cssi/SciSciGPT-SciSciNet huggingface dataset:
Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc. On the contrast, the large-scale network brings high requirements of both memory storage and computation resource, especially for portable medical devices and other embedded systems. In this work, we first train a DNN for pneumonia detection using the dataset provided by RSNA Pneumonia Detection Challenge [4]. To overcome hardware limitation for implementing large-scale networks, we develop a systematic structured weight pruning method with filter sparsity, column sparsity and combined sparsity. Experiments show that we can achieve up to 36x compression ratio compared to the original model with 106 layers, while maintaining no accuracy degradation. We evaluate the proposed methods on an embedded low-power device, Jetson TX2, and achieve low power usage and high energy efficiency.
NE-Table: A Neural key-value table for Named Entities.

Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set which are available at - https://github.com/IBM/ne-table-datasets/
The nonlinear stability regime of the viscous Faraday wave problem

This paper concerns the dynamics of a layer of incompressible viscous fluid lying above a vertically oscillating rigid plane and with an upper boundary given by a free surface. We consider the problem with gravity and surface tension for horizontally periodic flows. This problem gives rise to flat but vertically oscillating equilibrium solutions, and the main thrust of this paper is to study the asymptotic stability of these equilibria in certain parameter regimes. We prove that both with and without surface tension there exists a parameter regime in which sufficiently small perturbations of the equilibrium at time $t = 0$ give rise to global-in-time solutions that decay to equilibrium at an identified quantitative rate.
Fast Trajectory Optimization via Successive Convexification for Spacecraft Rendezvous with Integer Constraints

In this paper we present a fast method based on successive convexification for generating fuel-optimized spacecraft rendezvous trajectories in the presence of mixed-integer constraints. A recently developed paradigm of state-triggered constraints allows to efficiently embed a subset of discrete decision constraints into the continuous optimization framework of successive convexification. As a result, we are able to solve difficult trajectory optimization problems at interactive speeds, as opposed to a mixed-integer programming approach that would require significantly more solution time and computing power. Our method is applied to the real problem of transposition and docking of the Apollo command and service module with the lunar module. We demonstrate that, within seconds, we are able to obtain trajectories that are up to 90 percent more fuel efficient (saving up to 45 kg of fuel) than non-optimization based Apollo-era design targets. Our trajectories take explicit account of minimum thrust pulse width and plume impingement constraints. Both of these constraints are naturally mixed-integer, but we handle them as state-triggered constraints. In its current state, our algorithm will serve as a useful off-line design tool for rapid trajectory trade studies.
How Does Using a Mobile Device Change Investors’ Reactions to Firm Disclosures?

I examine how characteristics of investors’ information access tools change investors’ reactions to firm disclosures. I examine my research question in the context of information choice (i.e., allowing investors to choose the order of information and sections to read within a disclosure) and spatial layout (i.e., how information is displayed when viewing the disclosure). Results of an experiment are consistent with information choice improving investors’ judgments if the disclosure is viewed on a computer screen. Conversely, and consistent with cognitive overload, information choice harms investors’ judgments if the disclosure is viewed on a smaller screen, such as that of a mobile device. Follow‐up experiments show that changing the disclosure presentation to reduce the need to scroll is one way to improve investors’ judgments on a smaller (or mobile) screen. My findings caution firms and regulators about expanding information choice within disclosures without considering the screen size used to access the disclosure.
Reduced European aerosol emissions suppress winter extremes over northern Eurasia

Winter extreme weather events receive major public attention due to their serious impacts1, but the dominant factors regulating their interdecadal trends have not been clearly established2,3. Here, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970–2005, a unique transition period for global aerosol forcing4. Over this period, the local Rossby wave activity and extreme events (top 10% in wave amplitude) exhibited marked declining trends at high latitudes, mainly in northern Eurasia. The combination of long-term observational data and a state-of-the-art climate model revealed the unambiguous signature of anthropogenic aerosols on the wintertime jet stream, planetary wave activity and surface temperature variability on interdecadal timescales. In particular, warming due to aerosol reductions in Europe enhanced the meridional temperature gradient on the jet’s poleward flank and strengthened the zonal wind, resulting in significant suppression in extreme events over northern Eurasia. These results exemplify how aerosol forcing can impact large-scale extratropical atmospheric dynamics, and illustrate the importance of anthropogenic aerosols and their spatiotemporal variability in assessing the drivers of extreme weather in historical and future climate. Anthropogenic aerosol emissions decreased over North America and Europe but increased over Asia since the 1970s. This caused jet stream winds to shift poleward over the Atlantic, decreasing planetary wave activity and partially inhibiting extreme winter weather over northern Eurasia.
Localization and Mapping using Instance-specific Mesh Models

This paper focuses on building semantic maps, containing object poses and shapes, using a monocular camera. This is an important problem because robots need rich understanding of geometry and context if they are to shape the future of transportation, construction, and agriculture. Our contribution is an instance-specific mesh model of object shape that can be optimized online based on semantic information extracted from camera images. Multi-view constraints on the object shape are obtained by detecting objects and extracting category-specific keypoints and segmentation masks. We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes. We evaluate the performance of the proposed approach in simulation and on the KITTI dataset by building maps of car poses and shapes.
Incentives Don't Solve Blockchain's Problems

People need a motive to use and maintain a system. In many of the systems we use today, financial rewards and punishments provide a key incentive to participate and abide by the rules. From salaries to traffic tickets, financial motives are often closely tied to a system's viability. Distributed systems such as blockchain also need a mechanism to motivate good behavior. A blockchain must encourage users to maintain the system while preventing a minority of these users from colluding and gaining disproportionate control. Many popular public blockchains use monetary incentives to encourage users to participate and behave appropriately. But these same incentive schemes create more problems than they solve. Mining rewards cause centralization in proof of work chains such as Bitcoin. Validator rewards and punishments invite attacks in proof of stake chains. This paper argues why these incentive schemes are detrimental to blockchain. It also considers a range of other systems—some of which incorporate monetary incentives, some of which do not—to confirm that monetary incentives may be neither necessary nor sufficient for good user behavior.
Development of a SimpleProbe real-Time PCR Assay for rapid detection and identification of the US novel urethrotropic clade of Neisseria meningitidis ST-11 (US_NmUC)

Urethritis, or inflammation of the urethra, is one of the most common reasons men seek clinical care. Sexually transmitted pathogens including Neisseria gonorrhoeae are responsible for over half of the symptomatic urethritis cases in U.S. men. Recently, clinics in Indianapolis, Columbus, Atlanta, and other U.S. cities began to note increasing numbers of men presenting with urethritis and Gram-negative intracellular diplococci in their urethral smears who test negative for N. gonorrhoeae. Many of these discordant cases, which have periodically reached highs of more than 25% of presumed gonococcal cases in some sexually transmitted infection clinics in the U.S. Midwest, are infected with strains in a novel urethrotropic clade of Neisseria meningitidis ST-11 (US_NmUC). However, no cultivation-independent tests are available for the US_NmUC strains, and prior studies relied on microbial culture and genome sequencing to identify them. Here, we describe a PCR test that can identify the US_NmUC strains and distinguish them from commensal and invasive N. meningitidis strains as well as N. gonorrhoeae. Our SimpleProbe®-based real-time PCR assay targets a conserved nucleotide substitution in a horizontally acquired region of US_NmUC strain genomes. We applied the assay to 241 urine specimens whose microbial compositions had previously been determined by deep shotgun metagenomic sequencing. The assay detected the single US_NmUC positive case in this cohort, with no false positives. Overall, our simple and readily adaptable assay could facilitate investigation of the pathogenesis and epidemiology of the US_NmUC clade.
Backhaul-Aware Uplink Communications in Full-Duplex DBS-Aided HetNets

Drone-mounted base stations (DBSs) are promising solutions to provide ubiquitous connections to users and support many applications in the fifth generation of mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we have investigated the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network, and this problem is decomposed into two sub-problems: the DBS Placement problem (including the vertical position and horizontal position) and the joint UE association, power and bandwidth assignment (Joint-UPB) problem. Since the BUD problem is NP- hard, we propose approximation algorithms to solve the sub-problems and another, named the AA-BUD algorithm, to solve the BUD problem with guaranteed performance. The performance of the AA- BUD algorithm has been demonstrated via extensive simulations, and results show that the AA-BUD algorithm is superior to two benchmark algorithms.
Zero-temperature Glauber dynamics on the 3-regular tree and the median process

In zero-temperature Glauber dynamics, vertices of a graph are given i.i.d. initial spins $$\sigma _x(0)$$ from $$\{-1,+1\}$$ with $${\mathbb {P}}_p(\sigma _x(0) = +1)=p$$ , and they update their spins at the arrival times of i.i.d. Poisson processes to agree with a majority of their neighbors. We study this process on the 3-regular tree $${\mathbb {T}}_3$$ , where it is known that the critical threshold $$p_c$$ , below which $${\mathbb {P}}_p$$ -a.s. all spins fixate to $$-1$$ , is strictly less than 1/2. Defining $$\theta (p)$$ to be the $${\mathbb {P}}_p$$ -probability that a vertex fixates to $$+1$$ , we show that $$\theta $$ is a continuous function on [0, 1], so that, in particular, $$\theta (p_c)=0$$ . To do this, we introduce a new continuous-spin process we call the median process, which gives a coupling of all the measures $${\mathbb {P}}_p$$ . Along the way, we study the time-infinity agreement clusters of the median process, show that they are a.s. finite, and deduce that all continuous spins flip finitely often. In the second half of the paper, we show a correlation decay statement for the discrete spins under $${\mathbb {P}}_p$$ for a.e. value of p. The proof relies on finiteness of a vertex’s “trace” in the median process to derive a stability of discrete spins under finite resampling. Last, we use our methods to answer a question of Howard (J Appl Probab 37:736–747, 2000) on the emergence of spin chains in $${\mathbb {T}}_3$$ in finite time.
Generalized spin fluctuation feedback in heavy fermion superconductors

This paper develops a phenomenological generalization of the spin fluctuation feedback effect, which allows for a unified explanation of the observed multiple superconducting phases in the correlated fermion superconductors UPt${}_{3}$, U${}_{1\ensuremath{-}x}$Th${}_{x}$Be${}_{13}$, and PrOs${}_{4}$Sb${}_{12}$.
Dominant Vibrio cholerae phage exhibits lysis inhibition sensitive to disruption by a defensive phage satellite

Bacteria, bacteriophages that prey upon them, and mobile genetic elements (MGEs) compete in dynamic environments, evolving strategies to sense the milieu. The first discovered environmental sensing by phages, lysis inhibition, has only been characterized and studied in the limited context of T-even coliphages. Here, we discover lysis inhibition in the etiological agent of the diarrheal disease cholera, Vibrio cholerae, infected by ICP1, a phage ubiquitous in clinical samples. This work identifies the ICP1-encoded holin, teaA, and antiholin, arrA, that mediate lysis inhibition. Further, we show that an MGE, the defensive phage satellite PLE, collapses lysis inhibition. Through lysis inhibition disruption a conserved PLE protein, LidI, is sufficient to limit the phage produced from infection, bottlenecking ICP1. These studies link a novel incarnation of the classic lysis inhibition phenomenon with conserved defensive function of a phage satellite in a disease context, highlighting the importance of lysis timing during infection and parasitization.
Unexpected Consequences: The Effects on Non-Accelerated Filers of an Accelerated Filing Deadline and SOX Section 404

Regulations implemented by the SEC in 2003 and 2004 simultaneously shortened the financial statement filing deadlines and increased the time required for both the preparation of financial statement...
Charge Density of Cation Determines Inner versus Outer Shell Coordination to Phosphate in RNA.

Divalent cations are often required to fold RNA, which is a highly charged polyanion. Condensation of ions, such as Mg\textsuperscript{2+} or Ca\textsuperscript{2+}, in the vicinity of RNA renormal...
Cooperative deformation of hydrogen bonds in beta-strands and beta-sheet nanocrystals

Beta-sheet protein domains are stabilized by weak hydrogen bonds, yet materials such as silk--whose ultimate tensile strength is controlled primarily by this secondary structure--can exceed the ultimate tensile strength of steel. Earlier work has suggested that this is because hydrogen bonds deform cooperatively within small protein domains to reach the maximum strength. Here we study the atomistic mechanism of this concerted deformation mechanism by applying an elastic structural model, used to solve the deformation field of the chemical bonds in beta-sheet nanostructures under stretching and thereby identify the number of hydrogen bonds that deform cooperatively. Through this analysis, we predict the optimal beta-strand and beta-sheet nanocrystal size associated with reaching the maximum usage of hydrogen bonds under loading applied per unit material volume. Our results, albeit based on a simple model and analytical equations, quantitatively agree with results based on experimental and molecular-dynamics studies and provide physical insight into the underlying molecular mechanisms of weak bond cooperativity. A comparison with the size of hydrogen bond clusters in biology reveals excellent agreement with the cluster sizes predicted by our analysis, suggesting that perhaps the confinement of hydrogen bonds into nanoscale elements is a universal biological design paradigm that turns weakness to strength. The parameters used in this study could be modified and applied to other protein and polymer structures, which imply potential applications of our model in understanding the physics of deformation and failure in a broader range of biological and polymer materials, as well as in de novo biomaterial design.
Chapter 11 – Structuring the Deal: Payment and Legal Considerations

This chapter introduces the deal structuring process and the interrelatedness of the factors that comprise the process. The focus in this chapter is on the form of payment, form of acquisition, and alternative forms of legal structures in which ownership is conveyed.
Information Freshness for Timely Detection of Status Changes

In this paper, we aim to establish the connection between Age of Information (AoI) in network theory, information uncertainty in information theory, and detection delay in time series analysis. We consider a dynamic system whose state changes at discrete time points, and a state change won’t be detected until an update generated after the change point is delivered to the destination for the first time. We introduce an information theoretic metric to measure the information freshness at the destination, and name it as generalized Age of Information (GAoI). We show that under any state-independent online updating policy, if the underlying state of the system evolves according to a stationary Markov chain, the GAoI is proportional to the AoI. Besides, the accumulative GAoI and AoI are proportional to the expected accumulative detection delay of all changes points over a period of time. Thus, any (G)AoI-optimal state-independent updating policy equivalently minimizes the corresponding expected change point detection delay, which validates the fundamental role of (G)AoI in real-time status monitoring. Besides, we also investigate a Bayesian change point detection scenario where the underlying state evolution is not stationary. Although AoI is no longer related to detection delay explicitly, we show that the accumulative GAoI is still an affine function of the expected detection delay, which indicates the versatility of GAoI in capturing information freshness in dynamic systems.
Diaspora engagement institutions and venture investment activity in developing countries

In response to recent trends in migration and remittances, many home-country governments have created new agencies that we call diaspora engagement institutions (DEI) intended to address migrant issues. In developing countries, DEI policies often direct migrant money and attention to funding and founding new businesses back home. In this paper, we ask whether and when those DEIs are effective. Grounding our explanation in social exchange and social identity theories, we propose that DEIs are more effective when they promote a stronger sense of home-country belonging and reciprocal giving among migrants. Using evidence from panel data analysis of 35 countries observed from 2001 to 2010, we find partial support for our predictions.
A hard constraint algorithm to model particle interactions in DNA-laden flows

We present a new numerical method for particle interactions in polymer-fluid models of DNA-laden flows. The DNA is represented by a bead-rod polymer model and is fully-coupled to the fluid. The main objective in this work is to properly model polymer-polymer and polymer-surface interactions by enforcing the physical rod-rod and rod-surface non-crossing constraints. Our new method is based on a rigid constraint algorithm whereby rods elastically bounce off one another to prevent crossing, similar to our previous algorithm used to model polymer-surface interactions.
Quantum toroidal algebra associated with $\mathfrak{gl}_{m|n}$

We introduce and study the quantum toroidal algebra ${\mathcal {E}}_{m|n}(q_{1},q_{2},q_{3})$ associated with the superalgebra $\mathfrak {gl}_{m|n}$ with m≠n, where the parameters satisfy q1q2q3 = 1. We give an evaluation map. The evaluation map is a surjective homomorphism of algebras ${\mathcal {E}}_{m|n}(q_{1},q_{2},q_{3}) \to \widetilde {U}_q \widehat {\mathfrak {gl}}_{m|n}$ to the quantum affine algebra associated with the superalgebra $\mathfrak {gl}_{m|n}$ at level c completed with respect to the homogeneous grading, where q2 = q2 and $q_{3}^{m-n}=c^{2}$ . We also give a bosonic realization of level one ${\mathcal {E}}_{m|n}(q_{1},q_{2},q_{3})$ -modules.
Quantum Erasure Using Entangled Surface Acoustic Phonons

A new experiment implements a quantum eraser using phonons rather than light, erasing information about which path a phonon travels in an interferometer to recover the interference pattern.
TXtract: Taxonomy-aware knowledge extraction for thousands of product categories

Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for a single category of product, and thus do not apply to real-life e-Commerce scenarios, which often contain thousands of diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge extraction model that applies to thousands of product categories organized in a hierarchical taxonomy. Through category conditional self-attention and multi-task learning, our approach is both scalable, as it trains a single model for thousands of categories, and effective, as it extracts category-specific attribute values. Experiments on products from a taxonomy with 4,000 categories show that TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
Few-Shot Video Classification via Temporal Alignment

Difficulty in collecting and annotating large-scale video data raises a growing interest in learning models which can recognize novel classes with only a few training examples. In this paper, we propose the Ordered Temporal Alignment Module (OTAM), a novel few-shot learning framework that can learn to classify a previously unseen video. While most previous work neglects long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video data through ordered temporal alignment. This leads to strong data-efficiency for few-shot learning. In concrete, our proposed pipeline learns a deep distance measurement of the query video with respect to novel class proxies over its alignment path. We adopt an episode-based training scheme and directly optimize the few-shot learning objective. We evaluate OTAM on two challenging real-world datasets, Kinetics and Something-Something-V2, and show that our model leads to significant improvement of few-shot video classification over a wide range of competitive baselines and outperforms state-of-the-art benchmarks by a large margin.
Generating Waveform Families using Multi-Tone Sinusoidal Frequency Modulation

This paper presents a method for generating a family of waveforms with low in-band Auto/Cross-Correlation Function (ACF/CCF) properties using the Multi-Tone Sinusoidal Frequency Modulated (MTSFM) waveform model. The MTSFM waveform's modulation function is represented using a Fourier series expansion. The Fourier coefficients are utilized as a set of discrete parameters that can be modified to optimize the waveform family's properties. The waveforms' ACF/CCF properties are optimized utilizing a multi-objective optimization problem. Each objective function is weighted to place emphasis on either low ACF or CCF sidelobes. The resulting optimized MTSFM waveforms each possess a thumbtack-like Ambiguity Function in addition to the specifically designed ACF/CCF properties. Most importantly, the resulting MTSFM waveform families possess both ideally low Peak-to-Average Power Ratios (PAPR) and high Spectral Efficiency (SE) making them well suited for transmission on practical radar transmitters.
Optimal Parallel Algorithms in the Binary-Forking Model

In this paper we develop optimal algorithms in the binary-forking model for a variety of fundamental problems, including sorting, semisorting, list ranking, tree contraction, range minima, and ordered set union, intersection and difference. In the binary-forking model, tasks can only fork into two child tasks, but can do so recursively and asynchronously. The tasks share memory, supporting reads, writes and test-and-sets. Costs are measured in terms of work (total number of instructions), and span (longest dependence chain). The binary-forking model is meant to capture both algorithm performance and algorithm-design considerations on many existing multithreaded languages, which are also asynchronous and rely on binary forks either explicitly or under the covers. In contrast to the widely studied PRAM model, it does not assume arbitrary-way forks nor synchronous operations, both of which are hard to implement in modern hardware. While optimal PRAM algorithms are known for the problems studied herein, it turns out that arbitrary-way forking and strict synchronization are powerful, if unrealistic, capabilities. Natural simulations of these PRAM algorithms in the binary-forking model (i.e., implementations in existing parallel languages) incur an Ω(log n) overhead in span. This paper explores techniques for designing optimal algorithms when limited to binary forking and assuming asynchrony. All algorithms described in this paper are the first algorithms with optimal work and span in the binary-forking model. Most of the algorithms are simple. Many are randomized.
VERY HIGH-ENERGY gamma-ray ASTRONOMY

Abstract Current interest in gamma-astronomy at energies above 100 GeV comes from the identification of Cygnus X-3 and other X-ray binaries as sources. In addition there are reports of emission from radio pulsars and a variety of other objects. The statistical significance of many of the observations is not high and many reported effects await confirmation, but there are a sufficient number of independent reports that very high energy gamma-ray astronomy must now be considered to have an observational basis. The observations are summarized with particular emphasis on those reported since 1980. The techniques used - the detection of small air showers using the secondary photons and particles at ground level - are unusual and are described. Future prospects for the field are discussed in relation to new ground-based experiments, satellite gamma-ray studies and proposed neutrino astronomy experiments.
A new approach to the symplectic isotopy problem

The symplectic isotopy conjecture states that every smooth symplectic surface in $CP^2$ is symplectically isotopic to a complex algebraic curve. Progress began with Gromov's pseudoholomorphic curves [Gro85], and progressed further culminating in Siebert and Tian's proof of the conjecture up to degree 17 [ST05], but further progress has stalled. In this article we provide a new direction of attack on this problem. Using a solution to a nodal symplectic isotopy problem we guide model symplectic isotopies of smooth surfaces. This results in an equivalence between the smooth symplectic isotopy problem and an existence problem of certain embedded Lagrangian disks. This redirects study of this problem from the realm of pseudoholomorphic curves of high genus to the realm of Lagrangians and Floer theory. Because the main theorem is an equivalence going both directions, it could theoretically be used to either prove or disprove the symplectic isotopy conjecture.
Convexification for an inverse problem for a 1D wave equation with experimental data

The forward problem here is the Cauchy problem for a 1D hyperbolic PDE with a variable coefficient in the principal part of the operator. That coefficient is the spatially distributed dielectric constant. The inverse problem consists of the recovery of that dielectric constant from backscattering boundary measurements. The data depend on one variable, which is time. To address this problem, a new version of the convexification method is analytically developed. The theory guarantees the global convergence of this method. Numerical testing is conducted for both computationally simulated and experimental data. Experimental data, which are collected in the field, mimic the problem of the recovery of the spatially distributed dielectric constants of antipersonnel land mines and improvised explosive devices.
Simple holographic models of black hole evaporation

Several recent papers have shown a close relationship between entanglement wedge reconstruction and the unitarity of black hole evaporation in AdS/CFT. The analysis of these papers however has a rather puzzling feature: all calculations are done using bulk dynamics which are essentially those Hawking used to predict information loss, but applying ideas from entanglement wedge reconstruction seems to suggest a Page curve which is consistent with information conservation. Why should two different calculations in the same model give different answers for the Page curve? In this note we present a new pair of models which clarify this situation. Our first model gives a holographic illustration of unitary black hole evaporation, in which the analogue of the Hawking radiation purifies itself as expected, and this purification is reproduced by the entanglement wedge analysis. Moreover a smooth black hole interior persists until the last stages the evaporation process. Our second model gives an alternative holographic interpretation of the situation where the bulk evolution leads to information loss: unlike in the models proposed so far, this bulk information loss is correctly reproduced by the entanglement wedge analysis. This serves as an illustration that quantum extremal surfaces are in some sense kinematic: the time-dependence of the entropy they compute depends on the choice of bulk dynamics. In both models no bulk quantum corrections need to be considered: classical extremal surfaces are enough to do the job. We argue that our first model is the one which gives the right analogy for what actually happens to evaporating black holes, but we also emphasize that any complete resolution of the information problem will require an understanding of non-perturbative bulk dynamics.
Mathematical Modeling of Business Reopening When Facing SARS-CoV-2 Pandemic: Protection, Cost, and Risk

The sudden onset of the coronavirus (SARS-CoV-2) pandemic has resulted in tremendous loss of human life and economy in more than 210 countries and territories around the world. While self-protections such as wearing masks, sheltering in place and quarantine policies and strategies are necessary for containing virus transmission, tens of millions of people in the U.S. have lost their jobs due to the shutdown of businesses. Therefore, how to reopen the economy safely while the virus is still circulating in population has become a problem of significant concern and importance to elected leaders and business executives. In this study, mathematical modeling is employed to quantify the profit generation and the infection risk simultaneously from a business entity’s perspective. Specifically, an ordinary differential equation model was developed to characterize disease transmission and infection risk. An algebraic equation is proposed to determine the net profit that a business entity can generate after reopening and take into account the costs associated of several protection/quarantine guidelines. All model parameters were calibrated based on various data and information sources. Sensitivity analyses and case studies were performed to illustrate the use of the model in practice. The results show that with a combination of necessary infection protection measures implemented, a business entity may stand a good opportunity to generate a positive net profit while successfully controlling the within-business infection prevalence under that in the general population. The use of personal protective equipment (PPE) is also found of significant importance, especially at the early stage of business reopening.
Effects of Formative Assessment Strategies on the Fractions Computation Skills of Students With Disabilities

Learning to compute with fractions is a major challenge for many students and especially for students with disabilities (SWD). Phase 1 of this study employed a randomized pretest–posttest compariso...
Perfluorooctanoic acid activates multiple nuclear receptor pathways and skews expression of genes regulating cholesterol homeostasis in liver of humanized PPARα mice fed an American diet

Abstract Humans are exposed to per- and polyfluoroalkyl substances (PFAS) in their drinking water, food, air, dust, and by direct use of consumer products. Increased concentrations of serum total cholesterol and low density lipoprotein cholesterol are among the endpoints best supported by epidemiology. The objectives of this study were to generate a new model for examining PFAS-induced dyslipidemia and to conduct molecular studies to better define mechanism(s) of action. We tested the hypothesis that perfluorooctanoic acid (PFOA) exposure at a human-relevant level dysregulates expression of genes controlling cholesterol homeostasis in livers of mice expressing human PPARα (hPPARα). Female and male hPPARα and PPARα null mice were fed a diet based on the “What we eat in America” analysis and exposed to PFOA in drinking water (8 μM) for 6 weeks. This resulted in a serum PFOA concentration of 48 μg/ml. PFOA increased liver mass, which was associated with histologically-evident lipid accumulation. Pooled analyses of serum lipoprotein cholesterol suggest that PFOA increased serum cholesterol, particularly in male mice. PFOA induced PPARα and constitutive androstane receptor target gene expression in liver. Expression of genes in four pathways regulating cholesterol homeostasis were also measured. PFOA decreased expression of Hmgcr in a PPARα-dependent manner. PFOA decreased expression of Ldlr and Cyp7a1 in a PPARα-independent manner. Apob expression was not changed. Sex differences were evident. This novel study design (hPPARα mice, American diet, long term exposure) generated new insight on the effects of PFOA on cholesterol regulation in the liver and the role of hPPARα.
Microfluidic chip for label-free removal of teratoma-forming cells from therapeutic human stem cells

Abstract Teratoma formation remains a safety concern in therapeutic cells derived from human-induced pluripotent stem cells (hiPSCs). Residual Teratoma forming cells are present in small numbers in differentiated hiPSC cultures and yet are of significant roadblock to the manufacturing and clinical translation of stem cell therapies. Rare cells are often difficult to remove with standard flow cytometry or magnetic bead sorting techniques. Here, we first characterized time-dependent expression of a teratoma marker, stage-specific embryonic antigen (SSEA)-5, which binds the H type-1 glycan during neural differentiation of hiPSCs. The fraction of cells SSEA-5+ remained high at 97% on day 3, dropped to 70% on day 4, 40% by day 6, and down to 1% on day 12 of differentiation, indicating successful differentiation. We engineered a microfluidic geometrically enhanced differential immunocapture (GEDI) technology to remove SSEA-5+ rare cells from hiPSC-derived neural progenitor cells (hiPSC-NPCs). The GEDI chip removed more than 95% of teratoma-forming cells and presents a facile tool to potentially functionalize with multiple antibodies and robustly enhance hiPSC-derived cell population safety prior to therapeutic transplantation. The approach is potentially amenable to generate a wide variety of high-quality therapeutic cells and can be integrated within the pipeline of cell manufacturing to improve patient safety and reduce the cost of manufacturing through early removal of undesirable cell types.
Absorption in Time-Varying Markov Chains: Graph-Based Conditions

We investigate absorption, i.e., almost sure convergence to an absorbing state, in time-varying (non-homogeneous) discrete-time Markov chains with finite state space. We consider systems that can switch among a finite set of transition matrices, which we call the modes. Our analysis is focused on two properties: 1) almost sure convergence to an absorbing state under any switching, and 2) almost sure convergence to a desired set of absorbing states via a proper switching policy. We derive necessary and sufficient conditions based on the structures of the transition graphs of modes. More specifically, we show that a switching policy that ensures almost sure convergence to a desired set of absorbing states from any initial state exists if and only if those absorbing states are reachable from any state on the union of simplified transition graphs. We then show three sufficient conditions for absorption under arbitrary switching. While the first two conditions depend on the acyclicity (weak acyclicity) of the union (intersection) of simplified transition graphs, the third condition is based on the distances of each state to the absorbing states in all the modes. These graph theoretic conditions can verify the stability and stabilizability of absorbing states based only on the feasibility of transitions in each mode.
Zeroth Order Non-convex optimization with Dueling-Choice Bandits

We consider a novel setting of zeroth order non-convex optimization, where in addition to querying the function value at a given point, we can also duel two points and get the point with the larger function value. We refer to this setting as optimization with dueling-choice bandits since both direct queries and duels are available for optimization. We give the COMP-GP-UCB algorithm based on GP-UCB (Srinivas et al., 2009), where instead of directly querying the point with the maximum Upper Confidence Bound (UCB), we perform a constrained optimization and use comparisons to filter out suboptimal points. COMP-GP-UCB comes with theoretical guarantee of $O(\frac{\Phi}{\sqrt{T}})$ on simple regret where $T$ is the number of direct queries and $\Phi$ is an improved information gain corresponding to a comparison based constraint set that restricts the search space for the optimum. In contrast, in the direct query only setting, $\Phi$ depends on the entire domain. Finally, we present experimental results to show the efficacy of our algorithm.
An exponential regulator for rapidity divergences

Abstract Finding an efficient and compelling regularization of soft and collinear degrees of freedom at the same invariant mass scale, but separated in rapidity is a persistent problem in high-energy factorization. In the course of a calculation, one encounters divergences unregulated by dimensional regularization, often called rapidity divergences. Once regulated, a general framework exists for their renormalization, the rapidity renormalization group (RRG), leading to fully resummed calculations of transverse momentum (to the jet axis) sensitive quantities. We examine how this regularization can be implemented via a multi-differential factorization of the soft-collinear phase-space, leading to an (in principle) alternative non-perturbative regularization of rapidity divergences. As an example, we examine the fully-differential factorization of a color singlet's momentum spectrum in a hadron-hadron collision at threshold. We show how this factorization acts as a mother theory to both traditional threshold and transverse momentum resummation, recovering the classical results for both resummations. Examining the refactorization of the transverse momentum beam functions in the threshold region, we show that one can directly calculate the rapidity renormalized function, while shedding light on the structure of joint resummation. Finally, we show how using modern bootstrap techniques, the transverse momentum spectrum is determined by an expansion about the threshold factorization, leading to a viable higher loop scheme for calculating the relevant anomalous dimensions for the transverse momentum spectrum.
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits. As a proof of concept, we demonstrate the direct synthesis of a 1:1 transformer on a 45nm SOI process using our proposed neural network model. Using pre-existing transformer s-parameter files and their geometric design training samples, the model predicts target geometric designs.
Robot navigation in a known environment with unknown moving obstacles

We propose a new type of artificial potential field, that we call hybrid potential field, to navigate a robot in situations in which the environment is known except for unknown and possibly moving obstacles. We show how to compute hybrid potential fields in real time and use them to control the motions of a real robot. Our method is tested on both a real robot and a simulated one. We present a feature matching approach for position error correction that we have validated experimentally with our mobile robot. We show extensive simulation results with up to 50 randomly moving obstacles.
Holistic characterization of single-hepatocyte transcriptome responses to high-fat diet

During nutritional overload and obesity, hepatocyte function is grossly altered, and a subset of hepatocytes begins to accumulate fat droplets, leading to nonalcoholic fatty liver disease (NAFLD). Recent single-cell studies revealed how nonparenchymal cells, such as macrophages, hepatic stellate cells, and endothelial cells, heterogeneously respond to NAFLD. However, it remains to be characterized how hepatocytes, the major constituents of the liver, respond to nutritional overload in NAFLD. Here, using droplet-based, single-cell RNA sequencing (Drop-seq), we characterized how the transcriptomic landscape of individual hepatocytes is altered in response to high-fat diet (HFD) and NAFLD. We showed that the entire hepatocyte population undergoes substantial transcriptome changes upon HFD, although the patterns of alteration were highly heterogeneous, with zonation-dependent and -independent effects. Periportal (zone 1) hepatocytes downregulated many zone 1-specific marker genes, whereas a small number of genes mediating gluconeogenesis were upregulated. Pericentral (zone 3) hepatocytes also downregulated many zone 3-specific genes; however, they upregulated several genes that promote HFD-induced fat droplet formation, consistent with findings that zone 3 hepatocytes accumulate more lipid droplets. Zone 3 hepatocytes also upregulated ketogenic pathways as an adaptive mechanism to HFD. Interestingly, many of the top HFD-induced genes, which encode proteins regulating lipid metabolism, were strongly co-expressed with each other in a subset of hepatocytes, producing a variegated pattern of spatial co-localization that is independent of metabolic zonation. In conclusion, our data set provides a useful resource for understanding hepatocellular alteration during NAFLD at single cell level.
Speech Recognition and Multi-Speaker Diarization of Long Conversations.

Speech recognition (ASR) and speaker diarization (SD) models have traditionally been trained separately to produce rich conversation transcripts with speaker labels. Recent advances have shown that joint ASR and SD models can learn to leverage audio-lexical inter-dependencies to improve word diarization performance. We introduce a new benchmark of hour-long podcasts collected from the weekly This American Life radio program to better compare these approaches when applied to extended multi-speaker conversations. We find that training separate ASR and SD models perform better when utterance boundaries are known but otherwise joint models can perform better. To handle long conversations with unknown utterance boundaries, we introduce a striding attention decoding algorithm and data augmentation techniques which, combined with model pre-training, improves ASR and SD.
A small molecule that binds an RNA repeat expansion stimulates its decay via the exosome complex

Summary Many diseases are caused by toxic RNA repeats. Herein, we designed a lead small molecule that binds the structure of the r(CUG) repeat expansion [r(CUG)exp] that causes myotonic dystrophy type 1 (DM1) and Fuchs endothelial corneal dystrophy (FECD) and rescues disease biology in patient-derived cells and in vivo. Interestingly, the compound's downstream effects are different in the two diseases, owing to the location of the repeat expansion. In DM1, r(CUG)exp is harbored in the 3′ untranslated region, and the compound has no effect on the mRNA's abundance. In FECD, however, r(CUG)exp is located in an intron, and the small molecule facilitates excision of the intron, which is then degraded by the RNA exosome complex. Thus, structure-specific, RNA-targeting small molecules can act disease specifically to affect biology, either by disabling the gain-of-function mechanism (DM1) or by stimulating quality control pathways to rid a disease-affected cell of a toxic RNA (FECD).
Near-Optimal Coresets for Least-Squares Regression

We study the (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression. We give deterministic, low-order polynomial-time algorithms to construct such coresets with approximation guarantees, together with lower bounds indicating that there is not much room for improvement upon our results.
Line Patterns in Free Groups

We study line patterns in a free group by considering the topology of the decomposition space, a quotient of the boundary at infinity of the free group related to the line pattern. We show that the group of quasi-isometries preserving a line pattern in a free group acts by isometries on a related space if and only if there are no cut pairs in the decomposition space. We also give an algorithm to detect such cut pairs.