We introduce and study the problem of detecting short races in an observed trace. Specifically, for a race type $R$, given a trace $σ$ and window size $w$, the task is to determine whether there exists an $R$-race $(e_1, e_2)$ in $σ$ such that the subtrace starting with $e_1$ and ending with $e_2$ contains at most $w$ events. We present a monitoring framework for short-race prediction and instantiate the framework for happens-before and sync-preserving races, yielding efficient detection algorithms. Our happens-before algorithm runs in the same time as FastTrack but uses space that scales with $\log w$ as opposed to $\log |σ|$. For sync-preserving races, our algorithm runs faster and consumes significantly less space than SyncP. Our experiments validate the effectiveness of these short-race detection algorithms: they run more efficiently, use less memory, and detect significantly more races under the same budget, offering a reasonable balance between resource usage and predictive power.
Data races in GPU programs pose a threat to the reliability of GPU-accelerated software stacks. Prior works proposed various dynamic (runtime) and static (compile-time) techniques to detect races in GPU programs. However, dynamic techniques often miss critical races, as they require the races to manifest during testing. While static ones can catch such races, they often generate numerous false alarms by conservatively assuming values of variables/parameters that cannot ever occur during any execution of the program. We make a key observation that the host (CPU) code that launches GPU kernels contains crucial semantic information about the values that the GPU kernel's parameters can take during execution. Harnessing this hitherto overlooked information helps accurately detect data races in GPU kernel code. We create HGRD, a new state-of-the-art static analysis technique that performs a holistic analysis of both CPU and GPU code to accurately detect a broad set of true races while minimizing false alarms. While SOTA dynamic techniques, such as iGUARD, miss many true races, HGRD misses none. On the other hand, static techniques such as GPUVerify and FaialAA raise tens of false alarms, where HGRD raises none.
This article focuses on the challenges that uncertainty poses to countries in global and regional value chains. In this perspective, it focuses specifically on African countries and enriches the results with a comparative approach. Indeed, using a gravity model for 49 African countries and all their trading partners from 1990 to 2019, the paper proposes a comparative analysis of the effects of uncertainty on global trade in value chains and on trade in value chains within Africa. The robustness of the results shows that domestic uncertainty can drive the expansion of intra-African trade in value chains, while uncertainty in the partner country hinders the flourishing of trade relationships within a value chain.
Cities. Urban geography, Urbanization. City and country
Farnaz Behrang, Zhizhou Zhang, Georgian-Vlad Saioc
et al.
Data races are a prevalent class of concurrency bugs in shared-memory parallel programs, posing significant challenges to software reliability and reproducibility. While there is an extensive body of research on detecting data races and a wealth of practical detection tools across various programming languages, considerably less effort has been directed toward automatically fixing data races at an industrial scale. In large codebases, data races are continuously introduced and exhibit myriad patterns, making automated fixing particularly challenging. In this paper, we tackle the problem of automatically fixing data races at an industrial scale. We present Dr.Fix, a tool that combines large language models (LLMs) with program analysis to generate fixes for data races in real-world settings, effectively addressing a broad spectrum of racy patterns in complex code contexts. Implemented for Go--the programming language widely used in modern microservice architectures where concurrency is pervasive and data races are common--Dr.Fix seamlessly integrates into existing development workflows. We detail the design of Dr.Fix and examine how individual design choices influence the quality of the fixes produced. Over the past 18 months, Dr.Fix has been integrated into developer workflows at Uber demonstrating its practical utility. During this period, Dr.Fix produced patches for 224 (55%) from a corpus of 404 data races spanning various categories; 193 of these patches (86%) were accepted by more than a hundred developers via code reviews and integrated into the codebase.
Junhyung Shim, Quazi Ishtiaque Mahmud, Ali Jannesari
Detection of data races is one of the most important tasks for verifying the correctness of OpenMP parallel codes. Two main models of analysis tools have been proposed for detecting data races: dynamic analysis and static analysis. Dynamic analysis tools such as Intel Inspector, ThreadSanitizer, and Helgrind+ can detect data races through the execution of the source code. However, source code execution can be quite time-consuming when analyzing computation-intensive programs. There are also static analysis tools such as LLOV, and OpenRace. These tools statically detect data races using algorithms that often do not require the execution of the source code. Although both detection techniques assist programmers in analyzing the correct behavior of OpenMP programs, they still produce false positives that often defeat the purpose of applying automatic analysis. Therefore, we present DRS-oNE (Data Race Satisfiability on aNy Element), a data race detector that detects data races on array elements by solving for race constraints with the Z3 SMT solver.
Abstract The intense growth of Brazilian cities is remarkable, characterized by peripheralization and socio-spatial inequalities. However, studies that focus on the temporal dimension to understand spatial segregation are still scarce. This paper aims to understand the role played by commuting time in explaining the process of spatial segregation in the São Paulo metropolis. Its methodology is based on statistical data obtained from the weighting areas of the Demographic Census sample, by means of the variable usual commuting time, combined with other socioeconomic variables of income and race. This research contributes to the understanding of spatio-temporal segregation and shows that commuting time unites the poorest and black individuals, separating them from the richest and white individuals in the São Paulo metropolis.
Maria Boulos, Michaela Hynie, Shauna Spirling
et al.
This study explored changes in shared decision-making roles (day-to-day, financial, and major life decisions) and their relationships to perceived stress among 148 Syrian refugee parents after resettling in Toronto using a generalized estimated equation model. Parents were categorized as “towards shared” decision-making for 20.3%, 23.0%, and 21.6% of day-to-day, major life, and financial decisions, respectively. In families where both parents were unemployed, those who “always shared” making financial decisions had significantly lower perceived stress than those “towards shared” (p = .02). Understanding the cultural contexts of gender roles and the impact of acculturation may help promote better post-migration strategies.
Data races are critical issues in multithreaded program, leading to unpredictable, catastrophic and difficult-to-diagnose problems. Despite the extensive in-house testing, data races often escape to deployed software and manifest in production runs. Existing approaches suffer from either prohibitively high runtime overhead or incomplete detection capability. In this paper, we introduce HardRace, a data race monitor to detect races on-the-fly while with sufficiently low runtime overhead and high detection capability. HardRace firstly employs sound static analysis to determine a minimal set of essential memory accesses relevant to data races. It then leverages hardware trace instruction, i.e., Intel PTWRITE, to selectively record only these memory accesses and thread synchronization events during execution with negligible runtime overhead. Given the tracing data, HardRace performs standard data race detection algorithms to timely report potential races occurred in production runs. The experimental evaluations show that HardRace outperforms state-of-the-art tools like ProRace and Kard in terms of both runtime overhead and detection capability -- HardRace can detect all kinds of data races in read-world applications while maintaining a negligible overhead, less than 2% on average.
Resumo É marcante o intenso crescimento das cidades brasileiras caracterizado pela periferização e pelas desigualdades socioespaciais. Porém, ainda são escassas pesquisas que enfoquem a dimensão temporal quanto à compreensão da segregação espacial. Este artigo visa compreender o papel do tempo de deslocamento na explicação do processo de segregação espacial na metrópole de São Paulo. Para tanto, sua metodologia baseia-se em dados estatísticos das áreas de ponderação da amostra do Censo Demográfico, através da variável tempo de deslocamento habitual para o trabalho, combinada com outras variáveis socioeconômicas, de renda e raça. Busca-se contribuir para o entendimento da segregação espaço-temporal, demonstrando que o tempo de deslocamento une os mais pobres e os negros, separando-os dos mais ricos e dos brancos na referida metrópole.
Abstract Educators can use community engaged learning (CEL), also known as service learning, to empower all ages, bridging both college students and older adults needs. With the wrong approach, service just attached to a course may reinforce negative stereotypes. To assure best practice, educators benefit from CEL competencies to promote positive learning and avoid reinforcing ageism. Competencies, based on a community-engaged taxonomy, include: Reciprocal partnerships, Diversity of interactions and dialogue, Community activities, Civic competencies, Critical reflection, and Assessment (Rathlef, 2022; Kecskes, 2015). A complete explanation of each competency will be explained and illustrated. Using the competencies as a framework, two specific programs with college students and older adults were evaluated using both formative and summative measures. One program, a virtual intergenerational interaction, lasted seven weeks. The second program, an in-person program at the University’s equestrian and nature center, spanned five weeks. Pre and post comparisons of civic learning outcomes using CLEO, written reflections, focus group discussion, and partner perspectives were analyzed. Older adults reported some improvement in social interaction and companionship with some expansion in social networks. Students significantly improved civic knowledge outcomes. The students’ written reflections revealed a self-reported change in attitude toward older adults. Students also expressed a shift in their perceptions of future work in a health setting with older adults. Community-engaged learning can empower all ages, but an educator’s attention to the competencies supporting best practice can make a significant difference to the outcome of these efforts.
Mirtha Graciela Villagra-Ferreira, María Lourdes Falcó-de Ayala, Patricia Johanna Cabrera
The objective of this study was to measure the perception of students in the last year of the Economics and Business careers of the Universidad del Norte, about the activities developed as university extension, based on a survey applied randomly to the students of the Central, Itauguá and Caacupé headquarters, during the first semester of the 2022 school year. The work corresponds to an investigation framed in the quantitative paradigm, of a descriptive, non-experimental and cross-sectional type. In this context, it could be verified that only 23% of the students want to do volunteer work, this is due to the fact that 77% of the students of the last year have a work activity and lack time. Regarding the preferences on the activities offered in the
university extension department, they focus on consulting and social service offered to various communities as support for them, followed by participation and support for courses, seminars and congresses, then participation in general cultural activities and, as a last activity, research and scientific publications. Uninorte students from the three campuses stated that they value the activities offered by the institution and consider them an enriching experience that increases the value of the professional profile, which is a way to grow as future professionals and support some communities.
Economic growth, development, planning, Human settlements. Communities
Systems thinking can shed light on important relationships and conditions that affect community engagement activities. While robust tools like the community capitals framework and the sustainable livelihoods approach provide valuable context for engagement projects, additional insights can stem from models that describe the ebb and flow of different types of capital. This paper uses a well-studied ecosystem model called adaptive renewal (AR) to contextualize heritage-related challenges and opportunities in four rural communities on the Canadian prairies. Based on a reflective case-study analysis, we applied the AR model to focus group and semistructured interview data collected as part of a Museums Association of Saskatchewan (MAS) project aimed at using local heritage assets to build sociocultural and environmental capacity and attract investment. The MAS project identified four themes that could be addressed through training and policy changes, including concerns about funding, limited human resources, a lack of public services, and a desire to preserve and build on memories. By mapping each community onto the AR model, we uncovered additional insights about community resilience and other heritage-related challenges and opportunities. The AR model is likely to be a valuable tool for planning or assessing community engagement projects because it reflects the dynamic nature of socioeconomic and cultural relationships that affect community dynamics and local well-being.
Conviviality can briefly be defined as togetherness among strangers despite their differences. While most of the research on conviviality focuses on (inter-)cultural differences, this article argues that considering other kinds of differences (e.g., socio-economic status, gender, age, stage of the life course, etc.) may increase our understanding of conviviality. In addition, to help us measure the convivial use of public space, the article looks at participation in “optional activities” (e.g., enjoying the sun, playing), which contribute to a convivial atmosphere by encouraging people to be co-present, thus offering the potential for “thicker sociability.” Based on fieldwork consisting of behavioural mapping (n = 1,448) and an intercept survey (n = 1,474), this study explores key factors that increase the likelihood of people using three small public squares in Zurich, Switzerland, in a convivial way. A logistic regression model based on survey data shows that, even when controlling for individual factors, the squares and their affordances contribute substantially to convivial use, e.g., by providing ample seating. The model furthermore suggests that gender, people’s relationship to the neighbourhood, their occupation, and the time of day, are more significant factors in shaping convivial use of the squares than the cultural background, socio-economic status, age, or having children.
Tyrel Stokes, Gurashish Bagga, Kimberly Kroetch
et al.
Multi-competitor races often feature complicated within-race strategies that are difficult to capture when training data on race outcome level data. Further, models which do not account for such strategic effects may suffer from confounded inferences and predictions. In this work we develop a general generative model for multi-competitor races which allows analysts to explicitly model certain strategic effects such as changing lanes or drafting and separate these impacts from competitor ability. The generative model allows one to simulate full races from any real or created starting position which opens new avenues for attributing value to within-race actions and to perform counter-factual analyses. This methodology is sufficiently general to apply to any track based multi-competitor races where both tracking data is available and competitor movement is well described by simultaneous forward and lateral movements. We apply this methodology to one-mile horse races using data provided by the New York Racing Association (NYRA) and the New York Thoroughbred Horsemen's Association (NYTHA) for the Big Data Derby 2022 Kaggle Competition. This data features granular tracking data for all horses at the frame-level (occurring at approximately 4hz). We demonstrate how this model can yield new inferences, such as the estimation of horse-specific speed profiles which vary over phases of the race, and examples of posterior predictive counterfactual simulations to answer questions of interest such as starting lane impacts on race outcomes.
The attempt in this article is to extrapolate the notion of hybridity latent in B. R. Ambedkar’s reflections on mixed castes, and outcastes, which subsequently leads to the causal link that he then derives gesticulating to social evils, namely, the origin of untouchability. Whether this embryonic notion of hybridity present in Ambedkar’s work is amenable to the extrapolation of Dalit identity thought along the lines of Gilles Deleuze’s notion of “immanent mixtures” is a thread that this study pursues. This certainly has broad implications for the prevalent notions of Dalit identity. This study ventures to read Ambedkar’s work, Riddles in Hinduism (1987) alongside Deleuze, probing into the intuitive link between notions of hybridity and the plane of immanence.
Ideological distancing from predetermined categories of identity considered to be reductive in nature by the intellectuals of Indian philosophical thinking view such predetermined notions as facile conceptions that run short of representative qualities of complex and varied particularities of reasoned engagement with one’s resources. Amartya Sen heralded this ideological position in his work titled, The Argumentative Indian (2006), in favor of heterodoxy and reasoned choice determining priorities between different identities. Lacunae regarding identification of resources prominent in Sen’s work is pointed out by Jonardon Ganeri, who hails from the cluster of contemporary Sanskritists competent in philological and theoretical exegesis of “sastric” philosophical literature from the classical period of India.
This study is a close reading of Jonardon Ganeri’s concept of ‘resources within’ which he develops in his work, Identity as Reasoned Choice (2012) to examine the potentiality of this concept to advance a theoretical framework that could counter a sectarian view of Indian tradition, as it is professed at the outset of his work. Sectarianism, which Ganeri opposes, identifies mysticism to be its chief trait which he shows to be selectively usurping only those resources grounded in Vedantic wisdom from India’s past.
This article investigates the contradictions that characterize Mobile, Alabama’s Joe Cain Day celebration. We look at the official narratives that established Mobile’s Mardi Gras origin myths and the event’s tradition invention in 1967 with a People’s Parade centered around Cain’s redface character, Chief Slacabamorinico. Then we discuss the complicated and ever-evolving symbolism surrounding the character by discussing more recent iterations of this public performance. In its inception, the Joe Cain celebration was a clear example of Lost Cause nostalgia, yet it has been adopted, adapted, and embraced by historically marginalized people who use it as a way to claim their space in the festivities. Employing both historical and ethnographic research, we show that carnival can simultaneously be a space for defiance and reaffirmation of social hierarchies and exclusionary discourses. We discuss here some of the concrete material elements that lend this public performance its white supremacist subtext, but we also want to complicate the definition of “materiality” by claiming a procession as a Confederate monument/memorial.
Ethnology. Social and cultural anthropology, Communities. Classes. Races
Amalachukwu Chijindu Ananwude, Steve Nkem Ibenta, Gideon Kasie Ezu
et al.
Purpose - This paper investigates the effect of commercial bank regulations, namely the price, product, and geographic regulations, on the intermediation function of commercial banks in Nigeria.
Methods - Using secondary data from 1986 to 2017 from the Central Bank of Nigeria (CBN) and the World Bank, this study employs the Autoregressive Distributive Lag (ARDL) model and Granger causality framework.
Findings - This paper provides evidence of a long-run relationship between commercial bank regulation and intermediation function represented by private sector credit to RGDP (regional gross domestic product). It also finds that commercial banks' regulation index through price, product, and geographic regulation has a positive relationship with intermediation function. Furthermore, the long-run relationship between commercial bank regulation and intermediation function described by private sector credit to RGDP is affirmed.
Implication - The Central Bank of Nigeria (CBN) needs to relax the product regulation to allow commercial banks to engage in various conventionally non-banking activities.
Originality - The paper contributes to the literature by ascertaining the commercial banks' intermediation function to Nigeria's economic growth and development.
Economic growth, development, planning, Regional economics. Space in economics
Rucha Kulkarni, Umang Mathur, Andreas Pavlogiannis
Data races are among the most common bugs in concurrency. The standard approach to data-race detection is via dynamic analyses, which work over executions of concurrent programs, instead of the program source code. The rich literature on the topic has created various notions of dynamic data races, which are known to be detected efficiently when certain parameters (e.g., number of threads) are small. However, the \emph{fine-grained} complexity of all these notions of races has remained elusive, making it impossible to characterize their trade-offs between precision and efficiency. In this work we establish several fine-grained separations between many popular notions of dynamic data races. The input is an execution trace with $N$ events, $T$ threads and $L$ locks. Our main results are as follows. First, we show that happens-before (HB) races can be detected in $O(N\cdot \min(T, L))$ time, improving over the standard $O(N\cdot T)$ bound when $L=o(T)$. Moreover, we show that even reporting an HB race that involves a read access is hard for 2-orthogonal vectors (2-OV). This is the first rigorous proof of the conjectured quadratic lower-bound in detecting HB races. Second, we show that the recently introduced synchronization-preserving races are hard to detect for OV-3 and thus have a cubic lower bound, when $T=Ω(N)$. This establishes a complexity separation from HB races which are known to be less expressive. Third, we show that lock-cover races are hard for 2-OV, and thus have a quadratic lower-bound, even when $T=2$ and $L = ω(\log N)$. The similar notion of lock-set races is known to be detectable in $O(N\cdot L)$ time, and thus we achieve a complexity separation between the two. Moreover, we show that lock-set races become hitting-set (HS)-hard when $L=Θ(N)$, and thus also have a quadratic lower bound, when the input is sufficiently complex.
Shared e-scooters may complement public transport by offering a solution to the first/last mile problem by easing, or increasing the radius of, access and egress trips. We have gathered real time e-scooter supply and demand data and performed a web survey of e-scooter users in Oslo, Norway. We find that e-scooters stand out as a popular first/last mile mode to many public transport passengers. E-scooters can play an even stronger such role if the two modes are integrated further.
Transportation and communications, Urban groups. The city. Urban sociology