cssi/SciSciGPT-SciSciNet huggingface dataset:
Distributed algorithms for global structuring

In the search for speed and computing power, many researchers in computer science have turned to networks of computers as a possible solution. 1 , 5 , 8 , 14 , 9 These networks consist of minicomputers connected by links across which communication between processors occurs. In homogeneous networks, the computer at each node is identical to the others, with the possible exception of peripherals. Each processor has its own local memory, does not share memory with any other processor, and communicates with other processors via message passing. In order to fully utilize the speed and power inherent in a network, emphasis must be placed on the development of parallel (as opposed to sequential) algorithms.
Large-Scale Phased Array Calibration

One challenge in fielding large-scale phased array antennas is calibration of the antenna system, so that RF radiated beams can be accurately formed and steered. Here, we describe two new methods for calibrating large phased arrays, namely, the mobile Fourier gauge (MFG) and satellite-based calibration (SatCal) that permit rapid calibration of large phased arrays on site and that remove the need for expensive near-field facility testing that limits the size of the array sections that can be RF aligned. The MFG utilizes a known short RF line source that can be sequentially moved across the array using an existing crane; RF coupling measurements between this RF line source and the array elements made at different RF line source positions are vector summed, so as to form a known virtual RF line source that stretches the length of the array. A second independent method, SatCal, is presented that uses reflections from orbiting satellites to calibrate the array, either at the subarray or radiating element level. These calibration methods have been demonstrated on a large ${S}$ -band phased array and have resulted in array calibrations equivalent to within 5°, 0.7 dB rms (element level), or 4°, 0.6 dB rms (subarray level).
Exchange Rate Regimes in Central, Eastern, and Southeastern Europe: A Euro Bloc and a Dollar Bloc?

There are 13 countries in Central, Eastern, and Southeastern Europe (CESEE) with floating exchange rate regimes, de jure. This paper uses the framework pioneered by Frankel and Wei (1994) and extended in Frankel and Wei (2008) to show that most of them have been tracking either the euro or the US dollar in recent years. Eight countries, all of them current or aspiring EU members, track the euro. Of the five countries keying on the US dollar in various degrees, all but one belong to the Commonwealth of Independent States. The study shows that the extent to which each country’s currency tracks the euro (or the dollar) is correlated with the structure of its external trade and finance. However, some countries appear to track the euro or US dollar to an extent that appears inconsistent with inflation targeting, trade or financial integration, or the extent of business cycle synchronization. The phenomenon is particularly pronounced among the euro bloc countries in Emerging Europe, which may be deliberately gravitating around the euro in anticipation of eventually joining the euro area.
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual Networks.

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more attention since the inauguration of the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from all three major types: text-to-speech, voice conversion, and replay. Built upon previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for DNN-based approach to anti-spoofing. ASSERT has four components: feature engineering, DNN models, network optimization and system combination, where the DNN models are variants of squeeze-excitation and residual networks. We conducted an ablation study of the effectiveness of each component on the ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more than 93% and 17% relative improvements over the baseline systems in the two sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing systems. Code and pretrained models will be made publicly available.
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire input document list. This difference leads to the notion of relative relevance between documents in ranking. The majority of the existing learning-to-rank algorithms model such relativity at the loss level using pairwise or listwise loss functions. However, they are restricted to univariate scoring functions, i.e., the relevance score of a document is computed based on the document itself, regardless of other documents in the list. To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list. We refer to this framework as GSFs---groupwise scoring functions. We learn GSFs with a deep neural network architecture, and demonstrate that several representative learning-to-rank algorithms can be modeled as special cases in our framework. We conduct evaluation using click logs from one of the largest commercial email search engines, as well as a public benchmark dataset. In both cases, GSFs lead to significant performance improvements, especially in the presence of sparse textual features.
Effects of Electron-Beam Irradiation on Graphene Oxide

Graphene oxide (GO) is a nanofilm composed of graphene with various oxygen functional groups attached. GO is of interest due to its unique mechanical-enhancement properties, its tunable electronic properties, and its potential use in the wide-scale production of graphene. Scanning electron microscopes (SEMs) are frequently used to characterize and study GO films. The purpose of this project was to study the effects of SEM-imaging on GO films. Using an SEM, we irradiated GO samples at electron beam-energies of 10, 20, and 30 keV (at a constant emission current of ∼40 ± 2 μA) for times ranging from 15 minutes to 1 hour. Raman D- and G-band intensities were used to examine structural modifications/damage to GO samples as a function of beam energy and exposure time. The results suggest that imaging with a 30 keV electron beam for 30 minutes may lead to the formation of amorphous carbon, while imaging with 10 keV or 20 keV beams for 30 minutes does not have a significant effect on GO samples.Graphene oxide (GO) is a nanofilm composed of graphene with various oxygen functional groups attached. GO is of interest due to its unique mechanical-enhancement properties, its tunable electronic properties, and its potential use in the wide-scale production of graphene. Scanning electron microscopes (SEMs) are frequently used to characterize and study GO films. The purpose of this project was to study the effects of SEM-imaging on GO films. Using an SEM, we irradiated GO samples at electron beam-energies of 10, 20, and 30 keV (at a constant emission current of ∼40 ± 2 μA) for times ranging from 15 minutes to 1 hour. Raman D- and G-band intensities were used to examine structural modifications/damage to GO samples as a function of beam energy and exposure time. The results suggest that imaging with a 30 keV electron beam for 30 minutes may lead to the formation of amorphous carbon, while imaging with 10 keV or 20 keV beams for 30 minutes does not have a significant effect on GO samples.
Micro-structuring, ablation, and defect generation in graphene with femtosecond pulses

Femtosecond micromachining offers a contact-free and mask-less technique for material patterning. Femtosecond pulse irradiation of single layer graphene by low energy pulses around 12 nJ over a 15 second illumination period is studied. The ablated graphene holes are surrounded by a radially symmetric region characterized by generated defects, whose extension can be modified based on the laser fluence. The femtosecond induced structural modifications can be attractive for graphene or carbon-based device fabrication as well as sensor and transistor applications, where regions of varying carrier concentrations and different electrical, optical, or physical properties are desired.
Variation in developmental temperature alters adulthood plasticity of thermal tolerance in Tigriopus californicus .

In response to environmental change, organisms rely on both genetic adaptation and phenotypic plasticity to adjust key traits that are necessary for survival and reproduction. Given the accelerating rate of climate change, plasticity may be particularly important. For organisms in warming aquatic habitats, upper thermal tolerance is likely to be a key trait, and many organisms express plasticity in this trait in response to developmental or adulthood temperatures. Although plasticity at one life stage may influence plasticity at another life stage, relatively little is known about this possibility for thermal tolerance. Here, we used locally adapted populations of the copepod Tigriopus californicus to investigate these potential effects in an intertidal ectotherm. We found that low latitude populations had greater critical thermal maxima (CTmax) than high latitude populations, and variation in developmental temperature altered CTmax plasticity in adults. After development at 25°C, CTmax was plastic in adults, whereas no adulthood plasticity in this trait was observed after development at 20°C. This pattern was identical across four populations, suggesting that local thermal adaptation has not shaped this effect among these populations. Differences in the capacities to maintain ATP synthesis rates and to induce heat shock proteins at high temperatures, two likely mechanisms of local adaptation in this species, were consistent with changes in CTmax owing to phenotypic plasticity, which suggests that there is likely mechanistic overlap between the effects of plasticity and adaptation. Together, these results indicate that developmental effects may have substantial impacts on upper thermal tolerance plasticity in adult ectotherms.
Inflation co-movement in emerging and developing Asia: the monsoon effect

Co-movement (synchronicity) in food-inflation rates among emerging and developing countries in Asia is partly due to common rainfall shocks – a result which the paper terms the ‘monsoon effect’. Ec...
Special subgroups of Bianchi groups

We determine C-special subgroups of the Bianchi groups of index bounded above by 120 by effectivising the arguments of Agol-Long-Reid. These subgroups are congruence of level 2 or 4 and retract to the free group on two generators. As a consequence, we find a C-special 20-sheeted cover of the figure-eight knot complement. We also determine C-special congruence subgroups for a family of cocompact arithmetic Kleinian groups.
A Novel Compound Synapse Using Probabilistic Spin–Orbit-Torque Switching for MTJ-Based Deep Neural Networks

Analog electronic nonvolatile memories mimicking synaptic operations are being explored for the implementation of neuromorphic computing systems. Compound synapses consisting of ensembles of stochastic binary elements are alternatives to analog memory synapses to achieve multilevel memory operation. Among the existing binary memory technologies, magnetic tunneling junction (MTJ)-based magnetic random access memory (MRAM) technology has matured to the point of commercialization. More importantly for this work, stochasticity is natural to the MTJ switching physics, e.g., devices referred to as p-bits that mimic binary stochastic neurons. In this article, we experimentally demonstrate a novel compound synapse that uses stochastic spin–orbit-torque (SOT) switching of an ensemble of nanomagnets that are located on one shared spin Hall effect (SHE) material channel, i.e., tantalum. By using a properly chosen pulse scheme, we are able to demonstrate linear potentiation and depression in the synapse, as required for many neuromorphic architectures. In addition to this experimental effort, we also performed circuit simulations on an SOT-MRAM-based $784\times 200\times 10$ deep belief network (DBN) consisting of p-bit-based neurons and compound synapses. MNIST pattern recognition was used to evaluate the system performance, and our findings indicate that a significant reduction in recognition error rates can be achieved by improving the linearity of the potentiation and depression curves using an incremental pulse scheme.
Simple Near-Optimal Scheduling for the M/G/1

We consider the problem of preemptively scheduling jobs to minimize mean response time of an M/G/1 queue. When we know each job's size, the shortest remaining processing time (SRPT) policy is optimal. Unfortunately, in many settings we do not have access to each job's size. Instead, we know only the job size distribution. In this setting the Gittins policy is known to minimize mean response time, but its complex priority structure can be computationally intractable. A much simpler alternative to Gittins is the shortest expected remaining processing time (SERPT) policy. While SERPT is a natural extension of SRPT to unknown job sizes, it is unknown whether or not SERPT is close to optimal for mean response time. We present a new variant of SERPT called monotonic SERPT (M-SERPT) which is as simple as SERPT but has provably near-optimal mean response time at all loads for any job size distribution. Specifically, we prove the mean response time ratio between M-SERPT and Gittins is at most 3 for load ρ leq 8/9$ and at most 5 for any load. This makes M-SERPT the only non-Gittins scheduling policy known to have a constant-factor approximation ratio for mean response time.
The real Oaxaca decomposition: convergence within Mexico’s Oaxaca region in the twenty-first century—Do types of crime and religious belief matter?

The paper empirically examines municipality convergence within Mexico’s southern Oaxaca region. We find municipalities are converging more rapidly than the “iron law” of 2% with up to 8% β convergence. Though homicides from the Drug War have negatively impacted growth, overall crime perhaps through strengthening local institutions has a positive but weak impact. Higher income inequality and Catholic belief are associated with higher per capita GDP growth in Oaxaca overall and within two microregions. The positive impact of a higher share indigenous population on growth in the relatively urban Valles Centrales microregion suggests a cultural element to policy making with the dependency ratio drag on growth suggesting a need for policies that give more attention to how retirees can continue to participate in the economy as well.
Hot White Dwarfs

Research on hot white dwarfs during the past 30 years has greatly expanded, as many new discoveries and the new questions they raise have emerged from increasingly larger, deeper surveys conducted with multimeter class ground-based telescopes, the International Ultraviolet Explorer (IUE), the Hubble Space Telescope (HST), the Extreme Ultraviolet Explorer (EUVE), and the Far Ultraviolet Spectroscopic Explorer (FUSE). This review will focus on white dwarfs ranging in temperature from 20 000 up to 200 000 K and higher, which are the hottest white dwarf stars known. Since the mid-twentieth century, the earliest spectroscopic surveys of white dwarf candidates from the proper motion selected samples of Willem Luyten and Henry Giclas were carried out by Jesse Greenstein, Olin Eggen, James Liebert, Richard Green, and others. The selection criteria employed in many of these surveys did not reveal a large number of hot white dwarfs because the surveys lacked ultraviolet sensitivity and also missed objects with low flux levels in the optical. Nevertheless, the earliest surveys quickly revealed that white dwarfs divide into two basic composition groups with hydrogen-rich (the DA stars) and heliumrich atmospheric compositions (the DB and other non-DA stars). The origin of this dichotomy still represents a major unsolved problem in stellar evolution, although theoretical advances in late stellar evolution made starting in the 1980s, as well as advances in modeling envelope physical processes and mass loss, have shed important new light on this puzzle (Chayer et al., 1995; Fontaine and Michaud, 1979; Iben, 1984; Iben et al., 1983; Schoenberner, 1983; Unglaub and Bues, 1998, 2000; Vauclair et al., 1979). The spectroscopic properties of white dwarfs are determined by a host of physical processes which control and/or modify the flow of elements and, hence, surface abundances in high gravity atmospheres: convective dredge-up, mixing and dilution, accretion of gas and dust from the interstellar medium and debris disks, gravitational and thermal diffusion, radiative forces, mass loss due to wind outflow and episodic mass ejection, late nuclear shell burning and late thermal pulses, ro-
Impact of oral probiotic Lactobacillus acidophilus vaccine strains on the immune response and gut microbiome of mice.

The potential role of probiotic bacteria as adjuvants in vaccine trials led to their use as nonparenteral live mucosal vaccine vectors. Yet, interactions between these vectors, the host and the microbiome are poorly understood. This study evaluates impact of three probiotic, Lactobacillus acidophilus, vector strains, and their interactions with the host's immune response, on the gut microbiome. One strain expressed the membrane proximal external region from HIV-1 (MPER). The other two expressed MPER and either secreted interleukin-1s (IL-1s) or expressed the surface flagellin subunit C (FliC) as adjuvants. We also used MPER with rice bran as prebiotic supplement. We observed a strain dependent, differential effect suggesting that MPER and IL-1β induced a shift of the microbiome while FliC had minimal impact. Joint probiotic and prebiotic use resulted in a compound effect, highlighting a potential synbiotic approach to impact efficacy of vaccination. Careful consideration of constitutive adjuvants and use of prebiotics is needed depending on whether or not to target microbiome modulation to improve vaccine efficacy. No clear associations were observed between total or MPER-specific IgA and the microbiome suggesting a role for other immune mechanisms or a need to focus on IgA-bound, resident microbiota, most affected by an immune response.
Serum Amyloid A Proteins Induce Pathogenic Th17 Cells and Promote Inflammatory Disease

Summary Lymphoid cells that produce interleukin (IL)-17 cytokines protect barrier tissues from pathogenic microbes but are also prominent effectors of inflammation and autoimmune disease. T helper 17 (Th17) cells, defined by RORγt-dependent production of IL-17A and IL-17F, exert homeostatic functions in the gut upon microbiota-directed differentiation from naive CD4+ T cells. In the non-pathogenic setting, their cytokine production is regulated by serum amyloid A proteins (SAA1 and SAA2) secreted by adjacent intestinal epithelial cells. However, Th17 cell behaviors vary markedly according to their environment. Here, we show that SAAs additionally direct a pathogenic pro-inflammatory Th17 cell differentiation program, acting directly on T cells in collaboration with STAT3-activating cytokines. Using loss- and gain-of-function mouse models, we show that SAA1, SAA2, and SAA3 have distinct systemic and local functions in promoting Th17-mediated inflammatory diseases. These studies suggest that T cell signaling pathways modulated by the SAAs may be attractive targets for anti-inflammatory therapies.
On the Classification of q-Algebras

The problem is the classification of the ideals of ‘free differential algebras’, or the associated quotient algebras, the q-algebras; being finitely generated, unital C-algebras with homogeneous relations and a q-differential structure. This family of algebras includes the quantum groups, or at least those that are based on simple (super) Lie or Kac–Moody algebras. Their classification would encompass the so far incompleted classification of quantized (super) Kac–Moody algebras and of the (super) Kac–Moody algebras themselves. These can be defined as singular limits of q-algebras, and it is evident that to deal with the q-algebras in their full generality is more rational than the examination of each singular limit separately. This is not just because quantization unifies algebras and superalgebras, but also because the points ‘q=1’ and ‘q=−1’ are the most singular points in parameter space. In this Letter, one of two major hurdles in this classification program has been overcome. Fix a set of integers n1,...,nk, and consider the space \(\mathcal{B}_Q\) of homogeneous polynomials of degree n1 in the generator e1, and so on. Assume that there are no constants among the polynomials of lower degree, in any one of the generators; in this case all constants in the space \(\mathcal{B}_Q\) have been classified. The task that remains, the more formidable one, is to remove the stipulation that there are no constants of lower degree.
Pluricomplex Green Functions on Manifolds

In this paper we prove the basic facts for pluricomplex Green functions on manifolds. The main goal is to establish properties of complex manifolds that make them analogous to relatively compact or hyperconvex domains in Stein manifolds.
A sliding-window approach for improved VMAT dose calculation accuracy

ABSTRACT The continuous delivery of volumetric modulated arc therapy (VMAT) plans is usually approximated by discrete apertures at evenly-spaced gantry angles for dose calculation purposes. This approximation can potentially lead to large dose calculation errors if the gantry angle spacings are large and/or there are large changes in the MLC apertures from one control point (CP) to the next. In this work, we developed a sliding-window (SW) method to improve VMAT dose calculation accuracy. For any 2 adjacent VMAT CPs ni and ni + 1, the dose distribution was approximated by a 2-CP SW IMRT beam with the starting MLC positions at CP ni and ending MLC positions at CP ni + 1, with the gantry angle fixed in the middle of the 2 VMAT CPs. Therefore, a VMAT beam with N CPs was approximated by a SW plan with N-1 SW beams. To validate the method, VMAT plans were generated for 10 patients in Pinnacle using 4° gantry spacing. Each plan was converted to a SW plan and dose was recalculated. Another VMAT plan, with 1° gantry spacing, was created by interpolating the original VMAT beam. The original plans were delivered on an Elekta Versa HD and measured with ArcCHECK. For both the isodose distribution and DVH, there were significant differences between the original VMAT plan and either the SW or the interpolated plan. However, they were indistinguishable between the SW and the interpolated plans. When compared with measurement, the average passing rates of the original VMAT plans were 87.3 ± 2.8% and 93.1 ± 1.0% for the 5 HN and 5 spine SBRT cases, respectively. On the other hand, the passing rates for both the VMAT1 and SW plans were above 95% for all the 10 cases studied. The dose calculation times of the original VMAT plans and the SW plans were very similar. We conclude that the proposed SW approach improves VMAT dose calculation accuracy without increase in dose calculation time.
Dynamics of team library adoptions: an exploration of GitHub commit logs

When a group of people strives to understand new information, struggle ensues as various ideas compete for attention. Steep learning curves are surmounted as teams learn together. To understand how these team dynamics play out in software development, we explore Git logs, which provide a complete change history of software repositories. In these repositories, we observe code additions, which represent successfully implemented ideas, and code deletions, which represent ideas that have failed or been superseded. By examining the patterns between these commit types, we can begin to understand how teams adopt new information. We specifically study what happens after a software library is adopted by a project, i.e., when a library is used for the first time in the project. We find that a variety of factors, including team size, library popularity, and prevalence on Stack Overflow are associated with how quickly teams learn and successfully adopt new software libraries.
A genetic, genomic, and computational resource for exploring neural circuit function

In the brain, large numbers of different types of neurons connect with each other to form complex networks. In recent years, researchers have made great progress in mapping all the connections between these cells, creating ‘wiring diagrams’ known as connectomes. However, charting the connections between neurons does not give all the answers as to how the brain works; for example, it does not necessarily reveal the nature of the information two connected cells exchange. Assessing which genes are switched on in different neurons can give insight into neuronal properties that are not obvious from physical connections alone. To fill that knowledge gap, Davis, Nern et al. aimed to measure the genes expressed in a well-characterized network of neurons in the fruit fly visual system. First, 100 fly strains were established, each carrying a single type of neuron colored with a fluorescent marker. Then, a biochemical approach was developed to extract the part of the cell that contains the genetic code from the neurons with the marker. Finally, a statistical tool was used to assess which genes were on in each type of neurons. This led to the creation of a database that shows whether 15,000 genes in each neuron type across 100 fly strains were switched on. Combining this information with previous knowledge about the flies’ visual system revealed new information: for example, it helped to understand which chemicals the neurons use to communicate, and whether certain cells activate or inhibit each other. The work by Davis, Nern et al. demonstrates how genetic approaches can complement other methods, and it offers a new tool for other scientists to use in their work. With more advanced genetic methods, it may one day become possible to better grasp how complex brains in other organisms are organized, and how they are disrupted in disease.
Solid-state tube wakefield accelerator using surface waves in crystals

Solid-state or crystal acceleration has for long been regarded as an attractive frontier in advanced particle acceleration. However, experimental investigations of solid-state acceleration mechanis...
Randomized Prospective Study Evaluating Single-Injection Paravertebral Block, Paravertebral Catheter, and Thoracic Epidural Catheter for Postoperative Regional Analgesia After Video-Assisted Thoracoscopic Surgery

Objective Video-assisted thoracoscopic surgery (VATS) has improved patient outcomes; however, postoperative pain remains potentially severe. The objective of this study was to compare adjunct analgesic modalities for VATS, including paravertebral nerve blockade (PVB) and thoracic epidural anesthesia (TEA). Design Prospective, randomized trial. Setting Large academic hospital, single institution. Participants Adult patients undergoing VATS. Interventions Ultrasound-guided PVB catheter, ultrasound-guided single-injection PVB, or TEA. Measurements and Main Results Postoperative visual analog scale pain scores (at rest and with knee flexion) and opioid usage were recorded. Pain scores (with movement) for the TEA group were lower than those for either PVB group at 24 hours (p ≤ 0.008) and for the PVB catheter group at 48 hours (p = 0.002). Opioid use in TEA group was lower than that for either PVB group at 24 and 48 hours (p Conclusions TEA led to lower pain scores and opioid requirement for VATS procedures compared with PVB techniques. Single-injection PVB was faster and equally as effective as PVB catheter, and it led to similar patient satisfaction as TEA; therefore, it should be considered in patients who are not ideal candidates for TEA.
Dynamic Assessment for Identifying Spanish-Speaking English Learners' Risk for Mathematics Disabilities: Does Language of Administration Matter?.

We examined dynamic assessment's (DA's) added value over traditional assessments for identifying Spanish-speaking English learners' (ELs) risk for developing mathematics disabilities, as a function of the language of test administration (English vs. Spanish), type of math outcome, and EL's language dominance. At the start of first grade, ELs (N = 368) were randomly assigned to English-DA or Spanish-DA conditions, were assessed on static mathematics measures and domain-general (language, reasoning) measures in English, and completed DA in their assigned language condition. At year's end, they were assessed on calculation and word-problem solving outcomes in English. Results from multigroup path models indicated that Spanish-DA mitigates the impact of ELs' language dominance on DA performance. Moreover, ELs' language dominance moderated DA's predictive validity differentially depending on DA language and type of outcome. Spanish-DA showed higher predictive validity in Spanish-dominant ELs than English-dominant ELs when predicting calculations but not word-problem solving. English-DA was predictive for both outcomes, regardless of ELs' language dominance.
TicToc: Enabling Bandwidth-Efficient DRAM Caching for Both Hits and Misses in Hybrid Memory Systems

This paper investigates bandwidth-efficient DRAM caching for hybrid DRAM + 3D-XPoint memories. 3D-XPoint is becoming a viable alternative to DRAM as it enables high-capacity and non-volatile main memory systems. However, 3D-XPoint has several characteristics that limit it from outright replacing DRAM: 4-8x slower read, and even worse writes. As such, effective DRAM caching in front of 3D-XPoint is important to enable a high-capacity, low-latency, and high-write-bandwidth memory. There are currently two major approaches for DRAM cache design: (1) a Tag-Inside-Cacheline (TIC) organization that optimizes for hits, by storing tag next to each line such that one access gets both tag and data, and (2) a Tag-Outside-Cacheline (TOC) organization that optimizes for misses, by storing tags from multiple data lines together in a tag-line such that one access to a tag-line gets information on several data-lines. Ideally, we would like to have the low hit-latency of TIC designs, and the low miss-bandwidth of TOC designs. To this end, we propose a TicToc organization that provisions both TIC and TOC to get the hit and miss benefits of both. We find that naively combining both techniques actually performs worse than TIC individually, because one has to pay the bandwidth cost of maintaining both metadata. The main contribution of this work is developing architectural techniques to reduce bandwidth cost of accessing and maintaining both TIC and TOC metadata. We find that most of the update bandwidth is due to maintaining the TOC dirty information. We propose a DRAM Cache Dirtiness Bit technique that carries DRAM cache dirty information to last-level caches, to help prune repeated dirty-bit updates for known dirty lines. We also propose a Preemptive Dirty Marking (PDM) technique that predicts which lines will be written and proactively marks the dirty bit at install time, to help avoid the initial dirty-bit update for dirty lines. To support PDM, we develop a novel PC-based Write-Predictor to aid in marking only write-likely lines. Our evaluations on a 4GB DRAM cache in front of 3D-XPoint show that our TicToc organization enables 10% speedup over the baseline TIC, nearing the 14% speedup possible with an idealized DRAM cache design with 64MB of SRAM tags, while needing only 34KB SRAM.
Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes

Summary Host response to infection is a major determinant of disease severity in Ebola virus disease (EVD), but gene expression programs associated with outcome are poorly characterized. Collaborative Cross (CC) mice develop strain-dependent EVD phenotypes of differential severity, ranging from tolerance to lethality. We screen 10 CC lines and identify clinical, virologic, and transcriptomic features that distinguish tolerant from lethal outcomes. Tolerance is associated with tightly regulated induction of immune and inflammatory responses shortly following infection, as well as reduced inflammatory macrophages and increased antigen-presenting cells, B-1 cells, and γδ T cells. Lethal disease is characterized by suppressed early gene expression and reduced lymphocytes, followed by uncontrolled inflammatory signaling, leading to death. We apply machine learning to predict outcomes with 99% accuracy in mice using transcriptomic profiles. This signature predicts outcomes in a cohort of EVD patients from western Africa with 75% accuracy, demonstrating potential clinical utility.
Underactuation Design for Tendon-Driven Hands via Optimization of Mechanically Realizable Manifolds in Posture and Torque Spaces

Grasp synergies represent a useful idea to reduce grasping complexity without compromising versatility. Synergies describe coordination patterns between joints, either in terms of position (joint angles) or effort (joint torques). In both of these cases, a grasp synergy can be represented as a low-dimensional manifold lying in the high-dimensional joint posture or torque space. In this article, we use the term mechanically realizable manifolds to refer to the subset of such manifolds (in either posture or torque space) that can be achieved via mechanical coupling of the joints in underactuated hands. We present a method to optimize the design parameters of an underactuated hand in order to shape the mechanically realizable manifolds to fit a predefined set of desired grasps. Our method guarantees that the resulting synergies can be physically implemented in an underactuated hand, and will enable the resulting hand to both reach the desired grasp postures and achieve quasi-static equilibrium while loading the grasps. We demonstrate this method on three concrete design examples motivated by a real use case, and evaluate and compare their performance in practice.
Using weaker consistency models with monitoring and recovery for improving performance of key-value stores

Consistency properties provided by most key-value stores can be classified into sequential consistency and eventual consistency. The former is easier to program with but suffers from lower performance whereas the latter suffers from potential anomalies while providing higher performance. We focus on the problem of what a designer should do if he/she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P, and continues to run the protocol, as our system monitors P. If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds.We evaluate this approach with graph-based applications running on the Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2 instances show that using eventual consistency with monitoring can provide a 50–80% increase in throughput when compared with sequential consistency. We also observe that the overhead of the monitoring itself was low (typically less than 4%) and the latency of detecting violations was small. In particular, in a scenario designed to intentionally cause a large number of violations, more than 99.9% of violations were detected in less than 50 ms in regional networks (all clients and servers in the same Amazon AWS region) and in less than 3 s in global networks.We find that for some applications, frequent rollback can cause the program using eventual consistency to effectively stall. We propose alternate mechanisms for dealing with re-occurring rollbacks. Overall, for applications considered in this paper, we find that even with rollback, eventual consistency provides better performance than using sequential consistency.
The effect of number of healthcare visits on study sample selection in electronic health record data.

Introduction Few studies have addressed how to select a study sample when using electronic health record (EHR) data. Objective To examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence. Methods Year 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. In 2016, each of these health systems saw between 160,000 and 420,000 unique patients. Curated collections of ICD-9, ICD-10, and SNOMED codes (from CMS-approved electronic clinical quality measures) were used to define three diseases: acute myocardial infarction, asthma, and diabetic nephropathy). Results Across all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases. Conclusions In addition to providing thorough descriptions of case definitions, when using EHR data authors must carefully describe how a study sample is identified and report data for a range of sample definitions, including minimum number of visits, so that others can assess the sensitivity of reported results to sample definition in EHR data.
Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Abstract Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.
Frame-Based Overlapping Speech Detection Using Convolutional Neural Networks

Naturalistic speech recordings usually contain speech signals from multiple speakers. This phenomenon can degrade the performance of speech technologies due to the complexity of tracing and recognizing individual speakers. In this study, we investigate the detection of overlapping speech on segments as short as 25 ms using Convolutional Neural Networks. We evaluate the detection performance using different spectral features, and show that pyknogram features outperforms other commonly used speech features. The proposed system can predict overlapping speech with an accuracy of 84% and Fs-core of 88% on a dataset of mixed speech generated based on the GRID dataset.
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model

While the community keeps promoting end-to-end models over conventional hybrid models, which usually are long short-term memory (LSTM) models trained with a cross entropy criterion followed by a sequence discriminative training criterion, we argue that such conventional hybrid models can still be significantly improved. In this paper, we detail our recent efforts to improve conventional hybrid LSTM acoustic models for high-accuracy and low-latency automatic speech recognition. To achieve high accuracy, we use a contextual layer trajectory LSTM (cltLSTM), which decouples the temporal modeling and target classification tasks, and incorporates future context frames to get more information for accurate acoustic modeling. We further improve the training strategy with sequence-level teacher-student learning. To obtain low latency, we design a two-head cltLSTM, in which one head has zero latency and the other head has a small latency, compared to an LSTM. When trained with Microsoft’s 65 thousand hours of anonymized training data and evaluated with test sets with 1.8 million words, the proposed two-head cltLSTM model with the proposed training strategy yields a 28.2% relative WER reduction over the conventional LSTM acoustic model, with a similar perceived latency.
Simple Bayesian Algorithms for Best-Arm Identification

This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure...
Government Form and Public Spending: Theory and Evidence from U.S. Municipalities

There are two main forms of government in US cities: councilmanager and mayor-council. This paper develops a theory of fiscal policy determination under these two forms. The theory predicts that expected public spending will be lower under mayor-council but that either form of government could be favored by a majority of citizens. The latter prediction means that the theory is consistent with the coexistence of both government forms. Support for the former prediction is found in both a cross-sectional analysis and a panel analysis of changes in government form. {JEL HI 1, H72, R51) are two main forms of government in US cities: mayor-council and council-manager. Under the mayor-council form, a mayor and city council are independently elected by voters and jointly develop policy. Under the council-manager form, policy-making power resides with the city council. The council appoints a manager to assist in the administration of city government functions, but this manager has no authority over policy development and can be replaced at any time by a vote of the council.1 While some council-manager cities retain the position of mayor, the role is typically largely ceremonial.2 This paper develops a theory of fiscal policy determination under these two forms of government. This theory considers a city government charged with choosing among a set of projects or programs that could be undertaken. It assumes that the passage of projects under mayor-council requires the support of both the mayor and a majority of council-members, whereas under council-manager it requires the support of only the council. In addition, it assumes that voters have only partial
Exchange Market Pressure and Absorption by International Reserves: Emerging Markets and Fear of Reserve Loss During the 2008-09 Crisis

This paper evaluates how the global financial crisis emanating from the U.S. was transmitted to emerging markets. Our focus is on the extent that the crisis caused external market pressures (EMP), and whether the absorption of the shock was mainly through exchange rate depreciation or the loss of international reserves. Controlling for variety of factors associated with EMP, we find clear evidence that emerging markets with higher total foreign liabilities, including short- and long-term debt, equities, FDI and derivative products—had greater exposure and were much more vulnerable to the financial crisis. Countries with large balance sheet exposure -- high external portfolio liabilities exceeding international reserves—absorbed the global shock by allowing greater exchange rate depreciation and comparatively less reserve loss. Despite the remarkable buildup of international reserves by emerging markets during the period prior to the financial crisis, countries relied primarily on exchange rate depreciation rather than reserve loss to absorb most of the exchange market pressure shock. This could reflect a deliberate choice (“fear of reserve loss” or competitive depreciations) or market actions that caused very rapid exchange rate adjustment, especially in emerging markets with open capital markets, overwhelming policy actions.
Political Accountability Under Alternative Institutional Regimes

We analyze the interaction between electoral accountability and separation-of-powers by comparing three regimes: ‘Unilateral Authority’ (the President has exclusive decision-making power); ‘Mandatory Checks’ (the President cannot change policy without congressional assent); and ‘Opt-in Checks’ (the President may seek congressional authorization or act unilaterally). We find: (1) voters use asymmetric electoral rewards and punishments to offset the risk of politician bias, but voters rely less on this blunt instrument if there are internal checks; (2) adding a veto player need not alter the ex ante likelihood of policy change; and (3) voter welfare is highest under Opt-In Checks and lowest under Unilateral Authority.
Estimating Latent Asset-Pricing Factors

Abstract We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with smaller pricing errors.
7 – Marginal costing

Publisher Summary This chapter discusses marginal costing. Marginal costing is a principle whereby variable costs are charged to cost units, and the fixed cost attributable to the relevant period is written off in full against the contribution for the period. One of the most useful ways of using marginal costing is in reporting. It is especially useful, where there is a need to monitor operating divisions or activities, and deal with a corporate fixed overhead. If reports are a means of control on operations management, then it is unrealistic for them to take an arbitrary allocated cost over which they have no control, and then appraise them on it. Decision making requires one type of valuation, and inventory valuation requires another. An opportunity cost is defined as the value of a benefit sacrificed in favor of an alternative course of action.