiple resource models of aviation workload

The objective of this research was to establish the validity of predictive models of workload in the context of a controlled simulation of a helicopter flight mission. The models that were evaluated contain increasing levels of sophistication regarding their assumptions about the competition for processing resources underlying multiple task performance. Ten subjects performed the simulation which involved various combinations of a low level flight task with three cognitive side tasks, pertaining to navigation, spatial awareness and computation. Side task information was delivered auditorily or visually. Results indicated that subjective workload is best predicted by relatively simple models that simply integrate the total demands of tasks over time (r = 0.65). In contrast, performance is not well predicted by these models (r < .10), but is best predicted by models that assume differential competition between processing resources (r = 0.47). The relevance of these data to predictive models and to the use o...
Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets

This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exch...
AccuCor2: isotope natural abundance correction for dual-isotope tracer experiments.

Stable isotope labeling techniques have been widely applied in the field of metabolomics and proteomics. Before the measured mass spectral data can be used for quantitative analysis, it must be accurately corrected for isotope natural abundance and tracer isotopic impurity. Despite the increasing popularity of dual-isotope tracing strategy such as 13C-15N or 13C-2H, there are no accurate tools for correcting isotope natural abundance for such experiments in a resolution-dependent manner. Here, we present AccuCor2 as an R-based tool to perform the correction for 13C-15N or 13C-2H labeling experiments. Our method uses a newly designed algorithm to construct the correction matrices that link labeling pattern and measured mass fractions, then use non-negative least-squares to solve the labeling patterns. Our results show that the dual-isotope experiments often require a mass resolution that is high enough to resolve 13C and 15N or 13C and 2H. Otherwise, the labeling pattern is not solvable. However, this mass resolution may not be sufficiently high to resolve other non-tracer elements such as oxygen or sulfur from the tracer elements. Therefore, we design AccuCor2 to perform the correction based on the actual mass resolution of the measurements. Using both simulated and experimental data, we show that AccuCor2 performs accurate and resolution-dependent correction for dual-isotope tracer data. The authors developed AccuCor2 as the first resolution-dependent method for accurate isotope natural abundance correction of experimental data generated from dual-isotope tracers. They show that such correction requires a minimum resolution to resolve tracer isotopologues. AccuCor2 showed improved accuracy more than previously developed tools, which assume infinite resolution of the instrument.
A critical role of hepatic GABA in the metabolic dysfunction and hyperphagia of obesity

Hepatic lipid accumulation is a hallmark of type II diabetes (T2D) associated with hyperinsulinemia, insulin resistance, and hyperphagia. Hepatic synthesis of GABA, catalyzed by GABA-transaminase (GABA-T), is upregulated in obese mice. To assess the role of hepatic GABA production in obesity-induced metabolic and energy dysregulation, we treated mice with two pharmacologic GABA-T inhibitors and knocked down hepatic GABA-T expression using an antisense oligonucleotide. Hepatic GABA-T inhibition and knockdown decreased basal hyperinsulinemia and hyperglycemia and improved glucose intolerance. GABA-T knockdown improved insulin sensitivity assessed by hyperinsulinemic-euglycemic clamps in obese mice. Hepatic GABA-T knockdown also decreased food intake and induced weight loss without altering energy expenditure in obese mice. Data from people with obesity support the notion that hepatic GABA production and transport are associated with serum insulin, homeostatic model assessment for insulin resistance (HOMA-IR), T2D, and BMI. These results support a key role for hepatocyte GABA production in the dysfunctional glucoregulation and feeding behavior associated with obesity.
Three results concerning Auslander algebras

Our first result provides a new characterization of Auslander algebras using a property of hereditary torsion pairs. The second result shows an Auslander algebra Λ is left or right glued if and onl...
Wasserstein Distributionally Robust Inverse Multiobjective Optimization.

Inverse multiobjective optimization provides a general framework for the unsupervised learning task of inferring parameters of a multiobjective decision making problem (DMP), based on a set of observed decisions from the human expert. However, the performance of this framework relies critically on the availability of an accurate DMP, sufficient decisions of high quality, and a parameter space that contains enough information about the DMP. To hedge against the uncertainties in the hypothetical DMP, the data, and the parameter space, we investigate in this paper the distributionally robust approach for inverse multiobjective optimization. Specifically, we leverage the Wasserstein metric to construct a ball centered at the empirical distribution of these decisions. We then formulate a Wasserstein distributionally robust inverse multiobjective optimization problem (WRO-IMOP) that minimizes a worst-case expected loss function, where the worst case is taken over all distributions in the Wasserstein ball. We show that the excess risk of the WRO-IMOP estimator has a sub-linear convergence rate. Furthermore, we propose the semi-infinite reformulations of the WRO-IMOP and develop a cutting-plane algorithm that converges to an approximate solution in finite iterations. Finally, we demonstrate the effectiveness of our method on both a synthetic multiobjective quadratic program and a real world portfolio optimization problem.
Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree

The widely used alternating least squares (ALS) algorithm for the canonical polyadic (CP) tensor decomposition is dominated in cost by the matricized-tensor times Khatri-Rao product (MTTKRP) kernel. This kernel is necessary to set up the quadratic optimization subproblems. State-of-the-art parallel ALS implementations use dimension trees to avoid redundant computations across MTTKRPs within each ALS sweep. In this paper, we propose two new parallel algorithms to accelerate CP-ALS. We introduce the multi-sweep dimension tree (MSDT) algorithm, which requires the contraction between an order N input tensor and the first-contracted input matrix once every $(N-1)/N$ sweeps. This algorithm reduces the leading order computational cost by a factor of $2(N-1)/N$ relative to the best previously known approach. In addition, we introduce a more communication-efficient approach to parallelizing an approximate CP-ALS algorithm, pairwise perturbation. This technique uses perturbative corrections to the subproblems rather than recomputing the contractions, and asymptotically accelerates ALS. Our benchmark results on 1024 processors on the Stampede2 supercomputer show that CP decomposition obtains a 1.25X speed-up from MSDT and a 1.94X speedup from pairwise perturbation compared to the state-of-the-art dimension-tree based CP-ALS implementations.
Exploratory data analysis for airline disruption management

Abstract Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions. To that effect, this paper applies macroscopic and microscopic techniques by way of basic statistics and machine learning, respectively, to analyze historical scheduling and operations data from a major airline in the United States. Macroscopic results reveal that majority of irregular operations in airline schedule that occurred over a one-year period stemmed from disruptions due to flight delays, while microscopic results validate different modeling assumptions about key drivers for airline disruption management like turnaround as a Gaussian process.
Online Domain Adaptation for Continuous Cross-Subject Liver Viability Evaluation Based on Irregular Thermal Data

Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking an invasive biopsy of the liver. Recently, people have started to...
Ultrafast measurements of the interfacial spin Seebeck effect in Au and rare-earth iron-garnet bilayers

We investigate picosecond spin currents across Au/iron-garnet interfaces in response to ultrafast laser heating of the electrons in the Au film. In the picoseconds after optical heating, interfacial spin currents occur due to an interfacial temperature difference between electrons in the metal and magnons in the insulator. We report measurements of this interfacial longitudinal spin Seebeck effect between Au and rare-earth iron-garnet insulators, i.e., $\mathrm{R}{\mathrm{E}}_{3}\mathrm{F}{\mathrm{e}}_{5}{\mathrm{O}}_{12}$, where RE is Y, Eu, Tb, Tm. By systematically varying the rare-earth element, we modify the total magnetic moment of the iron garnet. We use time-domain thermoreflectance measurements to characterize the thermal response of the bilayer to ultrafast optical heating. We use time-resolved magneto-optic Kerr effect measurements of the Au layer to measure the time evolution of spin accumulation in the Au film. Replacing Y with other rare earths enhances the electron-magnon conductance ${G}_{e\ensuremath{-}m}$ at the Au iron-garnet interface by as much as a factor of 3. The electron-magnon conductance does not follow the trend of either the total magnetization of the iron garnet or the magnetic moment of the rare earth.
Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.
Gauge-invariant perturbation expansion in powers of electric charge for the density-of-states of a network model for charged-particle motion in a uniform background magnetic flux density

An explicitly gauge-invariant expansion in powers of e/ℏ times the magnetic flux density is formally obtained for the density of states [as characterized by the trace of the resolvent G = (ω−h)−1] of a charged particle moving on a Hermitian quantum network that is embedded in a Euclidean background that supports a uniform magnetic flux density. The explicit expressions, given here up to third order in the flux density, are also valid for the “local trace” (the trace of PiG, where Pi is the projector on a network node) and do not appear to have been previously given.
Multi-Output Random Forest Regression to Emulate the Earliest Stages of Planet Formation

In the current paradigm of planet formation re-search, it is believed that the first step to forming massive bodies (such as asteroids and planets) requires that small interstellar dust grains floating through space collide with each other and grow to larger sizes. The initial formation of these pebbles is governed by an integro-differential equation known as the Smoluchowski coagulation equation [1], to which analytical solutions are intractable for all but the simplest possible scenarios. While brute-force methods of approximation have been developed, they are computationally costly, currently making it infeasible to simulate this process including other physical processes relevant to planet formation, and across the very large range of scales on which it occurs. In this paper, we take a machine learning approach to designing a system for a much faster approximation. We develop a multi-output random forest regression model trained on brute-force simulation data to approximate distributions of dust particle sizes in protoplanetary disks at different points in time. The performance of our random forest model is measured against the existing brute-force models, which are the standard for realistic simulations. Results indicate that the random forest model can generate highly accurate predictions relative to the brute-force simulation results, with an R2 of 0.97, and do so significantly faster than brute-force methods.
Detection of Hidden Attacks on Cyber-Physical Systems from Serial Magnitude and Sign Randomness Inconsistencies

Stealthy false data injection attacks on cyber-physical systems (CPSs) introduce erroneous measurement information to on-board sensors with the purpose to degrade system performance. An intelligent attacker is able to leverage knowledge of the system model and noise characteristics to alter sensor measurements while remaining undetected. To achieve this objective, the stealthy attack sequence is designed such that the detector performs similarly in the attacked and attack-free cases. Consequently, an attacker that wants to remain hidden will leave behind traces of inconsistent behavior, contradicting the system model. To deal with this problem, we propose a runtime monitor to find these inconsistencies in sensor measurements by monitoring for serial inconsistencies of the detection test measure. Specifically, we employ the chi-square fault detection procedure to monitor the magnitude and signed sequence of its chi-square test measure. We validate our approach with simulations on an unmanned ground vehicle (UGV) under stealthy attacks and compare the detection performance with various state-of-the-art anomaly detectors.
On the Regret Analysis of Online LQR Control with Predictions

In this paper, we study the dynamic regret of online linear quadratic regulator (LQR) control with time-varying cost functions and disturbances. We consider the case where a finite look-ahead window of cost functions and disturbances are available at each stage. The online control algorithm studied in this paper falls into the category of model predictive control (MPC) with a particular choice of terminal costs to ensure exponential stability. It is proved that, when predictions are accurate, the regret of such an online algorithm decays exponentially fast with the length of predictions. The impact of inaccurate prediction on disturbances is also investigated, showing that errors of long-term predictions have an exponentially diminishing effect on dynamic regret.
Enabling simulation of high‐dimensional micro‐macro biophysical models through hybrid CPU and multi‐GPU parallelism

Micro-macro models provide a powerful tool to study the relationship between microscale mechanisms and emergent macroscopic behavior. However, the detailed microscopic modeling may require tracking and evolving a high-dimensional configuration space at high computational cost. In this work, we present a parallel algorithm for simulation a high-dimensional micro-macro model of a gliding motility assay. We utilize a holistic approach aligning the data residency and simulation scales with the hybrid CPU and multi-GPU hardware. With a combination of algorithmic modifications, GPU optimizations, and scaling to multiple GPUs, we achieve speedup factors of up to 27 over our previous hybrid CPU-GPU implementation and up to 540 over our single-threaded implementation. This approach enables micro-macro simulations of higher complexity and resolution than would otherwise be feasible.