bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning semantic program embeddings with graph interval neural network
Authors: Wang Yu , Wang Ke , Gao Fengjuan , Wang Linzhang 
Published: November 2020

Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, such approaches do not capitalize on the rich structural information possessed by source code. Of late, Graph Neural Network (GNN) was proposed to learn embeddings of programs from their graph representations. Due to the homogeneous (i.e. do not take advantage of the program-specific graph characteristics) and expensive (i.e. require heavy information exchange among nodes in the graph) message-passing procedure, GNN can suffer from precision issues, especially when dealing with programs rendered into large graphs. In this paper, we present a new graph neural architecture, called Graph Interval Neural Network (GINN), to tackle the weaknesses of the existing GNN. Unlike the standard GNN, GINN generalizes from a curated graph representation obtained through an abstraction method designed to aid models to learn. In particular, GINN focuses exclusively on intervals (generally manifested in looping construct) for mining the feature representation of a program, furthermore, GINN operates on a hierarchy of intervals for scaling the learning to large graphs.
We evaluate GINN for two popular downstream applications: variable misuse prediction and method name prediction. Results show in both cases GINN outperforms the state-of-the-art models by a comfortable margin. We have also created a neural bug detector based on GINN to catch null pointer deference bugs in Java code. While learning from the same 9,000 methods extracted from 64 projects, GINN-based bug detector significantly outperforms GNN-based bug detector on 13 unseen test projects. Next, we deploy our trained GINN-based bug detector and Facebook Infer, arguably the state-of-the-art static analysis tool, to scan the codebase of 20 highly starred projects on GitHub. Through our manual inspection, we confirm 38 bugs out of 102 warnings raised by GINN-based bug detector compared to 34 bugs out of 129 warnings for Facebook Infer. We have reported 38 bugs GINN caught to developers, among which 11 have been fixed and 12 have been confirmed (fix pending). GINN has shown to be a general, powerful deep neural network for learning precise, semantic program embeddings.
Scaling exact inference for discrete probabilistic programs
Authors: Holtzen Steven , Van den Broeck Guy , Millstein Todd 
Published: November 2020

Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge ofprobabilistic inferenceremains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no one-size-fits all solution. In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions arediscrete. Discrete distributions are common in many fields, including text analysis, network verification, artificial intelligence, and graph analysis, but they prove to be challenging for existing PPLs.We develop a domain-specific probabilistic programming language called Dice that features a new approach to exact discrete probabilistic program inference. Dice exploits program structure in order to factorize inference, enabling us to perform exact inference on probabilistic programs with hundreds of thousands of random variables. Our key technical contribution is a new reduction from discrete probabilistic programs to weighted model counting (WMC). This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference. We (1) show how to compositionally reduce Dice inference to WMC, (2) prove this compilation correct with respect to a denotational semantics, (3) empirically demonstrate the performance benefits over prior approaches, and (4) analyze the types of structure that allow Dice to scale to large probabilistic programs.
A sparse iteration space transformation framework for sparse tensor algebra
Authors: Senanayake Ryan , Hong Changwan , Wang Ziheng , Wilson Amalee , Chou Stephen , Kamil Shoaib , Amarasinghe Saman , Kjolstad Fredrik 
Published: November 2020

We address the problem of optimizing sparse tensor algebra in a compiler and show how to define standard loop transformations---split, collapse, and reorder---on sparse iteration spaces. The key idea is to track the transformation functions that map the original iteration space to derived iteration spaces. These functions are needed by the code generator to emit code that maps coordinates between iteration spaces at runtime, since the coordinates in the sparse data structures remain in the original iteration space. We further demonstrate that derived iteration spaces can tile both the universe of coordinates and the subset of nonzero coordinates: the former is analogous to tiling dense iteration spaces, while the latter tiles sparse iteration spaces into statically load-balanced blocks of nonzeros. Tiling the space of nonzeros lets the generated code efficiently exploit heterogeneous compute resources such as threads, vector units, and GPUs.
We implement these concepts by extending the sparse iteration theory implementation in the TACO system. The associated scheduling API can be used by performance engineers or it can be the target of an automatic scheduling system. We outline one heuristic autoscheduling system, but other systems are possible. Using the scheduling API, we show how to optimize mixed sparse-dense tensor algebra expressions on CPUs and GPUs. Our results show that the sparse transformations are sufficient to generate code with competitive performance to hand-optimized implementations from the literature, while generalizing to all of the tensor algebra.
Eliminating abstraction overhead of Java stream pipelines using ahead-of-time program optimization
Authors: Møller Anders , Veileborg Oskar Haarklou 
Published: November 2020

Java 8 introduced streams that allow developers to work with collections of data using functional-style operations. Streams are often used in pipelines of operations for processing the data elements, which leads to concise and elegant program code. However, the declarative data processing style comes at a cost. Compared to processing the data with traditional imperative language mechanisms, constructing stream pipelines requires extra heap objects and virtual method calls, which often results in significant run-time overheads.
In this work we investigate how to mitigate these overheads to enable processing data in the declarative style without sacrificing performance. We argue that ahead-of-time bytecode-to-bytecode transformation is a suitable approach to optimization of stream pipelines, and we present a static analysis that is designed to guide such transformations. Experimental results show a significant performance gain, and that the technique works for realistic stream pipelines. For 10 of 11 micro-benchmarks, the optimizer is able to produce bytecode that is as effective as hand-written imperative-style code. Additionally, 77% of 6879 stream pipelines found in real-world Java programs are optimized successfully.
FlowCFL: generalized type-based reachability analysis: graph reduction and equivalence of CFL-based and type-based reachability
Authors: Milanova Ana 
Published: November 2020

Reachability analysis is a fundamental program analysis with a wide variety of applications. We present FlowCFL, a type-based reachability analysis that accounts for mutable heap data. The underlying semantics of FlowCFL is Context-Free-Language (CFL)-reachability.
We make three contributions. First, we define a dynamic semantics that captures the notion of flow commonly used in reachability analysis. Second, we establish correctness of CFL-reachability over graphs with inverse edges (inverse edges are necessary for the handling of mutable heap data). Our approach combines CFL-reachability with reference immutability to avoid the addition of certain inverse edges, which results in graph reduction and precision improvement. The key contribution of our work is the formal account of correctness, which extends to the case when inverse edges are removed. Third, we present a type-based reachability analysis and establish equivalence between a certain CFL-reachability analysis and the type-based analysis, thus proving correctness of the type-based analysis.
Taming type annotations in gradual typing
Authors: Campora John Peter , Chen Sheng 
Published: November 2020

Gradual typing provides a methodology to integrate static and dynamic typing, harmonizing their often conflicting advantages in a single language. When a user wants to enjoy the advantages of static typing, most gradual languages require that they add type annotations. Many nontrivial tasks must be undertaken while adding type annotations, including understanding program behaviors and invariants. Unfortunately, if this is done incorrectly then the added type annotations can be wrong–leading to inconsistencies between the program and the type annotations. Gradual typing implementations detect such inconsistencies at runtime, raise cast errors, and generate messages. However, solely relying on such error messages for understanding and fixing inconsistencies and their resulting cast errors is often insufficient for multiple reasons. One reason is that while such messages cover inconsistencies in one execution path, fixing them often requires reconciling information from multiple paths. Another is that users may add many wrong type annotations that they later find difficult to identify and fix, when considering all added annotations.
Recent studies provide evidence that type annotations added during program migration are often wrong and that many programmers prefer compile-time warnings about wrong annotations. Motivated by these results, we develop exploratory typing to help with the static detection, understanding, and fixing of inconsistencies. The key idea of exploratory typing is that it systematically removes dynamic types and explores alternative types for static type annotations that can remedy inconsistencies. To demonstrate the feasibility of exploratory typing, we have implemented it in PyHound, which targets programs written in Reticulated Python, a gradual variant of Python. We have evaluated PyHound on a set of Python programs, and the evaluation results demonstrate that our idea can effectively detect inconsistencies in 98% of the tested programs and fix 93% of inconsistencies, significantly outperforming pytype, a widely used Python tool for enforcing type annotations.
Adding interactive visual syntax to textual code
Authors: Andersen Leif , Ballantyne Michael , Felleisen Matthias 
Published: November 2020

Many programming problems call for turning geometrical thoughts into code: tables, hierarchical structures, nests of objects, trees, forests, graphs, and so on. Linear text does not do justice to such thoughts. But, it has been the dominant programming medium for the past and will remain so for the foreseeable future.
This paper proposes a novel mechanism for conveniently extending textual programming languages with problem-specific visual syntax. It argues the necessity of this language feature, demonstrates the feasibility with a robust prototype, and sketches a design plan for adapting the idea to other languages.
Perspectives on Policing: Phillip Atiba Goff
Authors: Goff Phillip Atiba 
Published: November 2020

The Editorial Committee of the Annual Review of Criminology has launched a new section within the journal in which we invite one or more authors to write a more personal and yet scholarly article that puts forth their perspective on an important and timely topic. For Volume 4, we asked Dr. Phillip Atiba Goff and Dr. Cynthia Lum to provide their perspectives on policing. We hope our readers enjoy this new and exciting series of “Perspectives.”
Torsion of an Epididymal Cyst: A Rare Finding on Scrotal Exploration for Acute Scrotum
Authors: Ozaal A. M. Omar Mohamed , Pragalathan B. , Lavanya S. , Sarma S. T. 
Published: November 2020

Torsion of epididymal cyst (EC) is an exceedingly rare cause of acute scrotum in both children and adults. We add our case as the ninth case to literature which was an 8-year-old child presented with features of acute scrotum with history of EC on conservative management. Doppler sonography showed perfused normal bilateral testes and a

4.1
×
1.7

cm

septate cystic lesion of right epididymis. On scrotal exploration, we found a haemorrhagic cystic lesion attached to the upper pole of right testis and twisted for 540 degrees with normal testis and appendage. Cyst was excised, and histopathology revealed a haemorrhagic EC. Our case was peculiar due to, presenting as acute scrotum in a child of 1-10 years age group who was conservatively managed for right-sided EC and presence of 540 degrees torsion.
T-cell Acute Lymphoblastic Leukemia: A Roadmap to Targeted Therapies
Authors: Cordo' Valentina , van der Zwet Jordy C.G. , Canté-Barrett Kirsten , Pieters Rob , Meijerink Jules P.P. 
Published: November 2020

AbstractT-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy characterized by aberrant proliferation of immature thymocytes. Despite an overall survival of 80% in the pediatric setting, 20% of patients with T-ALL ultimately die from relapsed or refractory disease. Therefore, there is an urgent need for novel therapies. Molecular genetic analyses and sequencing studies have led to the identification of recurrent T-ALL genetic drivers. This review summarizes the main genetic drivers and targetable lesions of T-ALL and gives a comprehensive overview of the novel treatments for patients with T-ALL that are currently under clinical investigation or that are emerging from preclinical research.Significance:T-ALL is driven by oncogenic transcription factors that act along with secondary acquired mutations. These lesions, together with active signaling pathways, may be targeted by therapeutic agents. Bridging research and clinical practice can accelerate the testing of novel treatments in clinical trials, offering an opportunity for patients with poor outcome.
Towards the Therapeutic Use of Thrombospondin 1/CD47 Targeting TAX2 Peptide as an Antithrombotic Agent
Authors: Jeanne Albin , Sarazin Thomas , Charlé Magalie , Kawecki Charlotte , Kauskot Alexandre , Hedtke Tobias , Schmelzer Christian E.H. , Martiny Laurent , Maurice Pascal , Dedieu Stéphane 
Published: November 2020

Objective:
TSP-1 (thrombospondin 1) is one of the most expressed proteins in platelet α-granules and plays an important role in the regulation of hemostasis and thrombosis. Interaction of released TSP-1 with CD47 membrane receptor has been shown to regulate major events leading to thrombus formation, such as, platelet adhesion to vascular endothelium, nitric oxide/cGMP (cyclic guanosine monophosphate) signaling, platelet activation as well as aggregation. Therefore, targeting TSP-1:CD47 axis may represent a promising antithrombotic strategy.

Approach and Results:

A CD47-derived cyclic peptide was engineered, namely TAX2, that targets TSP-1 and selectively prevents TSP-1:CD47 interaction. Here, we demonstrate for the first time that TAX2 peptide strongly decreases platelet aggregation and interaction with collagen under arterial shear conditions. TAX2 also delays time for complete thrombotic occlusion in 2 mouse models of arterial thrombosis following chemical injury, while
Thbs1
−/−
mice recapitulate TAX2 effects. Importantly, TAX2 administration is not associated with increased bleeding risk or modification of hematologic parameters.

Conclusions:
Overall, this study sheds light on the major contribution of TSP-1:CD47 interaction in platelet activation and thrombus formation while putting forward TAX2 as an innovative antithrombotic agent with high added-value.
Association of Sedentary Time and Incident Heart Failure Hospitalization in Postmenopausal Women
Authors: LaMonte Michael J. , Larson Joseph C. , Manson JoAnn E. , Bellettiere John , Lewis Cora E. , LaCroix Andrea Z. , Bea Jennifer W. , Johnson Karen C. , Klein Liviu , Noel Corinna A. , Stefanick Marcia L. , Wactawski-Wende Jean , Eaton Charles B. 
Published: November 2020

Background:
The 2018 US Physical Activity Guidelines recommend reducing sedentary behavior (SB) for cardiovascular health. SB’s role in heart failure (HF) is unclear.

Methods:
We studied 80 982 women in the Women’s Health Initiative Observational Study, aged 50 to 79 years, who were without known HF and reported ability to walk ≥1 block unassisted at baseline. Mean follow-up was 9 years for physician-adjudicated incident HF hospitalization (1402 cases). SB was assessed repeatedly by questionnaire. Time-varying total SB was categorized according to awake time spent sitting or lying down (≤6.5, 6.6–9.5, >9.5 h/d); sitting time (≤4.5, 4.6–8.5, >8.5 h/d) was also evaluated. Hazard ratios and 95% CI were estimated using Cox regression.

Results:

Controlling for age, race/ethnicity, education, income, smoking, alcohol, menopausal hormone therapy, and hysterectomy status, higher HF risk was observed across incremental tertiles of time-varying total SB (hazard ratios [95% CI], 1.00 [referent], 1.15 [1.01–1.31], 1.42 [1.25–1.61], trend
P
<0.001) and sitting time (1.00 [referent], 1.14 [1.01–1.28], 1.54 [1.34–1.78], trend
P
<0.001). The inverse trends remained significant after further controlling for comorbidities including time-varying myocardial infarction and coronary revascularization (hazard ratios: SB, 1.00, 1.11, 1.27; sitting, 1.00, 1.09, 1.37, trend
P
<0.001 each) and for baseline physical activity (hazard ratios: SB 1.00, 1.10, 1.24; sitting 1.00, 1.08, 1.33, trend
P
<0.001 each). Associations with SB exposures were not different according to categories of baseline age, race/ethnicity, body mass index, physical activity, physical functioning, diabetes, hypertension, or coronary heart disease.

Conclusions:
SB was associated with increased risk of incident HF hospitalization in postmenopausal women. Targeted efforts to reduce SB could enhance HF prevention in later life.
Inhibition of Cholesteryl Ester Transfer Protein Preserves High-Density Lipoprotein Cholesterol and Improves Survival in Sepsis
Authors: Trinder Mark , Wang Yanan , Madsen Christian M. , Ponomarev Tatjana , Bohunek Lubos , Daisely Brendan A. , Julia Kong HyeJin , Blauw Lisanne L. , Nordestgaard Børge G. , Tybjærg-Hansen Anne , Wurfel Mark M. , Russell James A. , Walley Keith R. , Rensen Patrick C.N. , Boyd John H. , Brunham Liam R. 
Published: November 2020

Background:
The high-density lipoprotein hypothesis of atherosclerosis has been challenged by clinical trials of cholesteryl ester transfer protein (CETP) inhibitors, which failed to show significant reductions in cardiovascular events. Plasma levels of high-density lipoprotein cholesterol (HDL-C) decline drastically during sepsis, and this phenomenon is explained, in part, by the activity of CETP, a major determinant of plasma HDL-C levels. We tested the hypothesis that genetic or pharmacological inhibition of CETP would preserve high-density lipoprotein levels and decrease mortality in clinical cohorts and animal models of sepsis.

Methods:

We examined the effect of a gain-of-function variant in
CETP
(rs1800777, p.Arg468Gln) and a genetic score for decreased
CETP
function on 28-day sepsis survival using Cox proportional hazard models adjusted for age and sex in the UK Biobank (n=5949), iSPAAR (Identification of SNPs Predisposing to Altered Acute Lung Injury Risk; n=882), Copenhagen General Population Study (n=2068), Copenhagen City Heart Study (n=493), Early Infection (n=200), St Paul’s Intensive Care Unit 2 (n=203), and Vasopressin Versus Norepinephrine Infusion in Patients With Septic Shock studies (n=632). We then studied the effect of the CETP inhibitor, anacetrapib, in adult female APOE*3-Leiden mice with or without human CETP expression using the cecal-ligation and puncture model of sepsis.

Results:

A fixed-effect meta-analysis of all 7 cohorts found that the
CETP
gain-of-function variant was significantly associated with increased risk of acute sepsis mortality (hazard ratio, 1.44 [95% CI, 1.22–1.70];
P
<0.0001). In addition, a genetic score for decreased CETP function was associated with significantly decreased sepsis mortality in the UK Biobank (hazard ratio, 0.77 [95% CI, 0.59–1.00] per 1 mmol/L increase in HDL-C) and iSPAAR cohorts (hazard ratio, 0.60 [95% CI, 0.37–0.98] per 1 mmol/L increase in HDL-C). APOE*3-Leiden.CETP mice treated with anacetrapib had preserved levels of HDL-C and apolipoprotein-AI and increased survival relative to placebo treatment (70.6% versus 35.3%, Log-rank
P
=0.03), whereas there was no effect of anacetrapib on the survival of APOE*3-Leiden mice that did not express
CETP
(50.0% versus 42.9%, Log-rank
P
=0.87).

Conclusions:
Clinical genetics and humanized mouse models suggest that inhibiting CETP may preserve high-density lipoprotein levels and improve outcomes for individuals with sepsis.
Coronavirus Disease 2019–Associated Thrombosis and Coagulopathy: Review of the Pathophysiological Characteristics and Implications for Antithrombotic Management
Authors: Ortega‐Paz Luis , Capodanno Davide , Montalescot Gilles , Angiolillo Dominick J. 
Published: November 2020

Abstract
Coronavirus disease 2019 (COVID‐19) is an infectious disease caused by severe acute respiratory syndrome coronavirus‐2, which has posed a significant threat to global health. Although the infection is frequently asymptomatic or associated with mild symptoms, in a small proportion of patients it can produce an intense inflammatory and prothrombotic state that can lead to acute respiratory distress syndrome, multiple organ failure, and death. Angiotensin‐converting enzyme 2, highly expressed in the respiratory system, has been identified as a functional receptor for severe acute respiratory syndrome coronavirus‐2. Notably, angiotensin‐converting enzyme 2 is also expressed in the cardiovascular system, and there are multiple cardiovascular implications of COVID‐19. Cardiovascular risk factors and cardiovascular disease have been associated with severe manifestations and poor prognosis in patients with COVID‐19. More important, patients with COVID‐19 may have thrombotic and coagulation abnormalities, promoting a hypercoagulable state and resulting in an increased rate of thrombotic and thromboembolic events. This review will describe the pathophysiological characteristics of the cardiovascular involvement following infection by severe acute respiratory syndrome coronavirus‐2, with a focus on thrombotic and thromboembolic manifestations and implications for antithrombotic management.