cssi/SciSciGPT-SciSciNet huggingface dataset:
SYK wormhole formation in real time

We study the real time formation of the ground state of two coupled SYK models. This is a highly entangled state which is close to the thermofield double state and can be viewed as a wormhole. We start from a high temperature state, we let it cool by coupling to a cold bath. We numerically solve for the large N dynamics. Our main result is that the system forms a wormhole by going through a region with negative specific heat, taking time that is independent of N. The dynamics is smooth everywhere and it seems to follow the equilibrium thermodynamic configurations of the microcanonical ensemble. Also we comment on the relation between this coupled SYK model and Jackiw-Teitelboim gravity theory with bulk fields.
Deployable Tensegrity Lunar Tower

A tensegrity tower design to support a given payload for the moon mining operation is proposed in this paper. A non-linear optimization problem for the minimal-mass structure design is posed and solved, subject to the yielding constraints for strings and yielding and buckling constraints for bars in the presence of lunar gravity. The optimization variables for this non-linear problem are structural complexity and pre-stress in the strings. Apart from local failure constraints of yielding and buckling, global buckling is also considered. The structure designed as a deployable tower is a TnD1 tensegrity structure. A case study demonstrates the feasibility and advantage of the tower design. The principles developed in this paper are also applicable for building other structures on the Earth or other planets.
Exceedance Statistics of Accelerations Resulting from Thruster Firings on the Apollo-Soyuz Mission

Spacecraft acceleration resulting from firings of vernier control system thrusters is an important consideration in the design, planning, execution and post-flight analysis of laboratory experiments in space. In particular, scientists and technologists involved with the development of experiments to be performed in space in many instances required statistical information on the magnitude and rate of occurrence of spacecraft accelerations. Typically, these accelerations are stochastic in nature, so that it is useful to characterize these accelerations in statistical terms. Statistics of spacecraft accelerations are summarized. Previously announced in STAR as N82-12127
Advances in coherent coupling between magnons and acoustic phonons

The interaction between magnetic and acoustic excitations has recently inspired many interdisciplinary studies ranging from fundamental physics to circuit implementation. Specifically, the exploration of their coherent interconversion enabled via the magnetoelastic coupling opens a new playground combining straintronics and spintronics and provides a unique platform for building up on-chip coherent information processing networks with miniaturized magnonic and acoustic devices. In this Perspective, we will focus on the recent progress of magnon–phonon coupled dynamic systems, including materials, circuits, imaging, and new physics. In particular, we highlight the unique features such as nonreciprocal acoustic wave propagation and strong coupling between magnons and phonons in magnetic thin-film systems, which provide a platform for their coherent manipulation and transduction. We will also review the frontiers of surface acoustic wave resonators in coherent quantum transduction and discuss how the novel acoustic circuit design can be applied in microwave spintronics.
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification.

Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans. Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the ever-increasing biomedical literature poses unique challenges. We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement. Through automatic and human evaluation on two datasets, we demonstrate that BBAEG performs stronger attack with better language fluency, semantic coherence as compared to prior work.
Cellular Calcium Levels Influenced by NCA-2 Impact Circadian Period Determination in Neurospora.

Intracellular calcium signaling has been implicated in the control of a variety of circadian processes in animals and plants, but its role in microbial clocks has remained largely cryptic. To examine the role of intracellular Ca2+ in the Neurospora clock, we screened mutants with knockouts of calcium transporter genes and identified a gene encoding a calcium exporter, nca-2, uniquely as having significant period effects. The loss of NCA-2 results in an increase in the cytosolic calcium level, and this leads to hyper-phosphorylation of core clock components, FRQ and WC-1, and a short period, as measured by both the core oscillator and the overt clock. Genetic analyses showed that mutations in certain frq phospho-sites and in Ca2+-calmodulin-dependent kinase 2 (camk-2) are epistatic to nca-2 in controlling the pace of the oscillator. These data are consistent with a model in which elevated intracellular Ca2+ leads to the increased activity of CAMK-2, leading to enhanced FRQ phosphorylation, accelerated closure of the circadian feedback loop, and a shortened circadian period length. At a mechanistic level, some CAMKs undergo more auto-phosphorylations in the Δnca-2 mutant, consistent with high calcium levels in the Δnca-2 mutant influencing the enzymatic activities of CAMKs. NCA-2 interacts with multiple proteins, including CSP-6, a protein known to be required for circadian output. Most importantly, the expression of nca-2 is circadian clock-controlled at both the transcriptional and translational levels, and this in combination with the period effects seen in strains lacking NCA-2 firmly places calcium signaling within the larger circadian system, where it acts as both an input to and an output from the core clock. IMPORTANCE Circadian rhythms are based on cell-autonomous, auto-regulatory feedback loops formed by interlocked positive and negative arms, and they regulate myriad molecular and cellular processes in most eukaryotes, including fungi. Intracellular calcium signaling is also a process that impacts a broad range of biological events in most eukaryotes. Clues have suggested that calcium signaling can influence circadian oscillators through multiple pathways; however, mechanistic details have been lacking in microorganisms. When we built on prior work describing calcium transporters in the fungus Neurospora, one such transporter, NCA-2, was identified as a regulator of circadian period length. Increased intracellular calcium levels caused by the loss of NCA-2 resulted in overactivation of calcium-responsive protein kinases, in turn leading to a shortened circadian period length. Importantly, the expression of NCA-2 is itself controlled by the molecular clock. In this way, calcium signaling can be seen as providing both input to and output from the circadian system.
Comparison of the Effects of Velocity and Range Triggers on Trajectory Dispersions for the Mars 2020 Mission

Mars 2020, the next planned U.S. rover mission to land on Mars, is based on the design of the successful 2012 Mars Science Laboratory (MSL) mission. Mars 2020 retains most of the entry, descent, and landing (EDL) sequences of MSL, including the closed-loop entry guidance scheme based on the Apollo guidance algorithm. However, unlike MSL, Mars 2020 will trigger the parachute deployment and descent sequence on range trigger rather than the previously used velocity trigger. This difference will greatly reduce the landing ellipse sizes. Additionally, the relative contribution of each models to the total ellipse sizes have changed greatly due to the switch to range trigger. This paper considers the effect on trajectory dispersions due to changing the trigger schemes and the contributions of these various models to trajectory and EDL performance.
Si/SiO2 Interface Studies by Immersion Ellipsometry

The mechanisms associated with Si/SiO 2 interface annealing and thermal oxidation conditions were studied by spectroscopic immersion ellipsometry. Essentially, this surface sensitive ellipsometry technique uses liquids that match the refractive index of the films, thereby optically removing the films. With the use of an optical model, it is shown that at high annealing temperatures viscous relaxation dominates, while at low annealing temperatures the suboxide reduction is apparent. It is also shown that with the thickening SiO 2 overlayer, the thickness of the suboxide layer at the interface increases and the average radius of the crystalline silicon protrusions decreases for the three different orientation studied. These results are consistent with the commonly accepted Si oxidation model.
Probabilistic forecasting of the Arctic sea ice edge with contour modeling

Sea ice, or frozen ocean water, freezes and melts every year in the Arctic. Forecasts of where sea ice will be located weeks to months in advance have become more important as the amount of sea ice declines due to climate change, for maritime planning and other uses. Typical sea ice forecasts are made with ensemble models, physics-based models of sea ice and the surrounding ocean and atmosphere. This paper introduces Mixture Contour Forecasting, a method to forecast sea ice probabilistically using a mixture of two distributions, one based on postprocessed output from ensembles and the other on observed sea ice patterns in recent years. At short lead times, these forecasts are better calibrated than unadjusted dynamic ensemble forecasts and other statistical reference forecasts. To produce these forecasts, a statistical technique is introduced that directly models the sea ice edge contour, the boundary around the region that is ice-covered. Mixture Contour Forecasting and reference methods are evaluated for monthly sea ice forecasts for 2008–2016 at lead times ranging from 0.5–6.5 months using one of the European Centre for Medium-Range Weather Forecasts ensembles.
Prediction of jump phenomena in rotationally-coupled maneuvers of aircraft, including nonlinear aerodynamic effects

An analytical method has been developed for predicting critical control inputs for which nonlinear rotational coupling may cause sudden jumps in aircraft response. The analysis includes the effect of aerodynamics which are nonlinear in angle of attack. The method involves the simultaneous solution of two polynomials in roll rate, whose coefficients are functions of angle of attack and the control inputs. Results obtained using this procedure are compared with calculated time histories to verify the validity of the method for predicting jump-like instabilities.
Differential excitability of PV and SST neurons results in distinct functional roles in inhibition stabilization of Up-states

Up states are the best studied example of an emergent neural dynamic regime. Computational models based on a single class of inhibitory neurons indicate that Up states reflect bistable dynamic systems in which positive feedback is stabilized by strong inhibition and predict a paradoxical effect in which increased drive to inhibitory neurons results in decreased inhibitory activity. To date, however, computational models have not incorporated empirically defined properties of parvalbumin (PV) and somatostatin (SST) neurons. Here we first experimentally characterized the frequency-current (F-I) curves of pyramidal (Pyr), PV, and SST neurons from mice of either sex, and confirmed a sharp difference between the threshold and slopes of PV and SST neurons. The empirically defined F-I curves were incorporated into a three-population computational model that simulated the empirically derived firing rates of pyramidal, PV, and SST neurons. Simulations revealed that the intrinsic properties were sufficient to predict that PV neurons are primarily responsible for generating the nontrivial fixed points representing Up states. Simulations and analytical methods demonstrated that while the paradoxical effect is not obligatory in a model with two classes of inhibitory neurons, it is present in most regimes. Finally, experimental tests validated predictions of the model that the Pyr ↔ PV inhibitory loop is stronger than the Pyr ↔ SST loop.SIGNIFICANCE STATEMENT Many cortical computations, such as working memory, rely on the local recurrent excitatory connections that define cortical circuit motifs. Up states are among the best studied examples of neural dynamic regimes that rely on recurrent excitatory excitation. However, this positive feedback must be held in check by inhibition. To address the relative contribution of PV and SST neurons, we characterized the intrinsic input-output differences between these classes of inhibitory neurons and, using experimental and theoretical methods, show that the higher threshold and gain of PV leads to a dominant role in network stabilization.
Foreclosure spillovers and individual well-being: Evidence from the Great Recession

Abstract This paper explores the causal effect of foreclosure on individual well-being and social capital. Using plausibly exogenous variation in the timing of interest rate changes on different types of adjustable rate mortgages (ARMs), we find that a 10% rise in foreclosures is associated with a 0.58% and 0.28% decline in current and expected future life satisfaction. These effects are primarily driven by the effects on local optimism and social capital. The results are consistent with models of spatial externalities where large-scale shocks generate adverse effects on communities, not just individuals.
Simulating near-field enhancement in transmission of airborne viruses with a quadrature-based model.

Some infectious diseases, such as influenza, tuberculosis, and SARS-CoV-2, may be transmitted when virus-laden particles expelled from an infectious person are inhaled by someone else, which is known as the airborne transmission route. These virus-laden particles are more concentrated in the expiratory jet of an infectious person than elsewhere in a well-mixed room, but this near-field enhancement in virion exposure has not been well quantified. Transmission of airborne viruses depends on factors that are inherently variable and, in many cases, poorly constrained, and quantifying this uncertainty requires large ensembles of model simulations that span the variability in input parameters. However, models that are well-suited to simulate the near-field evolution of respiratory particles are also computationally expensive, which limits the exploration of parametric uncertainty. In order to perform many simulations that span the wide variability in factors governing airborne transmission, we developed the Quadrature-based model of Respiratory Aerosol and Droplets (QuaRAD). QuaRAD is an efficient framework for simulating the evolution of virus-laden particles after they are expelled from an infectious person, their deposition to the nasal cavity of a susceptible person, and the subsequent risk of initial infection. We simulated 10 000 scenarios to quantify the risk of initial infection by a particular virus, SARS-CoV-2. The predicted risk of infection was highly variable among scenarios and, in each scenario, was strongly enhanced near the infectious individual. In more than 50% of scenarios, the physical distancing needed to avoid near-field enhancements in airborne transmission was beyond the recommended safe distance of two meters (six feet) if the infectious person is not wearing a mask, though this distance defining the near-field extent was also highly variable among scenarios; the variability in the near-field extent is explained predominantly by variability in expiration velocity. Our findings suggest that maintaining at least two meters of distance from an infectious person greatly reduces exposure to airborne virions; protections against airborne transmission, such as N95 respirators, should be available when distancing is not possible.
Proving Integrality Gaps without Knowing the Linear Program

During the past decade we have had much success in proving (using probabilistically checkable proofs or PCPs) that computing approximate solutions to NP-hard optimization problems such as CLIQUE, COLORING, SET-COVER etc. is no easier than computing optimal solutions.
Computationally Tractable Stability Criteria for Exogenously Switched Model Predictive Control

Although state-based switching is well-studied in the context of model predictive control (MPC), the theory for exogenous switches is less advanced. External switching affects many systems and is of growing importance due to the increasing use of networked control systems where abrupt changes in communication lines induce switched dynamics. In search of practical means to implement MPC for switched systems, this paper examines techniques to ensure their stability. Several existing methods are summarized and a novel approach is suggested to reduce computational costs while unifying the process for both average and minimum dwell time constraints. Two numerical examples contrast current and previous works.
Who/What Is My Teammate? Team Composition Considerations in Human–AI Teaming

There are many unknowns regarding the characteristics and dynamics of human–AI teams, including a lack of understanding of how certain human–human teaming concepts may or may not apply to human–AI teams and how this composition affects team performance. This article outlines an experimental research study that investigates essential aspects of human–AI teaming such as team performance, team situation awareness, and perceived team cognition in various mixed composition teams (human-only, human–human–AI, human–AI–AI, and AI-only) through a simulated emergency response management scenario. Results indicate dichotomous outcomes regarding perceived team cognition and performance metrics, as perceived team cognition was not predictive of performance. Performance metrics like team situational awareness and team score showed that teams composed of all human participants performed at a lower level than mixed human–AI teams, with the AI-only teams attaining the highest performance. Perceived team cognition was highest in human-only teams, with mixed composition teams reporting perceived team cognition 58% below the all-human teams. These results inform future mixed teams of the potential performance gains in utilizing mixed teams’ over human-only teams in certain applications, while also highlighting mixed teams’ adverse effects on perceived team cognition.
Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination

The estimation of a large covariance matrix is challenging when the dimension p is large relative to the sample size n. Common approaches to deal with the challenge have been based on thresholding ...
Vibration Reduction on Helicopter Rotors Using Open-Loop Flow Control

The effectiveness of active flow control for reducing vibrations in helicopter rotors is examined using numerical simulations. The flow control actuators employ pulsed fluidic jets on the pressure ...
Moiré Pattern of Interference Dislocations and Superfluidity in Condensate of Indirect Excitons

We present a new mechanism, the moire effect, which leads to the appearance of dislocations in interference patterns. Remote interference dislocations in condensate of indirect excitons originate from the moire effect and evidence exciton superfluidity.
Phantom Evaluation of C-SPECT Performance for Myocardial Perfusion Imaging

Introduction: Myocardial Perfusion Imaging (MPI) remains a cornerstone of cardiovascular care with 8 million scans per year in the US. Relative to current SPECT technology, cardiac PET has improved sensitivity and specificity and allows for dynamic imaging and thus myocardial blood flow (MBF) quantification. Given growing awareness of the role of the coronary mirovasculature, the latter is particularly useful. Recently, several SPECT scanners have been developed that can acquire all lines of response simultaneously, trading a large Field of View (FOV) for multiple simultaneous projections of the cardiac region, potentially enabling fast dynamic scanning and MBF measurements in SPECT. C-SPECT is such a system with the additional capability of adaptive imaging, which allows for different resolution-sensitivity-FOV operating points. This can be used, for example, to switch between high-sensitivity mode for dynamic imaging and high-resolution mode for static imaging. In this study, we evaluate the capabilities of this novel system for identifying cardiac lesions in the high-resolution mode. Methods: We acquired high-count scans of emission data using an anthropomorphic torso phantom filled with aqueous 99mTc-pertechnetate for male and female configurations with torso, myocardium, and breast emission data acquired separately. The myocardium was acquired without a lesion and with four different positions each for six lesions. The projection data were mixed into ensembles in post-acquisition processing and reconstructed. Receiver Operating Characteristic (ROC) curves were determined and the areas under the ROC curves were determined. Results: The area under the ROC curve (AROC) was nearly 1 for all lesions imaged with 5-minute and 10-minute scans. The AROC for 2-minute scans varied by lesion location and decreased for females. Discussion: C-SPECT is very effective in identifying lesions, including focal subendocardial lesions for both male and female anatomy. The trends seen in the ROC curves for the 2-minute scans are consistent with expectations that lateral lesions were the easiest to identify and that additional attenuation and scatter in the female phantoms decreased image quality. Conclusions: C-SPECT is capable of reliably identifying cardiac lesions of varying severity in male and female phantoms using realistic background distributions for MPI down to scans of two minutes.
Dynamical cluster approximation study of electron localization in the extended Hubbard model

We perform a detailed study of the phase transitions and mechanisms of electron localization in the extended Hubbard model using the dynamical cluster approximation on a $2\ifmmode\times\else\texttimes\fi{}2$ cluster. We explore the interplay of charge order and Mott physics. We find that a nearest-neighbor Coulomb interaction $V$ causes ``screening'' effects close to the Mott phase transition, pushing the phase boundary to larger values of $U$. We also demonstrate the different effects of $V$ on correlations in metallic and insulating regimes, and document the different correlation aspects of charge order and Mott states.
A Universal Cloud Composition on the Nightsides of Hot Jupiters

The day and nightside temperatures of hot Jupiters are diagnostic of heat transport processes in their atmospheres. Recent observations have shown that the nightsides of hot Jupiters are a nearly constant 1100 K for a wide range of equilibrium temperatures (T$_{eq}$), lower than those predicted by 3D global circulation models. Here we investigate the impact of nightside clouds on the observed nightside temperatures of hot Jupiters using an aerosol microphysics model. We find that silicates dominate the cloud composition, forming an optically thick cloud deck on the nightsides of all hot Jupiters with T$_{eq}$ $\leq$ 2100 K. The observed nightside temperature is thus controlled by the optical depth profile of the silicate cloud with respect to the temperature-pressure profile. As nightside temperatures increase with T$_{eq}$, the silicate cloud is pushed upwards, forcing observations to probe cooler altitudes. The cloud vertical extent remains fairly constant due to competing impacts of increasing vertical mixing strength with T$_{eq}$ and higher rates of sedimentation at higher altitudes. These effects, combined with the intrinsically subtle increase of the nightside temperature with T$_{eq}$ due to decreasing radiative timescale at higher instellation levels lead to low, constant nightside photospheric temperatures consistent with observations. Our results suggest a drastic reduction in the day-night temperature contrast when nightside clouds dissipate, with the nightside emission spectra transitioning from featureless to feature-rich. We also predict that cloud absorption features in the nightside emission spectra of hot Jupiters should reach $\geq$100 ppm, potentially observable with the James Webb Space Telescope.
High-Bandwidth Nonlinear Control for Soft Actuators with Recursive Network Models.

We present a high-bandwidth, lightweight, and nonlinear output tracking technique for soft actuators that combines parsimonious recursive layers for forward output predictions and online optimization using Newton-Raphson. This technique allows for reduced model sizes and increased control loop frequencies when compared with conventional RNN models. Experimental results of this controller prototype on a single soft actuator with soft positional sensors indicate effective tracking of referenced spatial trajectories and rejection of mechanical and electromagnetic disturbances. These are evidenced by root mean squared path tracking errors (RMSE) of 1.8 mm using a fully connected (FC) substructure, 1.62 mm using a gated recurrent unit (GRU) and 2.11 mm using a long short term memory (LSTM) unit, all averaged over three tasks. Among these models, the highest flash memory requirement is 2.22 kB enabling co-location of controller and actuator.
Hedge Funds vs. Alternative Risk Premia

Alternative risk premia (ARP) are designed to provide low-cost exposures to long–short risk premia often embedded in hedge fund returns. This article describes the performance of the ARP market in ...
Spin-Label Investigation of Polyurethane Networks

Abstract Two types of polyurethane networks were examined using the spin-label technique. Nitroxide free radicals which are stable under moderate conditions were covalently attached to the network chains of amorphous polymers based on (1) poly(tetramethylene glycol), p,p′-diphenylmethane diisocyanate (MDI), trimethylolpropane, and (2) ethylene glycol, MDI, glycerine. Polymer (1) had very low “hard block” content, polymer (2) a very high content. Mechanical properties and differential thermal analysis measurements were made on polymer (1). Electron spin resonance spectra of the samples were obtained in the unstrained, strained, and swollen states. Large differences in the spectra were observed, depending on chemical composition, temperature, and state of swelling. Transitions from “frozen” to “rapid motion” type of spectra occurred at 70° for polymer (1), at −10° for polymer (1) swollen to equilibrium, and were unobserved up to 150° for polymer (2). Since the nitroxide label was necessarily adjacent to MDI...
Fast and Space-Efficient Construction of AVL Grammars from the LZ77 Parsing.

Grammar compression is, next to Lempel-Ziv (LZ77) and run-length Burrows-Wheeler transform (RLBWT), one of the most flexible approaches to representing and processing highly compressible strings. The main idea is to represent a text as a context-free grammar whose language is precisely the input string. This is called a straight-line grammar (SLG). An AVL grammar, proposed by Rytter [Theor. Comput. Sci., 2003] is a type of SLG that additionally satisfies the AVL property: the heights of parse trees for children of every nonterminal differ by at most one. In contrast to other SLG constructions, AVL grammars can be constructed from the LZ77 parsing in compressed time: 𝒪(z log n) where z is the size of the LZ77 parsing and n is the length of the input text. Despite these advantages, AVL grammars are thought to be too large to be practical. We present a new technique for rapidly constructing a small AVL grammar from an LZ77 or LZ77-like parse. Our algorithm produces grammars that are always at least five times smaller than those produced by the original algorithm, and usually not more than double the size of grammars produced by the practical Re-Pair compressor [Larsson and Moffat, Proc. IEEE, 2000]. Our algorithm also achieves low peak RAM usage. By combining this algorithm with recent advances in approximating the LZ77 parsing, we show that our method has the potential to construct a run-length BWT in about one third of the time and peak RAM required by other approaches. Overall, we show that AVL grammars are surprisingly practical, opening the door to much faster construction of key compressed data structures.
Infusing theory into deep learning for interpretable reactivity prediction

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces.
Multipliers and unicentral Leibniz algebras

In this paper, we prove Leibniz analogues of results found in Peggy Batten’s 1993 dissertation. We first construct a Hochschild–Serre-type spectral sequence of low dimension, which is used to chara...
Using Causal Analysis for Conceptual Deep Learning Explanation.

Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expert and is expressed using concepts or terminology that is meaningful to the clinicians. To provide such explanation, we first associate the hidden units of the classifier to clinically relevant concepts. We take advantage of radiology reports accompanying the chest X-ray images to define concepts. We discover sparse associations between concepts and hidden units using a linear sparse logistic regression. To ensure that the identified units truly influence the classifier’s outcome, we adopt tools from Causal Inference literature and, more specifically, mediation analysis through counterfactual interventions. Finally, we construct a low-depth decision tree to translate all the discovered concepts into a straightforward decision rule, expressed to the radiologist. We evaluated our approach on a large chest x-ray dataset, where our model produces a global explanation consistent with clinical knowledge.
An Isogenic Cell Line Panel for Sequence-Based Screening of Targeted Anti-Cancer Drugs

We describe the creation and characterization of an isogenic cell line panel representing common cancer pathways, with multiple features optimized for high-throughput screening. More than 1,800 cell lines from three normal human cells were generated using CRISPR-technologies. Surprisingly, we discovered most of these lines did not result in complete gene inactivation, despite integration of sgRNA at the desired genomic site. However, a subset of the lines harbored true, biallelic disruptions of the targeted tumor suppressor gene, yielding a final panel of 100 well-characterize lines covering 19 pathways frequently subject to loss of function in cancers. This panel included genetic markers optimized for sequence-based ratiometric assays for drug-based screening assays. To illustrate the potential utility of this panel, we developed a multiplexed high-throughput screen that identified Wee1 inhibitor MK-1775 as a selective growth inhibitor of cells with inactivation of TP53. These cell lines and screening approach should prove useful for researchers studying a variety of cellular and biochemical phenomena.
SPOT: A framework for selection of prototypes using optimal transport

In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making. We model the prototype selection problem as learning a sparse (empirical) probability distribution having the minimum OT distance from the target distribution. The learned probability measure supported on the chosen prototypes directly corresponds to their importance in representing the target data. We show that our objective function enjoys a key property of submodularity and propose an efficient greedy method that is both computationally fast and possess deterministic approximation guarantees. Empirical results on several real world benchmarks illustrate the efficacy of our approach.
Interaction effects in graphene in a weak magnetic field

A weak perpendicular magnetic field, $B$, breaks the chiral symmetry of each valley in the electron spectrum of graphene, preserving the overall chiral symmetry in the Brillouin zone. We explore the consequences of this symmetry breaking for the interaction effects in graphene. In particular, we demonstrate that the electron-electron interaction lifetime acquires an anomalous $B$ dependence. Also, the ballistic zero-bias anomaly, $\ensuremath{\delta}\ensuremath{\nu}(\ensuremath{\omega})$, where $\ensuremath{\omega}$ is the energy measured from the Fermi level, emerges at a weak $B$ and has the form $\ensuremath{\delta}\ensuremath{\nu}(B)\ensuremath{\sim}{B}^{2}/{\ensuremath{\omega}}^{2}$. Temperature dependence of the magnetic-field corrections to the thermodynamic characteristics of graphene is also anomalous. We discuss experimental manifestations of the effects predicted. The microscopic origin of the $B$-field sensitivity is an extra phase acquired by the electron wave function resulting from the chirality-induced pseudospin precession.
SSH: A Self-Supervised Framework for Image Harmonization

Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background images. However, collecting large-scale annotated datasets for this task requires complex professional retouching. Instead, we propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "free" natural images without being edited. We reformulate the image harmonization problem from a representation fusion perspective, which separately processes the foreground and background examples, to address the background occlusion issue. This framework design allows for a dual data augmentation method, where diverse [foreground, background, pseudo GT] triplets can be generated by cropping an image with perturbations using 3D color lookup tables (LUTs). In addition, we build a real-world harmonization dataset as carefully created by expert users, for evaluation and benchmarking purposes. Our results show that the proposed self-supervised method outperforms previous state-of-the-art methods in terms of reference metrics, visual quality, and subject user study. Code and dataset are available at \url{this https URL}.
Compact Dupin Hypersurfaces

A hypersurface $M$ in ${\bf R}^n$ is said to be Dupin if along each curvature surface, the corresponding principal curvature is constant. A Dupin hypersurface is said to be proper Dupin if the number of distinct principal curvatures is constant on $M$, i.e., each continuous principal curvature function has constant multiplicity on $M$. These conditions are preserved by stereographic projection, so this theory is essentially the same for hypersurfaces in ${\bf R}^n$ or $S^n$. The theory of compact proper Dupin hypersurfaces in $S^n$ is closely related to the theory of isoparametric hypersurfaces in $S^n$, and many important results in this field concern relations between these two classes of hypersurfaces. In 1985, Cecil and Ryan conjectured on p. 184 of the book, "Tight and Taut Immersions of Manifolds," that every compact, connected proper Dupin hypersurface $M \subset S^n$ is equivalent to an isoparametric hypersurface in $S^n$ by a Lie sphere transformation. This paper gives a survey of progress on this conjecture and related developments.
Fuel-Economical Distributed Model Predictive Control for Heavy-Duty Truck Platoon

This paper proposes a fuel-economical distributed model predictive control design (Eco-DMPC) for a homogenous heavy-duty truck platoon. The proposed control strategy integrates a fuel-optimal control strategy for the leader truck with a distributed formation control for the following trucks in the heavy-duty truck platoon. The fuel-optimal control strategy is implemented by a nonlinear model predictive control (NMPC) design with an instantaneous fuel consumption model. The proposed fuel-optimal control strategy utilizes the preview information of the preceding traffic to achieve the fuel-economical speed planning by avoiding energy-inefficient maneuvers, particularly under transient traffic conditions. The distributed formation control is designed with a serial distributed model predictive control (DMPC) strategy with guaranteed local and string stability. In the DMPC strategy, each following truck acquires the future predicted state information of its predecessor through vehicle connectivity and then applies local optimal control to maintain constant spacing. Simulation studies are conducted to investigate the fuel economy performance of the proposed control strategy and to validate the local and string stability of the platoon under a realistic traffic scenario. Compared with a human-operated platoon and a benchmark formation-controlled platoon, the proposed Eco-DMPC significantly improves fuel economy and road utilization.
Variational Bayesian Optimistic Sampling

We consider online sequential decision problems where an agent must balance exploration and exploitation. We derive a set of Bayesian `optimistic' policies which, in the stochastic multi-armed bandit case, includes the Thompson sampling policy. We provide a new analysis showing that any algorithm producing policies in the optimistic set enjoys $\tilde O(\sqrt{AT})$ Bayesian regret for a problem with $A$ actions after $T$ rounds. We extend the regret analysis for optimistic policies to bilinear saddle-point problems which include zero-sum matrix games and constrained bandits as special cases. In this case we show that Thompson sampling can produce policies outside of the optimistic set and suffer linear regret in some instances. Finding a policy inside the optimistic set amounts to solving a convex optimization problem and we call the resulting algorithm `variational Bayesian optimistic sampling' (VBOS). The procedure works for any posteriors, \ie, it does not require the posterior to have any special properties, such as log-concavity, unimodality, or smoothness. The variational view of the problem has many useful properties, including the ability to tune the exploration-exploitation tradeoff, add regularization, incorporate constraints, and linearly parameterize the policy.
Automated Dynamic Mechanism Design

We study Bayesian automated mechanism design in unstructured dynamic environments, where a principal repeatedly interacts with an agent, and takes actions based on the strategic agent's report of the current state of the world. Both the principal and the agent can have arbitrary and potentially different valuations for the actions taken, possibly also depending on the actual state of the world. Moreover, at any time, the state of the world may evolve arbitrarily depending on the action taken by the principal. The goal is to compute an optimal mechanism which maximizes the principal's utility in the face of the self-interested strategic agent. We give an efficient algorithm for computing optimal mechanisms, with or without payments, under different individual-rationality constraints, when the time horizon is constant. Our algorithm is based on a sophisticated linear program formulation, which can be customized in various ways to accommodate richer constraints. For environments with large time horizons, we show that the principal's optimal utility is hard to approximate within a certain constant factor, complementing our algorithmic result. We further consider a special case of the problem where the agent is myopic, and give a refined efficient algorithm whose time complexity scales linearly in the time horizon. Moreover, we show that memoryless mechanisms do not provide a good solution for our problem, in terms of both optimality and computational tractability. These results paint a relatively complete picture for automated dynamic mechanism design in unstructured environments. Finally, we present experimental results where our algorithms are applied to synthetic dynamic environments with different characteristics, which not only serve as a proof of concept for our algorithms, but also exhibit intriguing phenomena in dynamic mechanism design.
Risk of permanent pacemaker implantation after transcatheter aortic valve replacement: How do we manage beyond risk assessment?

Risk of permanent pacemaker implantation after transcatheter aortic valve replacement: How do we manage beyond risk assessment?
Centralization is about Control, not Protocols: Position Paper

Many common "consumer" applications, i.e., applications widely used by non-technical users, are now provided by a very small number of companies, even if that set of companies differ across geographic regions, or rely on a very small number of implementations even if the applications are largely standards-based. While likely only a partial solution, we can draw on earlier regulatory experiences to facilitate competition or at least lessen the impact of the lack thereof.
Search for dark-photon dark matter in the SuperMAG geomagnetic field dataset

In our recent companion paper [arXiv:2106.00022], we pointed out a novel signature of ultralight kinetically mixed dark-photon dark matter. This signature is a quasi-monochromatic, time-oscillating terrestrial magnetic field that takes a particular pattern over the surface of the Earth. In this work, we present a search for this signal in existing, unshielded magnetometer data recorded by geographically dispersed, geomagnetic stations. The dataset comes from the SuperMAG collaboration and consists of measurements taken with one-minute cadence since 1970, with $\mathcal{O}(500)$ stations contributing in all. We aggregate the magnetic field measurements from all stations by projecting them onto a small set of global vector spherical harmonics (VSH) that capture the expected vectorial pattern of the signal at each station. Within each dark-photon coherence time, we use a data-driven technique to estimate the broadband background noise in the data, and search for excess narrowband power in this set of VSH components; we stack the searches in distinct coherence times incoherently. Following a Bayesian analysis approach that allows us to account for the stochastic nature of the dark-photon dark-matter field, we set exclusion bounds on the kinetic mixing parameter in the dark-photon dark-matter mass range $2\times10^{-18} \text{eV} \lesssim m_{A'} \lesssim 7\times10^{-17} \text{eV}$ (corresponding to frequencies $6\times 10^{-4} \text{Hz}\lesssim f_{A'} \lesssim 2\times 10^{-2} \text{Hz}$). These limits are complementary to various existing astrophysical constraints. Although our main analysis also identifies a number of candidate signals in the SuperMAG dataset, these appear to either fail or be in strong tension with various additional robustness checks we apply to those candidates: we report no robust and significant evidence for a dark-photon dark-matter signal in the SuperMAG dataset.
Memory at your service: fast memory allocation for latency-critical services

Co-location and memory sharing between latency-critical services, such as key-value store and web search, and best-effort batch jobs is an appealing approach to improving memory utilization in multi-tenant datacenter systems. However, we find that the very diverse goals of job co-location and the GNU/Linux system stack can lead to severe performance degradation of latency-critical services under memory pressure in a multi-tenant system. We address memory pressure for latency-critical services via fast memory allocation and proactive reclamation. We find that memory allocation latency dominates the overall query latency, especially under memory pressure. We analyze the default memory management mechanism provided by GNU/Linux system stack and identify the reasons why it is inefficient for latency-critical services in a multi-tenant system. We present Hermes, a fast memory allocation mechanism in user space that adaptively reserves memory for latency-critical services. It advises Linux OS to proactively reclaim memory of batch jobs. We implement Hermes in GNU C Library. Experimental result shows that Hermes reduces the average and the 99th percentile memory allocation latency by up to 54.4% and 62.4% for a micro benchmark, respectively. For two real-world latency-critical services, Hermes reduces both the average and the 99th percentile tail query latency by up to 40.3%. Compared to the default Glibc, jemalloc and TCMalloc, Hermes reduces Service Level Objective violation by up to 84.3% under memory pressure.
Support Recovery Guarantees for Periodic Signals with Nested Periodic Dictionaries

Periodic signals composed of periodic mixtures admit sparse representations in nested periodic dictionaries (NPDs). Therefore, their underlying hidden periods can be estimated by recovering the exact support of said representations. In this paper, support recovery guarantees of such signals are derived both in noise-free and noisy settings. While exact recovery conditions have long been studied in the theory of compressive sensing, existing conditions fall short of yielding meaningful achievability regions in the context of periodic signals with sparse representations in NPDs, in part since existing bounds do not capture structures intrinsic to these dictionaries. We leverage known properties of NPDs to derive several conditions for exact sparse recovery of periodic mixtures in the noise-free setting. These conditions rest on newly introduced notions of nested periodic coherence and restricted coherence, which can be efficiently computed and verified. In the presence of noise, we obtain improved conditions for recovering the exact support set of the sparse representation of the periodic mixture via orthogonal matching pursuit based on the introduced notions of coherence. The theoretical findings are corroborated using numerical experiments for different families of NPDs. Our results show significant improvement over generic recovery bounds as the conditions hold over a larger range of sparsity levels.
Modeling and Solving Graph Synthesis Problems Using SAT-Encoded Reachability Constraints in Picat.

Many constraint satisfaction problems involve synthesizing subgraphs that satisfy certain reachability constraints. This paper presents programs in Picat for four problems selected from the recent LP/CP programming competitions. The programs demonstrate the modeling capabilities of the Picat language and the solving efficiency of the cutting-edge SAT solvers empowered with effective encodings.