cssi/SciSciGPT-SciSciNet huggingface dataset:
Preserving ON-OFF Privacy for Past and Future Requests

We study the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N sources. Moreover, the user is assumed to be incentivized to turn privacy ON or OFF whether he/she needs it or not. When privacy is ON, the user wants to keep private which source he/she is interested in. The challenge here is that the user’s behavior is correlated over time. Therefore, the user cannot simply ignore privacy when privacy is OFF, because this may leak information about his/her behavior when privacy was ON due to correlation. We model the user’s requests by a Markov chain. The goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. The user is assumed to know future requests within a window of positive size $\omega$ and uses it to construct privacy-preserving queries. In this paper, we construct ON-OFF privacy schemes for $N=2$ sources and prove their optimality.
GENDER AND THE CONSULTING ACADEMIC ECONOMIST

We surveyed academic economists on their experiences and perceptions of legal consulting. Nearly two‐thirds have consulting experience and 40% have consulted within the last 5 years. Base hourly rates average $244, with a median of $200. Women are less likely than men to have served as consultants and charge lower rates than comparable men, but there are few differences by gender in willingness to consult. Women report substantially more negative bias and are less likely to consult in complex cases or to have a high profile role in litigation. (JEL J44, J71, K41)
Test validity and the ethics of assessment.

Questions of the adequacy of a test as a measure of the characteristic it is interpreted to assess are answerable on scientific grounds by appraising psychometric evidence, especially construct validity. Questions of the appropriateness of test use in proposed applications are answerable on ethical grounds by appraising potential social consequences of the testing. The first set of answers provides an evidential basis for test interpretation and the second set provides a consequential basis for test use. In addition, this paper stresses the importance of construct validity for test use since it provides a rational foundation for predictiveness and relevance, as well as the importance of taking into account the value implications of test interpretations per se. By thus considering both the evidential and consequential bases of both test interpretation and test use, the roles of evidence and social values in the overall validation process are illuminated, and test validity comes to be based on ethical as well as evidential grounds.
Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations

Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health.
On turbulent spots in plane Poiseuille flow

Turbulence characteristics inside a turbulent spot in plane Poiseuille flow are investigated by analysing a database obtained from a direct numerical simulation. The spot is found to consist of two distinct regions — a turbulent area and a wave area. The flow inside the turbulent area has a strong resemblance to that found in the fully developed turbulent channel. Suitably defined mean and r.m.s. fluctuations as well as the internal shear-layer structures are found to be similar to the turbulent counterpart. In the wave area the inflexional mean spanwise profiles cause a rapid growth of oblique waves, which break down to turbulence. The breakdown process of the oblique waves is reminiscent of the secondary instability observed during transition to turbulence in channel and boundary-layer flows. Other detailed characteristics associated with the Poiseuille spot are presented and are compared with experimental results.
The interplay between superconductivity and non-Fermi liquid at a quantum-critical point in a metal

Abstract Near a quantum-critical point, a metal reveals two competing tendencies: destruction of fermionic coherence and attraction in one or more pairing channels. We analyze the competition within Eliashberg theory for a class of quantum-critical models with an effective dynamical electron–electron interaction V ( Ω m ) ∝ 1 ∕ | Ω m | γ (the γ -model) for 0 γ 1 . We argue that the two tendencies are comparable in strength, yet the one towards pairing is stronger, and the ground state is a superconductor. We show, however, that there exist two distinct regimes of system behavior below the onset temperature of the pairing T p . In the range T cross T T p fermions remain incoherent and the spectral function A ( k , ω ) and the density of states N ( ω ) both display “gap filling” behavior in which, i.e., the position of the maximum in N ( ω ) is set by temperature rather than the pairing gap. At lower T T cross , fermions acquire coherence, and A ( k , ω ) and N ( ω ) display conventional ”gap closing” behavior, when the peak position in N ( ω ) scales with the gap and shifts to a smaller value as T increases. We argue that the existence of the two regimes comes about because of special behavior of fermions with frequencies ω = ± π T along the Matsubara axis. Specifically, for these fermions, the component of the self-energy, which competes with the pairing, vanishes in the normal state. We further argue that the crossover at T ∼ T cross comes about because Eliashberg equations allow an infinite number of topologically distinct solutions for the onset temperature of the pairing within the same gap symmetry. Only one solution, with the highest T p , actually emerges, but other solutions are generated and modify the form of the gap function at T ≤ T cross . Finally, we argue that the actual T c is comparable to T cross , while at T cross T T p phase fluctuations destroy superconducting long-range order, and the system displays a pseudogap behavior.
Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems

978-1-7281-4300-2/19/$31.00 ©2019 IEEEThis paper proposes a novel neural network architecture, that we call an auto-precoder. This auto-precoder network jointly senses the millimeter wave (mmWave) channel and designs the hybrid precoding matrices with only a few training pilots. More specifically, the proposed machine learning model leverages the prior observations of the channel to achieve two objectives. First, it optimizes the compressive channel sensing vectors based on the surrounding environment in an unsupervised manner to focus the sensing power on the most promising spatial directions. This is enabled by a novel neural network architecture that accounts for the constraints on the RF chains and models the transmitter/receiver measurement matrices as two complex-valued convolutional layers. Second, the proposed model learns how to construct the RF beamforming vectors of the hybrid architectures directly from the projected channel vector (the received signal). Simulation results show that the proposed approach can significantly reduce the training overhead compared to classical (non-machine learning) solutions. For example, for a system of 64 transmit and 64 receive antennas, with 3 RF chains at both sides, the proposed solution needs only 8 or 16 channel training pilots to directly predict the RF beamforming/combining vectors of the hybrid architectures and achieve near-optimal achievable rates.
Temperature and rate dependent constitutive behaviors of low melt Field ’s metal

Abstract Low melting point metals such as Field’s metal and gallium are increasingly used as transition phases in stiffness-tuning soft materials and devices. Nevertheless, there is a knowledge gap on the fundamental constitutive behaviors of metals with melting points below 100℃. This letter reports the stress–strain relationships of Field’s metal at various temperatures and strain rates. This metal has the lowest melting point among metals and alloys whose constitutive behaviors have been studied systematically. Experimental results indicate that Field’s metal exhibits a strain-softening behavior, in oppose to the strain-hardening behaviors of most engineering metals and alloys. A modified Johnson–Cook model is devised in this letter to describe the constitutive behaviors of Field’s metal. The proposed model will facilitate the design and analysis of functional materials and structures consisting of Field’s metal.
CANOPY: A Verifiable Privacy-Preserving Token Ring-Based Communication Protocol for Smart Homes

This article focuses on the new privacy challenges that arise in smart homes. Specifically, the article focuses on inferring the user’s activities—which may, in turn, lead to the user’s privacy—via inferences through device activities and network traffic analysis. We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices to prevent inferences from device activities, via device workflow, i.e., inferences from a coordinated sequence of devices’ actuation. The solution hides the device activity and corresponding channel activities, thus preserving the individual’s activities. We also extend our solution to deal with a large number of devices and devices that produce large-sized data by implementing parallel rings. Our experiments also evaluate the performance in terms of communication overheads of the proposed approach and the obtained privacy.
Parameter uncertainty drives important incongruities between simulated chlorophyll-a and phytoplankton functional group dynamics in a mechanistic management model

Abstract Mechanistic phytoplankton functional group (PFG) models are used to develop water quality targets designed to mitigate cyanobacteria blooms, but it remains unclear whether PFG models adequately simulate cyanobacteria dynamics as most are evaluated against observations of chlorophyll-a instead of PFG biomass. To address this challenge, we analyzed an application of CE-QUAL-ICM, a 3D mechanistic PFG model used by water managers and modelers. Global Sensitivity Analysis was employed to assess the sensitivity of modeled chlorophyll-a, cyanobacteria biomass, and eukaryotic phytoplankton biomass to 42 uncertain input factors in CE-QUAL-ICM's PFG growth and loss functions. Results revealed that parameterization of CE-QUAL-ICM captured bloom variation but underpredicted bloom peaks, and simulated chlorophyll-a with greater skill than PFG biomass. Additionally, when run across realistic ranges of PFG parameter values, model outputs were highly sensitive to chlorophyll-to-carbon ratios and phosphorus uptake parameters, indicating that these factors should be the focus of targeted parameterization efforts.
Asymptotically tight bounds for computing with faulty arrays of processors

The computational power of 2-D and 3-D processor arrays that contain a potentially large number of faults is analyzed. Both a random and a worst-case fault model are considered, and it is proved that in either scenario low-dimensional arrays are surprisingly fault tolerant. It is also shown how to route, sort, and perform systolic algorithms for problems such as matrix multiplication in optimal time on faulty arrays. In many cases, the running time is the same as if there were no faults in the array (up to constant factors). On the negative side, it is shown that any constant congestion embedding of an n*n fault-free array on an n*n array with Theta (n/sup 2/) random faults (or Theta (log n) worst-case faults) requires dilation Theta (log n). For 3-D arrays, knot theory is used to prove that the required dilation is Omega ( square root log n). >
On Popa’s factorial commutant embedding problem

An open question of Sorin Popa asks whether or not every $R^{\mathcal{U}}$-embeddable factor admits an embedding into $R^{\mathcal{U}}$ with factorial relative commutant. We show that there is a locally universal McDuff II$_1$ factor $M$ such that every property (T) factor admits an embedding into $M^{\mathcal{U}}$ with factorial relative commutant. We also discuss how our strategy could be used to settle Popa's question for property (T) factors if a certain open question in the model theory of operator algebras has a positive solution.
Saturated Phosphatidylcholine in Amniotic Fluid and Prediction of the Respiratory Distress Syndrome

Abstract The lecithin/sphingomyelin (L/S) ratio in amniotic fluid is widely used to predict the risk of respiratory-distress syndrome. However, the results are unreliable if the specimen is contaminated or obtained during a complicated pregnancy. We therefore compared the predictive value of the L/S ratio with that of the concentration of saturated phosphatidylcholine (SPC) in 322 amniotic-fluid samples, 75 per cent of which were contaminated or obtained during complicated pregnancies or both. A positive result is one that predicted the development of respiratory-distress syndrome, taken as an L/S ratio equal to or less than 2/1 or an SPC below 500 μg per deciliter. The respiratory-distress syndrome was correctly predicted in 25 of 45 cases (55.5 per cent) with L/S ratios equal to or less than 2/1, and in 35 of 42 cases (82 per cent) with SPC's less than 500 μg per deciliter. When L/S ratios were greater than 2/1, there were 13 of 277 (4.7 per cent) false negatives, and when SPC's were above 500 μg per de...
Bayesian-Deep-Learning Estimation of Earthquake Location From Single-Station Observations

We present a deep-learning method for a single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multitask temporal convolutional neural network to learn epicentral distance and P travel time from 1-min seismograms. The network estimates epicentral distance and P travel time with mean errors of 0.23 km and 0.03 s and standard deviations of 5.42 km and 0.66 s, respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle and its epistemic uncertainty. This network estimates the direction from which seismic waves arrive at the station with a mean error of 1°. Using this information, we estimate the epicenter, origin time, and depth along with their confidence intervals. We use a global data set of earthquake signals recorded within 1° (~112 km) from the event to build the model and demonstrate its performance. Our model can predict epicenter, origin time, and depth with mean errors of 7.3 km, 0.4 s, and 6.7 km, respectively, at different locations around the world. Our approach can be used for fast earthquake source characterization with a limited number of observations and also for estimating the location of earthquakes that are sparsely recorded—either because they are small or because stations are widely separated.
Lower Bounds for the Average and Smoothed Number of Pareto Optima

Smoothed analysis of multiobjective 0-1 linear optimization has drawn considerable attention recently. In this literature, the number of Pareto-optimal solutions (i.e., solutions with the property that no other solution is at least as good in all the coordinates and better in at least one) for multiobjective optimization problems is the central object of study. In this paper, we prove several lower bounds for the expected number of Pareto optima. Our basic result is a lower bound of Omega_d(n^{d-1}) for optimization problems with d objectives and $n$ variables under fairly general conditions on the distributions of the linear objectives. Our proof relates the problem of lower bounding the number of Pareto optima to results in discrete geometry and geometric probability connected to arrangements of hyperplanes. We use our basic result to derive (1) To our knowledge, the first lower bound for natural multiobjective optimization problems. We illustrate this for the maximum spanning tree problem with randomly chosen edge weights. Our technique is sufficiently flexible to yield such lower bounds for other standard objective functions studied in this setting (such as multiobjective shortest path, TSP tour, matching). (2) Smoothed lower bound of min(Omega_d( n^{d-1.5} phi^{(d-\log d) (1-Theta(1/phi))}), 2^{Theta(n)}) for the 0-1 knapsack problem with d profits for phi-semirandom distributions for a version of the knapsack problem. This improves the recent lower bound of Brunsch and Roglin.
CHAPTER 9 – Monotonicity and Oriented Tangent Cones

Publisher Summary This chapter presents an introduction to the two basic tools of the regularity theory of area-minimizing surfaces, that is, monotonicity and oriented tangent cones. It presents the monotonicity of the mass ratio, a lower bound on area growth. The theorem on the monotonicity of the mass ratio is one of the most useful tools in regularity theory. Monotonicity actually holds for stationary (minimal) surfaces or, in a weakened form, even for bounded mean curvature surfaces but not for more general integrands than area. The chapter also presents the existence of an oriented tangent cone at every interior point of an area-minimizing surface. It presents the development of a generalization to locally integral flat chains of the notion of the tangent plane to a C1 manifold at a point. A locally integral flat chain C is called a cone if every homothetic expansion or contraction μR#C = C. An oriented tangent cone C is a current, whereas a tangent cone Tan is a set.
Oil and Governance in Uganda

Many critical governance issues were created with the discovery of major oil deposits in Uganda. Because developing countries like Uganda lack strong institutional foundations, it is widely assumed that riches flowing from oil will result in huge sums of money being diverted to politicians while the country ends up worse off in the long run. Uganda certainly faces this “natural resource curse,” but the potential for corruption is only one of many governance issues arising from the potential of oil riches. The government needs to work effectively with foreign oil companies and neighboring countries to recover and transport the oil. It must also establish institutions and procedures to manage its new oil economy. Moreover, questions must be answered regarding ownership the oil producing lands and how the fragile environment of the country can be protected.
Understanding the Response of Tropical Ascent to Warming Using an Energy Balance Framework

Previous work has established that warming is associated with an increase in dry static stability, a weakening of the tropical circulation, and a decrease in the convective mass flux. Using a set o...
Continuous logic and embeddings of Lebesgue spaces

We use the compactness theorem of continuous logic to give a new proof that $$L^r([0,1]; {\mathbb {R}})$$ isometrically embeds into $$L^p([0,1]; {\mathbb {R}})$$ whenever $$1 \le p \le r \le 2$$ . We will also give a proof for the complex case. This will involve a new characterization of complex $$L^p$$ spaces based on Banach lattices.
Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism

Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how system-designers choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers.
Complexity of Unordered CNF Games

The classic TQBF problem is to determine who has a winning strategy in a game played on a given conjunctive normal form formula (CNF), where the two players alternate turns picking truth values for the variables in a given order, and the winner is determined by whether the CNF gets satisfied. We study variants of this game in which the variables may be played in any order, and each turn consists of picking a remaining variable and a truth value for it.For the version where the set of variables is partitioned into two halves and each player may only pick variables from his or her half, we prove that the problem is PSPACE-complete for 5-CNFs and in P for 2-CNFs. Previously, it was known to be PSPACE-complete for unbounded-width CNFs (Schaefer, STOC 1976). For the general unordered version (where each variable can be picked by either player), we also prove that the problem is PSPACE-complete for 5-CNFs and in P for 2-CNFs. Previously, it was known to be PSPACE-complete for 6-CNFs (Ahlroth and Orponen, MFCS 2012) and PSPACE-complete for positive 11-CNFs (Schaefer, STOC 1976).
Consumer search in the U.S. auto industry: The role of dealership visits

In many markets, consumers visit stores and physically inspect products before making purchase decisions. We view the inspection of a product at a retail location as a search for product fit. We quantify the cost and benefit from searching for product fit using a discrete choice model of demand with optimal sequential search. In these models, the benefit of searching is measured by the standard deviation of the product fit and has, heretofore, been fixed to one in estimation. We show that, with an exogenous search cost shifter, both the cost and benefit of searching can be separately estimated. Our empirical setting is the U.S. automotive market. We assemble a unique data set containing individual-level smartphone geolocation data that inform us about dealership visits. We also obtain information on new vehicle purchases from proprietary DMV registration data. Our exogenous cost shifter is the distance a consumer must travel to visit a dealership. Our results show that the benefit provided by dealerships to consumers is substantial. Within our empirical context, failure to estimate the standard deviation of the product fit leads to biased search cost and consumer surplus estimates and to inaccurate predictions regarding consumers’ number of searches and effects of at-home test drive programs.
Asymmetry between galaxies with different spin patterns: A comparison between COSMOS, SDSS, and Pan-STARRS

Previous observations of a large number of galaxies show differences between the photometry of spiral galaxies with clockwise spin patterns and spiral galaxies with counterclockwise spin patterns. In this study the mean magnitude of a large number of clockwise galaxies is compared to the mean magnitude of a large number of counterclockwise galaxies. The observed difference between clockwise and counterclockwise spiral galaxies imaged by the space-based COSMOS survey is compared to the differences between clockwise and counterclockwise galaxies imaged by the Earth-based SDSS and Pan-STARRS around the same field. The annotation of clockwise and counterclockwise galaxies is a fully automatic process that does not involve human intervention, and in all experiments both clockwise and counterclockwise galaxies are separated from the same fields. The comparison shows that the same asymmetry was identified by all three telescopes, providing strong evidence that the rotation direction of a spiral galaxy is linked to its luminosity as measured from Earth. Analysis of the luminosity difference using a large number of galaxies from different parts of the sky shows that the difference between clockwise and counterclockwise galaxies changes with the direction of observation, and oriented around an axis.
Ensuring the reproductive rights of women with intellectual disability

Background: Women with intellectual disability experience disparities in sexual and reproductive health care services. Methods: To explore perceptions of caring for persons with disability, includi...
A Mixture of h - 1 Heads is Better than h Heads

Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss. In this work, we instead “reallocate” them—the model learns to activate different heads on different inputs. Drawing connections between multi-head attention and mixture of experts, we propose the mixture of attentive experts model (MAE). MAE is trained using a block coordinate descent algorithm that alternates between updating (1) the responsibilities of the experts and (2) their parameters. Experiments on machine translation and language modeling show that MAE outperforms strong baselines on both tasks. Particularly, on the WMT14 English to German translation dataset, MAE improves over “transformer-base” by 0.8 BLEU, with a comparable number of parameters. Our analysis shows that our model learns to specialize different experts to different inputs.
Neural Contextual Bandits with UCB-based Exploration

We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other than boundedness. We propose a new algorithm, NeuralUCB, which leverages the representation power of deep neural networks and uses a neural network-based random feature mapping to construct an upper confidence bound (UCB) of reward for efficient exploration. We prove that, under standard assumptions, NeuralUCB achieves $\tilde O(\sqrt{T})$ regret, where $T$ is the number of rounds. To the best of our knowledge, it is the first neural network-based contextual bandit algorithm with a near-optimal regret guarantee. We also show the algorithm is empirically competitive against representative baselines in a number of benchmarks.
The Role of Accounting in the Financial Crisis: Lessons for the Future

SYNOPSIS: The advent of the Great Recession in 2008 was the culmination of a perfect storm of lax regulation, a growing housing bubble, rising popularity of derivatives instruments, and questionable banking practices. In addition to these causes, management incentives as well as certain U.S. accounting standards contributed to the financial crisis. We outline the significant effects of these incentive structures and the role of fair value accounting standards during the crisis, and discuss implications and relevance of these rules to practitioners, standard-setters, and academics.
Metapopulation Network Models for Understanding, Predicting, and Managing the Coronavirus Disease COVID-19

Mathematical models of SARS-CoV-2 (the virus which causes COVID-19) spread are used for guiding the design of mitigation steps and helping identify impending breaches of health care system surge capacity. The challenges of having only lacunary information about daily new infections and mortality counts are compounded by geographic heterogeneity of the population. This complicates prediction, particularly when homogenized population models assume well mixed. To address this problem, we account for the differences between rural and urban settings using network-based, distributed models where the spread of the pandemic is described in distinct local cohorts with nested SE(A)IR models, i.e., modified SEIR models that include infectious asymptomatic individuals. The model parameters account for the SARS-CoV-2 transmission mostly via human-to-human contact, and the fact that contact frequency among individuals differs between urban and rural areas, and may change over time. The probability that the virus spreads into an uninfected community is associated with influx of individuals from communities where the infection is already present, thus each node is characterized by its internal contact and by its connectivity with other nodes. Census and cell phone data used to set up the adjacency matrix of the network, which can be modified to simulate changes in mitigation measures. Our network SE(A)IR model depends on easily interpretable parameters estimated from available community level data. The parameters estimated with Bayesian techniques include transmission rate and the ratio asymptomatic to symptomatic infectious individuals. The methodology predicts that the latter quantity approaches 0.5 as the epidemic reaches an equilibrium, in full agreement with the May 22, 2020 CDC modeling. The network model gives rise to a spatially distributed computational model that explains the geographic dynamics of the contagion, e.g., in larger cities surrounded by suburban and rural areas. The time courses of the infected cohorts in the different counties predicted by the network model are remarkably similar to the reported observations. Moreover, the model shows that monitoring the infection prevalence in each county, and adopting local mitigation measures as infections climb beyond a certain threshold, is almost as effective as blanket measures, and more effective than reducing inter-county mobility.
Cortical ChAT+ neurons co-transmit acetylcholine and GABA in a target-and brain-region-specific manner

The mouse cerebral cortex contains neurons that express choline acetyltransferase (ChAT) and are a potential local source of acetylcholine. However, the neurotransmitters released by cortical ChAT+ neurons and their synaptic connectivity are unknown. We show that the nearly all cortical ChAT+ neurons in mice are specialized VIP+ interneurons that release GABA strongly onto other inhibitory interneurons and acetylcholine sparsely onto layer 1 interneurons and other VIP+/ChAT+ interneurons. This differential transmission of ACh and GABA based on the postsynaptic target neuron is reflected in VIP+/ChAT+ interneuron pre-synaptic terminals, as quantitative molecular analysis shows that only a subset of these are specialized to release acetylcholine. In addition, we identify a separate, sparse population of non-VIP ChAT+ neurons in the medial prefrontal cortex with a distinct developmental origin that robustly release acetylcholine in layer 1. These results demonstrate both cortex-region heterogeneity in cortical ChAT+ interneurons and target-specific co-release of acetylcholine and GABA.
Service Industrialization, Employment and Wages in the US Information Economy

The US economy has undergone significant shifts towards services and towards information intensive industries. The latter trend has been driven by advances in information technology. These advances have concurrently led to substantial changes in the production and delivery of services, especially notable in information-intensive sectors. We examine these changes from the perspective of service industrialization, since they are similar in many ways to the historical industrialization of goods production. We focus on the effect of industrialization on employment and wages, and identify certain important consequences of this direction. One major consequence is the impact on the customer facing services and the front office in addition to the effect on service processes in the back room. An important aggregate result is a decline in white collar jobs in both those categories. A larger effect is at the sector level, with significant disruptions in some sectors leading to their substantial restructuring. Such disruptions are likely to occur in other information intensive sectors as well.
Observations of Protoplanetary Disk Structures

The disks that orbit young stars are the essential conduits and reservoirs of material for star and planet formation. Their structures, meaning the spatial variations of the disk physical condition...
Image polaritons in boron nitride for extreme polariton confinement with low losses

Polaritons in two-dimensional materials provide extreme light confinement that is difficult to achieve with metal plasmonics. However, such tight confinement inevitably increases optical losses through various damping channels. Here we demonstrate that hyperbolic phonon polaritons in hexagonal boron nitride can overcome this fundamental trade-off. Among two observed polariton modes, featuring a symmetric and antisymmetric charge distribution, the latter exhibits lower optical losses and tighter polariton confinement. Far-field excitation and detection of this high-momenta mode become possible with our resonator design that can boost the coupling efficiency via virtual polariton modes with image charges that we dub ‘image polaritons’. Using these image polaritons, we experimentally observe a record-high effective index of up to 132 and quality factors as high as 501. Further, our phenomenological theory suggests an important role of hyperbolic surface scattering in the damping process of hyperbolic phonon polaritons. The tight confinement of polaritons in 2D materials leads to increased optical losses. Here, the authors demonstrate image phonon polariton modes in hexagonal boron nitride with an antisymmetric charge distribution that feature quality factors of up to 501 and an effective index of 132.
An influenza A hemagglutinin small-molecule fusion inhibitor identified by a new high-throughput fluorescence polarization screen

Influenza hemagglutinin (HA) glycoprotein is the primary surface antigen targeted by the host immune response and a focus for development of novel vaccines, broadly neutralizing antibodies (bnAbs), and therapeutics. HA enables viral entry into host cells via receptor binding and membrane fusion and is a validated target for drug discovery. However, to date, only a very few bona fide small molecules have been reported against the HA. To identity new antiviral lead candidates against the highly conserved fusion machinery in the HA stem, we synthesized a fluorescence-polarization probe based on a recently described neutralizing cyclic peptide P7 derived from the complementarity-determining region loops of human bnAbs FI6v3 and CR9114 against the HA stem. We then designed a robust binding assay compatible with high-throughput screening to identify molecules with low micromolar to nanomolar affinity to influenza A group 1 HAs. Our simple, low-cost, and efficient in vitro assay was used to screen H1/Puerto Rico/8/1934 (H1/PR8) HA trimer against ∼72,000 compounds. The crystal structure of H1/PR8 HA in complex with our best hit compound F0045(S) confirmed that it binds to pockets in the HA stem similar to bnAbs FI6v3 and CR9114, cyclic peptide P7, and small-molecule inhibitor JNJ4796. F0045 is enantioselective against a panel of group 1 HAs and F0045(S) exhibits in vitro neutralization activity against multiple H1N1 and H5N1 strains. Our assay, compound characterization, and small-molecule candidate should further stimulate the discovery and development of new compounds with unique chemical scaffolds and enhanced influenza antiviral capabilities.
Heterogeneous Computation Assignments in Coded Elastic Computing

We study the optimal design of a heterogeneous coded elastic computing (CEC) network where machines have varying relative computation speeds. CEC introduced by Yang et al. is a framework which mitigates the impact of elastic events, where machines join and leave the network. A set of data is distributed among storage constrained machines using a Maximum Distance Separable (MDS) code such that any subset of machines of a specific size can perform the desired computations. This design eliminates the need to re-distribute the data after each elastic event. In this work, we develop a process for an arbitrary heterogeneous computing network to minimize the overall computation time by defining an optimal computation load, or number of computations assigned to each machine. We then present an algorithm to define a specific computation assignment among the machines that makes use of the MDS code and meets the optimal computation load.
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8–30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50–50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients. Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive. A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data.

Spatial and time-dependent data is of interest in many applications. This task is difficult due to its complex spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity. To address these challenges, we propose Forecaster, a graph Transformer architecture. Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations. Based on the topology of the graph, we sparsify the Transformer to account for the strength of spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity. We evaluate Forecaster in the problem of forecasting taxi ride-hailing demand and show that our proposed architecture significantly outperforms the state-of-the-art baselines.
Optimizing information freshness using low-power status updates via sleep-wake scheduling

In this paper, we consider the problem of optimizing the freshness of status updates that are sent from a large number of low-power source nodes to a common access point. The source nodes utilize carrier sensing to reduce collisions and adopt an asychronized sleep-wake strategy to achieve an extended battery lifetime (e.g., 10-15 years). We use age of information (AoI) to measure the freshness of status updates, and design the sleep-wake parameters for minimizing the weighted-sum peak AoI of the sources, subject to per-source battery lifetime constraints. When the sensing time is zero, this sleep-wake design problem can be solved by resorting to a two-layer nested convex optimization procedure; however, for positive sensing times, the problem is non-convex. We devise a low-complexity solution to solve this problem and prove that, for practical sensing times that are short and positive, the solution is within a small gap from the optimum AoI performance. Our numerical and NS-3 simulation results show that our solution can indeed elongate the batteries lifetime of information sources, while providing a competitive AoI performance.
Continuously triangulating the continuous cluster category

Abstract In [8] , the continuous cluster category was introduced. This is a topological category whose space of isomorphism classes of indecomposable objects forms a Moebius band. It was found in [8] that, in order to have a continuously triangulated structure on this category, one needs at least two copies of each indecomposable object forming a 2-fold covering space of the Moebius band. This paper classifies all continuous triangulations of finite coverings of the basic continuous cluster category. This includes the connected 2-fold covering of Igusa-Todorov [8] , the disconnected 2-fold covering of Orlov [13] and a third unexpected continuously add-triangulated 2-fold covering of the Moebius strip category.
Achieving Correlated Equilibrium by Studying Opponent’s Behavior Through Policy-Based Deep Reinforcement Learning

Game theory is a very profound study on distributed decision-making behavior and has been extensively developed by many scholars. However, many existing works rely on certain strict assumptions such as knowing the opponent’s private behaviors, which might not be practical. In this work, we focused on two Nobel winning concepts, the Nash equilibrium, and the correlated equilibrium. We proposed a policy-based deep reinforcement learning model which instead of just learning the regions for corresponding strategies and actions, it learns why and how the rational opponent plays. With our proposed policy-based deep reinforcement learning model, we successfully reached the correlated equilibrium which maximizes the utility for each player. Depending on the scenario, the equilibrium can reach outside of the Nash equilibrium convex hull to achieve higher utility for the players, while the traditional non-regret algorithms cannot. In addition, we also proposed a mathematical model to inverse the calculation of the correlated equilibrium probability to estimate the rational opponent player’s payoff. Through simulations, with limited interaction among the players, we showed that our proposed method can achieve the optimal correlated equilibrium where each player gains an equal or higher utility than the Nash equilibrium.
The Effect of Operational Process Changes on Preoperative Patient Flow: Evidence from Field Research

We partnered with a leading U.S. academic medical center to empirically examine the impact of operational process changes designed to improve preoperative flow of patients through the perioperative environment. We focus on the implementation of centralized decision making and the introduction of an information technology (IT) enabled intraoperative prompt, on efficiency, as measured by preoperative patient processing time. We analyze over 33,000 individual surgical cases in a unique field experimental setting to conduct an empirical investigation of the effects of each intervention on patients' time spent in preoperative processing. To identify the causal effect of each process change, we leverage our field experimental research design and cast our analyses in the difference-in-differences modeling framework. We compare the preoperative patient processing time of two distinct patient groups, a treatment group that is impacted by the implemented operational changes and a control group that was not impacted by the changes, before and after each process change. Our results suggest a 3.4% reduction in preoperative processing time with only centralized decision making in place, yet a 10.8% reduction in preoperative processing time when centralized decision making is paired with the IT enabled intraoperative prompt. We also find evidence of a complementarity effect between our process changes and surgeon prior process experience. Our study contributes to the healthcare operations literature by demonstrating the benefits of coordinated information flow within the patient supply chain. We offer insights for hospital managers and healthcare operations scholars alike on the role of information coordination in improving preoperative patient flow with minimal impact on existing resources and staff.
Distributed Dense Subgraph Detection and Low Outdegree Orientation.

The densest subgraph problem, introduced in the 80s by Picard and Queyranne as well as Goldberg, is a classic problem in combinatorial optimization with a wide range of applications. The lowest outdegree orientation problem is known to be its dual problem. We study both the problem of finding dense subgraphs and the problem of computing a low outdegree orientation in the distributed settings. Suppose $G=(V,E)$ is the underlying network as well as the input graph. Let $D$ denote the density of the maximum density subgraph of $G$. Our main results are as follows. Given a value $\tilde{D} \leq D$ and $0 < \epsilon < 1$, we show that a subgraph with density at least $(1-\epsilon)\tilde{D}$ can be identified deterministically in $O((\log n) / \epsilon)$ rounds in the LOCAL model. We also present a lower bound showing that our result for the LOCAL model is tight up to an $O(\log n)$ factor. In the CONGEST model, we show that such a subgraph can be identified in $O((\log^3 n) / \epsilon^3)$ rounds with high probability. Our techniques also lead to an $O(diameter + (\log^4 n)/\epsilon^4)$-round algorithm that yields a $1-\epsilon$ approximation to the densest subgraph. This improves upon the previous $O(diameter /\epsilon \cdot \log n)$-round algorithm by Das Sarma et al. [DISC 2012] that only yields a $1/2-\epsilon$ approximation. Given an integer $\tilde{D} \geq D$ and $\Omega(1/\tilde{D}) < \epsilon < 1/4$, we give a deterministic, $\tilde{O}((\log^2 n) /\epsilon^2)$-round algorithm in the CONGEST model that computes an orientation where the outdegree of every vertex is upper bounded by $(1+\epsilon)\tilde{D}$. Previously, the best deterministic algorithm and randomized algorithm by Harris [FOCS 2019] run in $\tilde{O}((\log^6 n)/ \epsilon^4)$ rounds and $\tilde{O}((\log^3 n) /\epsilon^3)$ rounds respectively and only work in the LOCAL model.
Expanding the scope of statistical computing: Training statisticians to be software engineers

Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since the publication of Nolan and Temple Lang ...