cssi/SciSciGPT-SciSciNet huggingface dataset:
Revisiting AES-GCM-SIV: Multi-user Security, Faster Key Derivation, and Better Bounds

This paper revisits the multi-user (mu) security of symmetric encryption, from the perspective of delivering an analysis of the \(\mathsf {AES\text {-}GCM\text {-}SIV}\) AEAD scheme. Our end result shows that its mu security is comparable to that achieved in the single-user setting. In particular, even when instantiated with short keys (e.g., 128 bits), the security of \(\mathsf {AES\text {-}GCM\text {-}SIV}\) is not impacted by the collisions of two user keys, as long as each individual nonce is not re-used by too many users. Our bounds also improve existing analyses in the single-user setting, in particular when messages of variable lengths are encrypted. We also validate security against a general class of key-derivation methods, including one that halves the complexity of the final proposal.
Capturing the Onset of PRC2-Mediated Repressive Domain Formation.

Summary Polycomb repressive complex 2 (PRC2) maintains gene silencing by catalyzing methylation of histone H3 at lysine 27 (H3K27me2/3) within chromatin. By designing a system whereby PRC2-mediated repressive domains were collapsed and then reconstructed in an inducible fashion in vivo, a two-step mechanism of H3K27me2/3 domain formation became evident. First, PRC2 is stably recruited by the actions of JARID2 and MTF2 to a limited number of spatially interacting “nucleation sites,” creating H3K27me3-forming Polycomb foci within the nucleus. Second, PRC2 is allosterically activated via its binding to H3K27me3 and rapidly spreads H3K27me2/3 both in cis and in far-cis via long-range contacts. As PRC2 proceeds further from the nucleation sites, its stability on chromatin decreases such that domains of H3K27me3 remain proximal, and those of H3K27me2 distal, to the nucleation sites. This study demonstrates the principles of de novo establishment of PRC2-mediated repressive domains across the genome.
Cis-activation in the Notch signaling pathway

The Notch signaling pathway consists of transmembrane ligands and receptors that can interact both within the same cell (cis) and across cell boundaries (trans). Previous work has shown that cis-interactions act to inhibit productive signaling. Here, by analyzing Notch activation in single cells while controlling cell density and ligand expression level, we show that cis-ligands can also activate Notch receptors. This cis-activation process resembles trans-activation in its ligand level dependence, susceptibility to cis-inhibition, and sensitivity to Fringe modification. Cis-activation occurred for multiple ligand-receptor pairs, in diverse cell types, and affected survival in neural stem cells. Finally, mathematical modeling shows how cis-activation could potentially expand the capabilities of Notch signaling, for example enabling ‘negative’ (repressive) signaling. These results establish cis-activation as an additional mode of signaling in the Notch pathway, and should contribute to a more complete understanding of how Notch signaling functions in developmental, physiological, and biomedical contexts.
Spliceosome Profiling Visualizes Operations of a Dynamic RNP at Nucleotide Resolution

Tools to understand how the spliceosome functions in vivo have lagged behind advances in the structural biology of the spliceosome. Here, methods are described to globally profile spliceosome-bound pre-mRNA, intermediates, and spliced mRNA at nucleotide resolution. These tools are applied to three yeast species that span 600 million years of evolution. The sensitivity of the approach enables the detection of canonical and non-canonical events, including interrupted, recursive, and nested splicing. This application of statistical modeling uncovers independent roles for the size and position of the intron and the number of introns per transcript in substrate progression through the two catalytic stages. These include species-specific inputs suggestive of spliceosome-transcriptome coevolution. Further investigations reveal the ATP-dependent discard of numerous endogenous substrates after spliceosome assembly in vivo and connect this discard to intron retention, a form of splicing regulation. Spliceosome profiling is a quantitative, generalizable global technology used to investigate an RNP central to eukaryotic gene expression.
Passive mutual funds and ETFs: Performance and comparison

Abstract Over 26% of investment company assets are held in passively managed vehicles. Thus, it is important to understand what affects the performance of passive vehicles and how to choose among the multiple passive options following any index. This paper examines the factors that are important in explaining differences across funds following the same index and demonstrates how to select a passive vehicle that has a high probability of having the best performance in the following years.
Pricing and Product Design for Vice Goods: A Strategic Analysis

The rising obesity epidemic is a worldwide concern for consumers, firms, and policy makers. One reason for the rise in obesity is consumers’ over-consumption of vice goods such as cookies, crackers...
Nudging energy efficiency audits: Evidence from a field experiment

Abstract This paper uses a randomized field experiment to test how information provision leveraging social norms, salience, and a personal touch can serve as a nudge to influence the uptake of residential energy audits. Our results show that a low-cost carefully-crafted notecard can increase the probability of a household to follow through with an already scheduled audit by 1.1 percentage points on a given day. The effect is very similar across individuals with different political views, but households in rural areas display a substantially greater effect than those in urban areas. Our findings have important managerial and policy implications, as they suggest a cost-effective nudge for increasing energy audit uptake and voluntary energy efficiency adoption.
Low-Noise Operating Mode for Propeller-Driven Electric Airplanes

Mechanical shaft power and shaft speed of reciprocating internal combustion engines are closely coupled. Maximum rated shaft power is typically produced at or near peak shaft speed. If a general aviation airplane equipped with a reciprocating engine and a variable-pitch propeller attempts a low-noise takeoff by reducing propeller tip speed, propeller power and thrust are reduced. Such takeoffs are not tolerated due to punishing performance effects, such as increased field lengths and poor climb rates. Certain electric motors, however, are able to deliver maximum shaft power over a wide range of shaft speed. Electric or hybrid-electric propeller-driven airplanes should be able to exploit this behavior. At low shaft speeds, high shaft power levels and high blade pitch angles could be combined to recover much of the thrust that would otherwise be lost. This could enable a low-noise operating mode for propellers normally designed for performance rather than for noise. The subject of this paper is an analytical investigation into low-noise takeoffs and steady overflights of a notional general aviation airplane equipped with a propeller driven by an electric motor.
Medusa: A Scalable Interconnect for Many-Port DNN Accelerators and Wide DRAM Controller Interfaces

To cope with the increasing demand and computational intensity of deep neural networks (DNNs), industry and academia have turned to accelerator technologies. In particular, FPGAs have been shown to provide a good balance between performance and energy efficiency for accelerating DNNs. While significant research has focused on how to build efficient layer processors, the computational building blocks of DNN accelerators, relatively little attention has been paid to the on-chip interconnects that sit between the layer processors and the FPGA's DRAM controller. We observe a disparity between DNN accelerator interfaces, which tend to comprise many narrow ports, and FPGA DRAM controller interfaces, which tend to be wide buses. This mismatch causes traditional interconnects to consume significant FPGA resources. To address this problem, we designed Medusa: an optimized FPGA memory interconnect which transposes data in the interconnect fabric, tailoring the interconnect to the needs of DNN layer processors. Compared to a traditional FPGA interconnect, our design can reduce LUT and FF use by 4.7x and 6.0x, and improves frequency by 1.8x.
Hilbert spaces contractively contained in weighted Bergman spaces on the unit disk

Abstract Sub-Bergman Hilbert spaces are analogues of de Branges–Rovnyak spaces in the Bergman space setting. They are reproducing kernel Hilbert spaces contractively contained in the Bergman space of the unit disk. K. Zhu analyzed sub-Bergman Hilbert spaces associated with finite Blaschke products, and proved that they are norm equivalent to the Hardy space. Later S. Sultanic found a different proof of Zhu's result, which works in weighted Bergman space settings as well. In this paper, we give a new approach to this problem and obtain a stronger result. Our method relies on the theory of reproducing kernel Hilbert spaces.
Fit for a Bayesian: An Evaluation of PPP and DIC for Structural Equation Modeling

Despite its importance to structural equation modeling, model evaluation remains underdeveloped in the Bayesian SEM framework. Posterior predictive p-values (PPP) and deviance information criteria ...
MDN brain descending neurons coordinately activate backward and inhibit forward locomotion

When we choose to make one kind of movement, it often prevents us making another. We cannot move forward and backward at the same time, for example, and a horse cannot simultaneously gallop and walk. These ‘antagonistic’ behaviors often use the same group of muscles, but the muscles contract in a different order. This requires exquisite control over muscle contractions. Neurons located in the central nervous system form circuits to produce distinct patterns of muscle contractions and to switch between these patterns. Smooth, rapid switching between behaviors is important for animal escape and survival, as well as for performing fine movements. However, we know little about how the activity of the neuronal circuits enables this. Carreira-Rosario, Zarin, Clark et al. set out to identify the underlying neuronal circuitry that allows larval fruit flies to transition between crawling forward and backward. Results from a combination of genetics and microscopy techniques revealed that a neuron called the Mooncrawler Descending Neuron (MDN) induces a switch from forward to backward travel. MDN activates a neuron that stops the larvae crawling forward, and at the same time activates a different neuron that is only active when the larvae crawl backward. Carreira-Rosario et al. also found that MDN triggers backward crawling in the six-limbed adult fly. Understanding how a single neuron – in this case MDN – can trigger a smooth switch between opposing behaviors could be beneficial for the medical and robotics fields. In the medical field, understanding how movement is generated could help to improve therapies that fix damage to the relevant neuronal circuits. Understanding how behavioral transitions occur may also help to design autonomous robots that can navigate complex terrain.
Reinforcement Learning Testbed for Power-Consumption Optimization

Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and often lead to suboptimal or unstable performance. In this paper, we show how deep reinforcement learning techniques can be used to control the cooling system of a simulated data center. In contrast to common control algorithms, those based on reinforcement learning techniques can optimize a system’s performance automatically without the need of explicit model knowledge. Instead, only a reward signal needs to be designed. We evaluated the proposed algorithm on the open source simulation platform EnergyPlus. The experimental results indicate that we can achieve 22% improvement compared to a model-based control algorithm built into the EnergyPlus. To encourage the reproduction of our work as well as future research, we have also publicly released an open-source EnergyPlus wrapper interface (https://github.com/IBM/rl-testbed-for-energyplus) directly compatible with existing reinforcement learning frameworks.
Learning End-to-End Goal-Oriented Dialog with Multiple Answers.

In a dialog, there could be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of end-to-end goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks. We also release permuted-bAbI dialog tasks, our proposed testbed, to the community for evaluating dialog systems in a goal-oriented setting.
A Bayesian framework for high-throughput T cell receptor pairing.

MOTIVATION The study of T cell receptor (TCR) repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for TCRs. RESULTS Here, we describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. This method relies upon the distribution of a T cell population across a 96-well plate, followed by barcoding and sequencing of the relevant transcripts from each T cell. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract TCR information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications. AVAILABILITY AND IMPLEMENTATION The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
When does absence of evidence constitute evidence of absence

Negative forensic evidence can be defined as the failure to find a trace after looking for it. Such evidence is often dismissed by referring to the aphorism "absence of evidence is not evidence of absence." However, this reasoning can be misleading in the context of forensic science. This commentary is designed to help forensic scientists understand the probative value of negative forensic evidence.
Profiling grapevine trunk pathogens in planta: a case for community-targeted DNA metabarcoding

DNA metabarcoding, commonly used in exploratory microbial ecology studies, is a promising method for the simultaneous in planta-detection of multiple pathogens associated with disease complexes, such as the grapevine trunk diseases. Profiling of pathogen communities associated with grapevine trunk diseases is particularly challenging, due to the presence within an individual wood lesion of multiple co-infecting trunk pathogens and other wood-colonizing fungi, which span a broad range of taxa in the fungal kingdom. As such, we designed metabarcoding primers, using as template the ribosomal internal transcribed spacer of grapevine trunk-associated ascomycete fungi (GTAA) and compared them to two universal primer widely used in microbial ecology. We first performed in silico simulations and then tested the primers by high-throughput amplicon sequencing of (i) multiple combinations of mock communities, (ii) time-course experiments with controlled inoculations, and (iii) diseased field samples from vineyards under natural levels of infection. All analyses showed that GTAA had greater affinity and sensitivity, compared to those of the universal primers. Importantly, with GTAA, profiling of mock communities and comparisons with shotgun-sequencing metagenomics of field samples gave an accurate representation of genera of important trunk pathogens, namely Phaeomoniella, Phaeoacremonium, and Eutypa, the abundances of which were over- or under-estimated with universal primers. Overall, our findings not only demonstrate that DNA metabarcoding gives qualitatively and quantitatively accurate results when applied to grapevine trunk diseases, but also that primer customization and testing are crucial to ensure the validity of DNA metabarcoding results.
Supply Chain Power and Real Earnings Management: Stock Market Perceptions, Financial Performance Effects, and Implications for Suppliers

This study examines supply chain power in the context of real earnings management (REM), instances in which executives execute (or forego) operations transactions for the sole purpose of meeting or beating earnings targets. We examine whether powerful major customers in supply chains exploit their positions to engage in REM to a greater degree than less powerful firms. We also examine (1) whether the stock market reacts differently to major customers’ and nonmajor customers’ REM, (2) whether any difference exists between major customers’ and nonmajor customers’ post‐REM financial performance, and (3) how suppliers are impacted by their major customers’ REM behavior. Results suggest that major customers exploit their supply chain power to engage in more REM. In contrast to the skeptical stock market reaction when other firms engage in REM, we find no evidence that major customers’ earnings are discounted when there is evidence of REM. Instead, the market appears to interpret major customers’ behavior as “legitimate” uses of power in supply chain management, rather than REM typically considered to be value‐destroying. Further, we find that in post‐REM periods, major customers that engage in REM exhibit better operating cash flow performance than nonmajor customers who do so. These findings suggest that the consequential costs of REM are lower for major customers than for nonmajor customers. Finally, we report evidence that the particular form of major customers’ REM appears to determine the impact on their suppliers. Suppliers’ financial performance deteriorates when major customers’ REM entails discretionary expense cuts. These findings offer new insights into the benefits and uses of power in supply chain relationships, in a previously unexplored context. We discuss the implications of the findings for future research.
Modeling Framework and Implementation of Activity- and Agent-Based Simulation: An Application to the Greater Boston Area:

This paper presents a utility-maximizing approach to agent-based modeling with an application to the Greater Boston Area (GBA). It leverages day activity schedules (DAS) to create a framework for r...
Energy-momentum tensor of a ferromagnet

The energy-momentum tensor of a ferromagnet derived according to the standard prescription of Noether's theorem has a major flaw: the term originating from the spin Berry phase is gauge dependent. As a consequence, some physical quantities computed from the tensor show unphysical behavior. For example, the presence of a spin-polarized current does not affect the energy of the domain wall in the commonly accepted gauge, which implies, incorrectly, the absence of the adiabatic spin torque. In other gauges, the spin torque shows unphysical glitches occurring when the plane of magnetization crosses the Dirac string associated with a magnetic monopole in spin space. We derive a gauge-invariant energy-momentum tensor that is free from these artifacts but requires the addition of an extra spatial dimension, with the ferromagnet living on its boundary. It can be obtained most directly from the Wess-Zumino action for spins, which relies on the same extra dimension.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.
Use of rectal mucosal grafts in substitution urethroplasty: an early series

Background: To evaluate the feasibility of use of rectal mucosal grafts for augmentation urethroplasty. Methods: A series of five patients who underwent rectal mucosal graft urethroplasty for urethral stricture disease were identified. Descriptive statistics were used to describe these patients. Primary endpoints were recurrence of stricture and perioperative morbidity. Results: Five patients underwent rectal mucosal graft augmentation urethroplasty. Four had a history of prior buccal mucosal graft (BMG) urethroplasty and one had a history of head and neck cancer. Rectal mucosa was noted to be thinner and required more tailoring than buccal mucosa. All patients had patent urethras at time of postoperative retrograde urethrogram. A small diverticulum was noted in one patient with no further sequelae. No complications from rectal mucosal graft harvest were noted. All patients with prior buccal grafting subjectively preferred the rectal graft due to fewer side effects. Subjectively, patients with prior buccal grafts preferred the post-operative recovery following rectal mucosal graft urethroplasty. Conclusions: Rectal mucosal graft augmentation urethroplasty is a safe alternative in patients with contraindications to buccal grafting with limited morbidity.
Hydralazine-associated antineutrophil cytoplasmic antibody vasculitis with pulmonary-renal syndrome.

Hydralazine, a vasodilator, is commonly used as an adjunctive treatment for moderate to severe hypertension, heart failure and hypertensive emergencies in pregnancy. Hydralazine-induced lupus was first described in 1953. Clinical presentation ranges from arthralgia, myalgia, petechiae, or rash to single or multiorgan involvement. An occurrence of systemic vasculitis is a rare complication. When presented as the pulmonary–renal syndrome, it could have a rapidly progressive course which can be fatal. Here, we describe a case of hydralazine-associated rapidly progressive glomerulonephritis and pulmonary haemorrhage. We use this case to review the current literature and discuss and highlight the importance of a high degree of clinical acumen, early diagnosis and prompt treatment for better clinical outcomes.
Who does private equity buy? Evidence on the role of private equity from buyouts of divested businesses

Research Summary: We examine the role of nonventure private equity firms in the market for divested businesses, comparing targets bought by such firms to those bought by corporate acquirers. We argue that a combination of vigilant monitoring, high‐powered incentives, patient capital, and business independence makes private equity firms uniquely suited to correcting underinvestment problems in public corporations, and that they will therefore systematically target divested businesses that are outside their parents’ core area, whose rivals invest more in long‐term strategic assets than their parents, and whose parents have weak managerial incentives both overall and at the divisional level. Results from a sample of 1,711 divestments confirm these predictions. Our study contributes to our understanding of private equity ownership, highlighting its advantage as an alternate governance form. Managerial Summary: Private equity firms are often portrayed as destroyers of corporate value, raiding established companies in pursuit of short‐term gain. In contrast, we argue that private equity investors help to revitalize businesses by enabling investments in long‐term strategic resources and capabilities that they are better able to evaluate, monitor, and support than public market investors. Consistent with these arguments, we find that when acquiring businesses divested by public corporations, private equity firms are more likely to buy units outside the parent's core area, those whose peers invest more in R&D than their parents, and those whose parents have weak managerial incentives, especially at the divisional level. Thus, private equity firms systematically target those businesses that may fail to realize their full potential under public ownership.
Does CIO risk appetite matter? Evidence from information security breach incidents

Abstract After a series of recent high-profile information security breach incidents, practitioners have engaged in heated debates about the role of the chief information officer (CIO), particularly his/her role in information security risk management. However, little is known in the academic literature about how a CIO's appetite for risk affects the effectiveness of information security management. We address this gap by examining how a CIO's risk appetite is associated with information security breach incidents. We show that the level of CIO risk aversion is negatively associated with the likelihood of breach incidents. Furthermore, we find that this association is stronger if the company's chief executive officer (CEO) is also risk averse. In additional analyses, we show that the relationship between CIO risk aversion and breach incidents varies depending on breach type and the strategic position of the company and is moderated by the CIO's power.
Subcritical turbulence spreading and avalanche birth

In confined plasmas, a localized fluctuation in a marginal or weakly damped region will propagate and generate an avalanche if it exceeds a threshold. In this letter, a new model for turbulence spreading based on subcritical instability in the turbulence intensity is introduced. We derive a quantitative threshold for spreading from a seed in a stable region, based on a competition between diffusion and nonlinear growth of the turbulence intensity. The model resolves issues with the established Fisher equation model for turbulence spreading, which is supercritical and cannot support the stationary coexistence of multiple turbulence levels. Implications for turbulence spreading are discussed, including the dynamics of ballistic penetration of turbulence into the stable zone. Tests of the theory are suggested.
Craniobot: A computer numerical controlled robot for cranial microsurgeries

Over the last few decades, a plethora of tools has been developed for neuroscientists to interface with the brain. Implementing these tools requires precisely removing sections of the skull to access the brain. These delicate cranial microsurgical procedures need to be performed on the sub-millimeter thick bone without damaging the underlying tissue and therefore, require significant training. Automating some of these procedures would not only enable more precise microsurgical operations, but also facilitate widespread use of advanced neurotechnologies. Here, we introduce the "Craniobot", a cranial microsurgery platform that combines automated skull surface profiling with a computer numerical controlled (CNC) milling machine to perform a variety of cranial microsurgical procedures on mice. The Craniobot utilizes a low-force contact sensor to profile the skull surface and uses this information to perform precise milling operations within minutes. We have used the Craniobot to perform intact skull thinning and open small to large craniotomies over the dorsal cortex.
Identifying Weaknesses in Eddy-Viscosity Models for Predicting Film Cooling via Large-Eddy Simulations

Large-eddy simulations (LES) and Reynolds-averaged Navier–Stokes (RANS) simulations were performed to investigate modeling weaknesses in two popular RANS models, realizable k − ϵ and shear-stress t...
Cardinal Contests

We model and analyze cardinal contests, where a principal running a rank-order tournament has access to an absolute measure of the quality of agents’ submissions in addition to their relative rankings. We show that a mechanism that compares each agent’s output quality against a threshold to decide whether to award her the prize corresponding to her rank is optimal amongst the set of all mixed cardinal-ordinal mechanisms where the jth-ranked submission receives a fraction of the jth prize that is a non-decreasing function of the submission’s quality. Furthermore, the optimal threshold mechanism uses exactly the same threshold for each rank.
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery

Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both network structures and contents. To deal with network structures, most existing works assume a given or enumerable set of meta-paths and then leverage them for the computation of meta-path-based proximities or network embeddings. However, expert knowledge for given meta-paths is not always available, and as the length of considered meta-paths increases, the number of possible paths grows exponentially, which makes the path searching process very costly. On the other hand, while there are often rich contents around network nodes, they have hardly been leveraged to further improve similarity modeling. In this work, to properly model node similarity in content-rich heterogeneous networks, we propose to automatically discover useful paths for pairs of nodes under both structural and content information. To this end, we combine continuous reinforcement learning and deep content embedding into a novel semi-supervised joint learning framework. Specifically, the supervised reinforcement learning component explores useful paths between a small set of example similar pairs of nodes, while the unsupervised deep embedding component captures node contents and enables inductive learning on the whole network. The two components are jointly trained in a closed loop to mutually enhance each other. Extensive experiments on three real-world heterogeneous networks demonstrate the supreme advantages of our algorithm. Code related to this paper is available at: https://github.com/yangji9181/AutoPath.
Proceedings of the First Workshop on PGAS Applications

In an effort to enable extreme-scale computing, system architectures and technologies are undergoing dramatic transformations, involving complex memory hierarchies, hardware heterogeneity, many- and multi-core architectures, and different levels of parallelization. In order to effectively exploit these transformations when producing scalable applications, scientific researchers must increasingly have cross-cutting technical expertise in hardware, software, and algorithm development. Given this landscape, programming models that provide scientific researchers with a more effective approach for developing their parallel applications are of the utmost importance. The partitioned global address space (PGAS) parallel programming model effectively combines the productive data reference semantics of shared memory systems with a strong locality model in which programmers can reason about the mapping of data to distributed memories. This combination of features simplifies programming complexity while enhancing performance by supporting shared namespaces in a way that exposes data locality in support of scalability. Many PGAS models rely on the single-program, multiple-data (SPMD) programming model commonly used for distributed memory programming. Others provide more of a global view of execution in which language concepts are used to map data and control structures to the target nodes. There are many programming languages within the PGAS family, including Fortran 2008, Unified Parallel C (UPC), X10, and Chapel. Such languages provide users with rich syntax for expressing locality-aware parallel computations. By embedding PGAS concepts into a language's type system and execution semantics, compilers can be leveraged to help with error checking and optimization. PGAS models also take the form of meta-languages and libraries, such as Unified Parallel C++ (UPC++), Coarray C++, OpenSHMEM, MPI-3, and Global Arrays. These have the benefit of being integrated with existing languages, simplifying the learning curve for existing programmers. PGAS Applications Workshop (PAW) includes work from case studies of PGAS programming models in the context of real-world applications as a means of better understanding practical applications of PGAS technologies. The work characterizes the scalability and performance, expressiveness and programmability, as well as any downsides or areas for improvement in existing PGAS models.
The one-is-more illusion: Sets of discrete objects appear less extended than equivalent continuous entities in both space and time.

Abstract We distinguish between discrete objects and continuous entities in categorization and language, but might we actually see such stimuli differently? Here we report the one-is-more illusion, wherein ‘objecthood’ changes what we perceive in an unexpected way. Across many variations and tasks, observers perceived a single continuous object (e.g. a rectangle) as longer than an equated set of multiple discrete objects (e.g. two shorter rectangles separated by a gap). This illusion is phenomenologically compelling, exceptionally reliable, and it extends beyond space, to time: a single continuous tone is perceived to last longer than an equated set of multiple discrete tones. Previous work has emphasized the importance of objecthood for processes such as attention and visual working memory, but these results typically require careful analyses of subtle effects. In contrast, we provide striking demonstrations of how perceived objecthood changes the perception of other properties in a way that you can readily see (and hear!) with your own eyes (and ears!).
Usefulness and Usability of a Personal Health Record and Survivorship Care Plan for Colorectal Cancer Survivors: Survey Study.

Background As a result of improvements in cancer screening, treatment, and supportive care, nearly two-thirds of individuals diagnosed with colorectal cancer (CRC) live for 5 years after diagnosis. An ever-increasing population of CRC survivors creates a need for effective survivorship care to help manage and mitigate the impact of CRC and its treatment. Personal health records (PHRs) and survivorship care plans provide a means of supporting the long-term care of cancer survivors. Objective The purpose of this study is to characterize the usefulness of a CRC PHR and survivorship care plan and to describe the usability of these technologies in a population of CRC survivors. To our knowledge, this is the first study to assess a PHR and survivorship care plan specifically targeting CRC survivors. Methods Twenty-two patients with CRC were recruited from surgery clinics of an academic medical center and Veterans Affairs hospital in Indianapolis and provided access to an online Colorectal Cancer Survivor's Personal Health Record (CRCS-PHR). Survey data were collected to characterize the usefulness of the CRCS-PHR and describe its usability in a population of CRC survivors. CRC survivors were surveyed 6 months after being provided online access. Means and proportions were used to describe the usefulness and ease of using the CRC website. Open-ended questions were qualitatively coded using the constant comparative method. Results CRC survivors perceived features related to their health care (ie, summary of cancer treatment history, follow-up care schedule, description of side effects, and list of community resources) to be more useful than communication features (ie, creating online relationships with family members or caregivers, communicating with doctor, and secure messages). CRC survivors typically described utilizing traditional channels (eg, via telephone or in person) to communicate with their health care provider. Participants had overall positive perceptions with respect to ease of use and overall satisfaction. Major challenges experienced by participants included barriers to system log-in, lack of computer literacy or experience, and difficulty entering their patient information. Conclusions For CRC, survivors may find the greater value in a PHR's medical content than the communication functions, which they have available elsewhere. These findings regarding the usefulness and usability of a PHR for the management of CRC survivorship provide valuable insights into how best to tailor these technologies to patients' needs. These findings can inform future design and development of PHRs for purposes of both cancer and chronic disease management.
Robust incentives for information acquisition

Abstract A principal needs to make a decision, and contracts with an expert, who can obtain information relevant to the decision by exerting costly effort. The principal can incentivize effort by paying a reward based on the expert's reported information and on the true state of nature, which is revealed ex post. Both parties are financially risk-neutral, and payments are constrained by limited liability. The principal is uncertain about the expert's information acquisition technology: she knows some actions (experiments) that he can take to obtain information, but there may also be other experiments available. The principal seeks robustness to this uncertainty, and so evaluates any incentive contract using a worst-case criterion. Under quite general conditions, we show that the optimal contract is a restricted-investment contract, in which the expert chooses from a subset of the decisions available to the principal, and is then rewarded proportionally to the value of his designated decision in the realized state.
A spatial valence model of political participation in China

In spatial models of political competition in democracies, citizens vote for the party or candidate that is the closest to their own ideological position, while in valence models, voters decide on ...
Pangolin: A Fault-Tolerant Persistent Memory Programming Library

Non-volatile main memory (NVMM) allows programmers to build complex, persistent, pointer-based data structures that can offer substantial performance gains over conventional approaches to managing persistent state. This programming model removes the file system from the critical path which improves performance, but it also places these data structures out of reach of file system-based fault tolerance mechanisms (e.g., block-based checksums or erasure coding). Without fault-tolerance, using NVMM to hold critical data will be much less attractive. This paper presents Pangolin, a fault-tolerant persistent object library designed for NVMM. Pangolin uses a combination of checksums, parity, and micro-buffering to protect an application's objects from both media errors and corruption due to software bugs. It provides these protections for objects of any size and supports automatic, online detection of data corruption and recovery. The required storage overhead is small (1% for gigabyte-sized pools of NVMM). Pangolin provides stronger protection, requires orders of magnitude less storage overhead, and achieves comparable performance relative to the current state-of-the-art fault-tolerant persistent object library.
Thinking about decisions: An integrative approach of person and task factors

Decisions vary. They vary in both content and complexity. People also vary. An important way that people vary is how much they think about a decision. Some prior research investigating thinking and decision making largely conflicts with most traditional decision theories. For example, if considering an array of products to choose from, thinking more about the different alternative’s attributes should lead to a better decision. However, some research indicates that it may also lead to more focus on irrelevant aspects of the decision situation. We propose that this conflict exists because of a failure to consider the interaction between the individual and the decision task. To test this, we used separate methodologies that enhance or attenuate a person’s thinking. In Study 1 we selected people who were especially high or low in need for cognition (Cacioppo & Petty, 1982) and had them complete a robust decision-making inventory, which included both complex and simple tasks. In Study 2 we manipulated participant’s level of glucose, which acts as the brain’s fuel to enhance or attenuate thinking ability. Both studies support the view that more thought leads to better decisions in complex tasks but does not influence simple decisions, including those that are valence based. These findings show how the individual’s thinking interacts with the constructive elements of the task to shape decision choice. Key Words: Decision making, Thinking, A-DMC, Complex decisions, Need-forcognition, glucose
Competition, scale and hedge fund performance: Evidence from merger arbitrage

Abstract The study explores the impact of sector and individual fund’s size on performance. We demonstrate that alpha is related to demand for arbitrage capital as much as to its supply. Sector size affects alpha via price impact channel: larger scale depresses merger arbitrage spreads, thus lowering alpha. While we find that fund size has no impact on alpha, managerial skill has an economically significant impact of funds’ performance.
The Impact of Legalized Abortion on Crime over the Last Two Decades

Donohue and Levitt (2001) presented evidence that the legalization of abortion in the early 1970s played an important role in the crime drop of the 1990s. That paper concluded with a strong out-of-sample prediction regarding the next two decades: “When a steady state is reached roughly twenty years from now, the impact of abortion will be roughly twice as great as the impact felt so far. Our results suggest that all else equal, legalized abortion will account for persistent declines of 1% a year in crime over the next two decades.” Estimating parallel specifications to the original paper, but using the seventeen years of data generated after that paper was written, we find strong support for the prediction and the broad hypothesis, while illuminating some previously unrecognized patterns of crime and arrests. We estimate that overall crime fell 17.5% from 1998 to 2014 due to legalized abortion—a decline of 1% per year. From 1991 to 2014, the violent and property crime rates each fell by 50%. Legalized abortion is estimated to have reduced violent crime by 47% and property crime by 33% over this period, and thus can explain most of the observed crime decline.
Comparison of cardiovascular parameter estimation methods using swine data

In this study, new and existing methods of estimating stroke volume, cardiac output and total peripheral resistance from analysis of the arterial blood pressure waveform were tested over a wide range of conditions. These pulse contour analysis methods (PCMs) were applied to data obtained in six swine during infusion of volume, phenylephrine, dobutamine, isoproterenol, esmolol and nitroglycerine as well as during progressive hemorrhage. Performance of PCMs was compared using true end-ejection pressures as well as estimated end-ejection pressures. There was considerable overlap in the accuracies of the PCMs when using true end-ejection measures. However, for perhaps the most clinically relevant condition, where radial artery pressure is the input, only Wesseling’s Corrected Impedance method and the Kouchoukos Correction method achieved statistically superior results. We introduced a method of estimating end-ejection by determining when the systolic pressure dropped to a value equal to the sum of the end-diastolic pressure plus a fraction of the pulse pressure. The most accurate estimation of end-ejection was obtained when that fraction was set to 60% for the central arterial pressure and to 50% for the femoral and radial arterial pressures. When the estimated end-ejection measures were used for the PCMs that depend on end-ejection measures and when radial artery pressure was used as the input, only Wesseling’s Corrected Impedance method and the modified Herd’s method achieved statistically superior results. This study provides a systematic comparison of multiple PCMs’ ability to estimate stroke volume, cardiac output, and total peripheral resistance and introduces a new method of estimating end-systole.
Distributed estimation of the inverse Hessian by determinantal averaging

In distributed optimization and distributed numerical linear algebra, we often encounter an inversion bias: if we want to compute a quantity that depends on the inverse of a sum of distributed matrices, then the sum of the inverses does not equal the inverse of the sum. An example of this occurs in distributed Newton's method, where we wish to compute (or implicitly work with) the inverse Hessian multiplied by the gradient. In this case, locally computed estimates are biased, and so taking a uniform average will not recover the correct solution. To address this, we propose determinantal averaging, a new approach for correcting the inversion bias. This approach involves reweighting the local estimates of the Newton's step proportionally to the determinant of the local Hessian estimate, and then averaging them together to obtain an improved global estimate. This method provides the first known distributed Newton step that is asymptotically consistent, i.e., it recovers the exact step in the limit as the number of distributed partitions grows to infinity. To show this, we develop new expectation identities and moment bounds for the determinant and adjugate of a random matrix. Determinantal averaging can be applied not only to Newton's method, but to computing any quantity that is a linear tranformation of a matrix inverse, e.g., taking a trace of the inverse covariance matrix, which is used in data uncertainty quantification.
Strawberry: Fast and accurate genome-guided transcript reconstruction and quantification from RNA-Seq.

We propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression. Availability: Strawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.