Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility

Virtual devices based on device emulation have been widely used in lab research of mobile app testing for their efficiency and low cost. However, it remains controversial to use virtual devices for app testing in industry, given the inherent difficulties of high-fidelity emulation across diverse mobile systems and devices. Hence, mobile app companies still rely on physical device farms or services like AWS Device Farm. This paper presents our effort to analyze, improve, and effectively use virtual devices for large-scale testing of mobile apps like Douyin. Our goal is to understand the fidelity of virtual devices and to explore how to better utilize virtual devices to improve the efficiency and accessibility of mobile app testing in industrial settings. Our study is conducted on a massive commercial testing infrastructure that deploys a physical device farm and its virtualized counterpart. We show that high-fidelity app testing can be achieved by sensible design and implementation of virtual device farms. With that, we find that major discrepancies are no longer caused by commonly believed factors like hardware heterogeneity and system customizations, but due to non-standard, uncoordinated, and occasionally defective vendor-specific services and drivers, as well as defense mechanisms against malicious app behavior. We present effective solutions to address those problems to significantly improve testing fidelity. We also share our experiences of using virtual device farms to substantially improve the efficiency and accessibility of mobile app testing, without compromising safety.
UvA-DARE (Digital Academic Repository) Is Alcohol-Related Aggression Related to Automatic Alcohol-Power or Alcohol-Aggression Associations? to

Objective: One of the risks of alcohol use is violent behavior. The aim of the current study was to explore the role of automatic cognitive associations between alcohol and power and between alcohol and aggression in alcohol-related aggression and to test the moderating role of executive control. Method: Implicit association tests (IATs) were used to measure alcohol–power and alcohol–aggression associations in a sample of adolescents. The classical Stroop task was used to assess executive control. The tendency toward alcohol-related aggression was measured via self-report. Results: Alcohol–power associations predicted self-reported alcohol-related aggression, but there was no significant moderation by executive control. Alcohol–aggression associations did not predict self-reported alcohol-related aggression. Mathematical modeling was used to further explore differences between alcohol–power and alcohol–aggression associations. This analysis tentatively indicated that participants may have had a stronger tendency to overcome the bias in the alcohol–aggression IAT. Conclusions: Alcohol–power associations, but not alcohol–aggression associations, predicted alcohol-related aggression. Participants appear to exert more control when dealing with aggression- than with power-related alcohol associations, which may therefore better predict alcohol-related aggression. Implicit measures in combination with modeling techniques may be important in further disentangling the automatic processes involved in alcohol-related aggressive behavior, but the relationship between alcohol and power appears to be an essential factor.
Elementary excitations and quantized vortices in bosonic superfluids

In this work we studied two sistems of bosonic superfluids at zero temperature, ultra-cold Bose gases and superfluid helium, within a simple classical field formalism and we underlined differences and similarities between them. We also analyzed how the presence of strong interactions, as in the case of helium, modifies the quasiparticle excitation spectrum and subsequently the vortex line density profile. After a general review about these bosonic systems properties we started in general from the Hartree equation of a bosonic system and, with different hypotesis on the interaction potential between bosons, we obtained the equations describing the two above mentioned systems, mainly focusing on elementary quasiparticle excitations and quantized vortices. Furthermore throughout this work the results were compared with experimental data and aftermaths of the main theories in literature.
A Multi-instance Learning Approach to Civil Unrest Event Detection on Twitter

Social media has become an established platform for people to organize and take offline actions, often in the form of civil unrest. Understanding these events can help support pro-democratic movements. The primary method to detect these events on Twitter relies on aggregating many tweets, but this includes many that are not relevant to the task. We propose a multi-instance learning (MIL) approach, which jointly identifies relevant tweets and detects civil unrest events. We demonstrate that MIL improves civil unrest detection over methods based on simple aggregation. Our best model achieves a 0.73 F1 on the Global Civil Unrest on Twitter (G-CUT) dataset.
The Marlborough Nickel Laterite Deposits

The enrichment was discovered in 1962 by the Geological Survey of Queensland (GSQ). Throughout the 1960's and 1970's BHP and INAL drilled over 2000 reverse-circulation and air core holes. Queensland Nickel Pty Ltd acquired the exploration leases in 1986 and developed the Brolga deposit in the southern end of the region. Full-scale mining of the Brolga deposit began in 1993 and ceased 2.5 years later in 1995. Preston Resources acquired the Marlborough portion of the Nickel deposits from Central Queensland Nickel in 1997 and began an extensive resource proving operation. In 1998 the mine feasibility was completed. The current proved reserve is 52 million tonnes at 0.88% nickel and 0.06% cobalt with an estimated resource of 210 million tonnes of ore grading 1.02% nickel and 0.06% cobalt (Preston Resources 2000).
Overflow-free Compute Memories for Edge AI Acceleration

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can provide computing capabilities as part of the memory array structures (In-Memory Computing, IMC) or at their immediate periphery (Near-Memory Computing, NMC). By bringing the processing elements inside (or very close to) storage, compute memories minimize the cost of data access. Moreover, highly parallel (and, hence, high-performance) computations are enabled by exploiting the regular structure of memory arrays. However, the regular layout of memory elements also constrains the data range of inputs and outputs, since the bitwidths of operands and results stored at each address cannot be freely varied. Addressing this challenge, we herein propose a HW/SW co-design methodology combining careful per-layer quantization and inter-layer scaling with lightweight hardware support for overflow-free computation of dot-vector operations. We demonstrate their use to implement the convolutional and fully connected layers of AI models. We embody our strategy in two implementations, based on IMC and NMC, respectively. Experimental results highlight that an area overhead of only 10.5% (for IMC) and 12.9% (for NMC) is required when interfacing with a 2KB subarray. Furthermore, inferences on benchmark CNNs show negligible accuracy degradation due to quantization for equivalent floating-point implementations.
Kryptonite: Worst-Case Program Interference Estimation on Multi-Core Embedded Systems

Due to the low costs and energy needed, cyber-physical systems are adopting multi-core processors for their embedded computing requirements. In order to guarantee safety when the application has real-time constraints, a critical requirement is to estimate the worst-case interference from other executing programs. However, the complexity of multi-core hardware inhibits precisely determining the Worst-Case Program Interference. Existing solutions are either prone to overestimate the interference or are not scalable to different hardware sizes and designs. In this paper we present Kryptonite, an automated framework to synthesize Worst-Case Program Interference (WCPI) environments for multi-core systems. Fundamental to Kryptoniteis a set of tiny hardware-specific code gadgets that are crafted to maximize interference locally. The gadgets are arranged using a greedy approach and then molded using a Reinforcement Learning algorithm to create the WCPI environment. We demonstrate Kryptoniteon the automotive grade Infineon AURIX TC399 processor with a wide range of programs that includes a commercial real-time automotive application. We show that, while being easily scalable and tunable, Kryptonitecreates WCPI environments increasing the runtime by up to 58% for benchmark applications and 26% for the automotive application.
Morelos A-2010, new rice variety for direct sowing for central Mexico

En la zona central de Mexico se siembra el arroz (Oryza sativa L.) en trasplante y directa bajo riego; la mayoria de las variedades se han liberado para trasplante; sin embargo, al usarlas para siembra directa tienen el problema del acame. En 1998 se inicio el mejoramiento del arroz por induccion de mutaciones con el objetivo de obtener una variedad para siembra directa. Para esto, se irradiaron 200 gramos de semilla de la variedad Morelos A-92, en la fuente Gammacell 220 del Instituto Nacional de Investigaciones Nucleares, con las dosis de rayos gamma 60Co: 20, 25, 30 y 35 krad. Entre 1999 a 2005 se avanzo en la seleccion de la generacion M1 a la M7 dando como resultado 18 lineas uniformes en 2006. En 2007 y 2008 se evaluaron en ensayos de rendimiento en las localidades de Zacatepec y Cuautla, Morelos comparandolas con el testigo comercial Morelos A-98, sobresaliendo las lineas: M7, M14, M16, M17 y M18 por rendimiento superior a 10 t ha-1, moderada resistencia a pirycularia, (Magnaporthe grisea) habito de crecimiento erecto, altura de planta de 120 cm, resistentes al acame; rendimiento en industria 57% de grano pulido entero; conservando la calidad Morelos (20% de centro blanco). En 2009 estas lineas se validaron en trasplante y siembra directa en parcelas de productores cooperantes en cuatro localidades de Morelos y la M18 sobresalio de las demas por su rendimiento, adaptabilidad y calidad del grano, de tal forma que para el ano 2010 fue liberada como nueva variedad de arroz. Abstract
Ecology laboratory manual

1 The Nature of Data 2 The Process of Science 3 Soil Analysis 4 Oxygen and Carbon Dioxide Cycling 5 Population Growth 6 Age Distribution and Survivorship 7 Terrestrial Plant Community Assessment 8 Stream Ecosystem Assessment 9 Micro Community Assessment 10 Sampling a Plant Community 11 Sampling Animal Communities 12 Species Diversity 13 Primary Production in an Aquatic Community 14 Competition 15 Natural Selection 16 Adaptations of Vertebrates to Their Environment 17 Adaptations of Plants to Their Environment
The Effect of Racial and Ethnic Attitudes on Hispanic Identity in the U.S.

I study the determinants of the choice to identify as Hispanic among those who could—those whose parents, grandparents, or selves were born in a Spanish-speaking country. I find that individuals with Hispanic ancestry are significantly less likely to self-identify as Hispanic if they live in states with high levels of implicit ethnic bias. A one standard deviation increase in bias decreases self-reported Hispanic identity by 7 and 13 percentage points for first and second-generation Hispanics, respectively. These effects are more prominent among second-generation immigrants with both parents born in a Spanish-speaking country than among children of inter-ethnic parents. These findings have implications for the interpretation of research on ethnic gaps in economic outcomes and the correct counting of the population
Some statistical features of the aftershock temporal behavior after the M7.4 Izmit earthquake of august 17, 1999 in Turkey

An earthquake of magnitude Mw=7.4 struck 8 km. southeast of Izmit, Turkey at 3:02 AM local time on August 17, 1999. The earthquake occurred on one of the world’s longest and best studied strike-slip (horizontal motion) faults the east-west trending North Anatolian fault. Seismologists are not able to predict the timing and sizes of individual aftershocks but stochastic modeling allows determinationof probabilities for aftershocks and larger mainshocks duringintervals following the mainshock.
Significación y métodos de valoración del recuento celular en la leche de oveja

La respuesta leucocitaria, desarrollada como consecuencia de la infeccion mamaria, origina cambios significativos en la concentracion celular de la leche, cuyo conocimiento requiere de tecnicas analiticas especificas. De los 5 tipos celulares presentes en la leche (polimorfonucleares, macrofagos, linfocitos, celulas epiteliales y celulas plasmaticas), los polimorfonucleares asumen el papel preponderante en el desarrollo de la reaccion inflamatoria consecuente a la infeccion. En la leche de oveja, al igual que en la de vaca, la microscopia directa con la tincion azul de metileno es un metodo de referencia valido. El metodo fluoro-opto-electronico (Fossomatic) ha demostrado ser un buen metodo laboratorial, en terminos de precision y repetibilidad, para la determinacion rutinaria del recuento celular de la leche, si bien resulta necesario conocer sus principales factores de variacion con el fin de definir sus condiciones optimas de funcionamiento en cada especie. La informacion existente actualmente sobre la validez del metodo Coulter counter en la oveja es concluyente. El control de calidad intra e interlaboratorios, y la utilizacion de patrones de referencia de recuento celular conocido, se revelan como aspectos del mayor interes de cara a garantizar un adecuado rendimiento de metodos y laboratorios de recuento celular.
Non-Compliant Bandits

Bandit algorithms arose as a standard approach to learning better models online. As they become more popular, they are increasingly deployed in complex machine learning pipelines, where their actions can be overwritten. For example, in ranking problems, a list of recommended items can be modified by a downstream algorithm to increase diversity. This may break the classic bandit algorithms and lead to linear regret. Specifically, if the proposed action is not taken, uncertainty in its estimated mean reward may not get reduced. In this work, we study this setting and call it non-compliant bandits; as the agent tries to learn rewarding actions that comply with a downstream task. We propose two algorithms, compliant contextual UCB (CompUCB) and Thompson sampling (CompTS), which learn separate reward and compliance models. The compliance model allows the agent to avoid non-compliant actions. We derive a sublinear regret bound for CompUCB. We also conduct experiments that compare our algorithms to classic bandit baselines. The experiments show failures of the baselines and that we mitigate them by learning compliance models.
IOSR: Improving I/O Efficiency for Memory Swapping on Mobile Devices Via Scheduling and Reshaping

Mobile systems and applications are becoming increasingly feature-rich and powerful, which constantly suffer from memory pressure, especially for devices equipped with limited DRAM. Swapping inactive DRAM pages to the storage device is a promising solution to extend the physical memory. However, existing mobile devices usually adopt flash memory as the storage device, where swapping DRAM pages to flash memory may introduce significant performance overhead. In this paper, we first conduct an in-depth analysis of the I/O characteristics of the flash-based memory swapping, including the I/O interference and swap I/O randomness in swap subsystem. Then an I/O efficiency optimization framework for memory swapping (IOSR) is proposed to enhance the performance of flash-based memory swapping for mobile devices. IOSR consists of two methods: swap I/O scheduling (SIOS) and swap I/O pattern reshaping (SIOR). SIOS is designed to schedule the swap I/O to reduce interference with other processes I/Os. SIOR is designed to reshape the swap I/O pattern with process-oriented swap slot allocation and adaptive granularity swap read-ahead. IOSR is implemented on Google Pixel 4. Experimental results show that IOSR reduces the application switching time by 31.7% and improves the swap-in bandwidth by 35.5% on average compared to the state-of-the-art.
Cannabinoids and endocannabinoids as therapeutics for nervous system disorders: preclinical models and clinical studies

Cannabinoids are lipophilic substances derived from Cannabis sativa that can exert a variety of effects in the human body. They have been studied in cellular and animal models as well as in human clinical trials for their therapeutic benefits in several human diseases. Some of these include central nervous system (CNS) diseases and dysfunctions such as forms of epilepsy, multiple sclerosis, Parkinson’s disease, pain and neuropsychiatric disorders. In addition, the endogenously produced cannabinoid lipids, endocannabinoids, are critical for normal CNS function, and if controlled or modified, may represent an additional therapeutic avenue for CNS diseases. This review discusses in vitro cellular, ex vivo tissue and in vivo animal model studies on cannabinoids and their utility as therapeutics in multiple CNS pathologies. In addition, the review provides an overview on the use of cannabinoids in human clinical trials for a variety of CNS diseases. Cannabinoids and endocannabinoids hold promise for use as disease modifiers and therapeutic agents for the prevention or treatment of neurodegenerative diseases and neurological disorders.
Rbm8a regulates neurogenesis and reduces Alzheimer’s disease-associated pathology in the dentate gyrus of 5×FAD mice

Alzheimer's disease is a prevalent and debilitating neurodegenerative condition that profoundly affects a patient's daily functioning with progressive cognitive decline, which can be partly attributed to impaired hippocampal neurogenesis. Neurogenesis in the hippocampal dentate gyrus is likely to persist throughout life but declines with aging, especially in Alzheimer's disease. Recent evidence indicated that RNA-binding protein 8A (Rbm8a) promotes the proliferation of neural progenitor cells, with lower expression levels observed in Alzheimer's disease patients compared with healthy people. This study investigated the hypothesis that Rbm8a overexpression may enhance neurogenesis by promoting the proliferation of neural progenitor cells to improve memory impairment in Alzheimer's disease. Therefore, Rbm8a overexpression was induced in the dentate gyrus of 5×FAD mice to validate this hypothesis. Elevated Rbm8a levels in the dentate gyrus triggered neurogenesis and abated pathological phenotypes (such as plaque formation, gliosis reaction, and dystrophic neurites), leading to ameliorated memory performance in 5×FAD mice. RNA sequencing data further substantiated these findings, showing the enrichment of differentially expressed genes involved in biological processes including neurogenesis, cell proliferation, and amyloid protein formation. In conclusion, overexpressing Rbm8a in the dentate gyrus of 5×FAD mouse brains improved cognitive function by ameliorating amyloid-beta-associated pathological phenotypes and enhancing neurogenesis.
Couple Recovery from Problematic Pornography Use: A Foundational Phenomenological Inquiry

Abstract Marriage and family therapists report problematic pornography use (PPU) as a common concern seen while working with couples and families. However, the research regarding treatment of PPU is limited, particularly relational/systemic treatment has been underexplored. The present study utilized a phenomenological approach to understand the lived experience of 11 heterosexual, married, highly religious, American couples who have achieved significant recovery from PPU. The major themes that emerged from the interviews are Catalysts for Recovery, Foundation of Support, The Work, Healing Perspectives, and Hindrances and Cautions. Examples of themes and subthemes are discussed as well as implications for treatment of couples seeking healing from PPU.
Enhancing Speaking Skills and Vocabulary in the EAL Classroom Through TikTok: A Literature Review

The widespread adoption of TikTok globally has positively impacted its application in education, particularly in language teaching and learning. English, being widely spoken as a lingua franca, is extensively used for content dissemination through TikTok worldwide. However, a preliminary search on the internet revealed a need for more research syntheses on the use of this platform in the English as an Additional Language (EAL) classroom. This scarcity prompted the research discussed in this article. The study took the form of a literature review and followed the principles of Systematic Literature Review, aiming to explore how TikTok has been used in the EAL classroom and what learning benefits it offers. An adapted version of the research protocol model developed by Sarah Visintini was employed for searching, selecting, and extracting written productions from web-based databases to compose the research sample. Eight peer-reviewed articles constituted the final sample based on retention and discard criteria. The retained texts were analyzed using the thematic analysis method proposed by Virginia Braun and Victoria Clarke. The findings indicate that TikTok can effectively enhance speaking skills and expand the vocabulary repertoire of EAL students. Moreover, its usage can aid in maintaining student focus on classroom activities. Further comprehensive searches in online databases, using diverse mechanisms, can yield substantial corpora, facilitating broader and more in-depth analyses and discussions on the pedagogical applications and benefits of TikTok in the EAL classroom.
Computationally Efficient DNN Mapping Search Heuristic using Deep Reinforcement Learning

In this work, we present a computationally efficient Reinforcement Learning mapping search heuristic for finding high quality mappings for N-dimensional convolution loops that uses a computationally inexpensive reward function based on potential data reuse of operands to guide the search process. We also present a RL state representation generalizable to N-dimensional convolution loops, and a state representation parsing strategy ensuring that only valid mappings are evaluated for quality. Our RL search heuristic is applicable to multi-core systems with a memory hierarchy. We show that our RL based search heuristic for a range of 3D convolution layers, at significantly lower computational expense than random search, generally yields mappings with lower Energy-Delay Product (EDP) for an architecture with multiple processing elements with shared memory connected to DRAM. Our evaluation results demonstrated across 19 3D convolution layers, shows that our RL method performed only an average 11.24% of the operations of that of Timeloop’s random search for assessing same number of valid mappings. The mappings found using Timeloop had an average 12.51% higher EDP compared to lowest EDP mapping found using our RL method. Further, the lowest EDP mappings found using our method had an average only 4.69× higher EDP than the theoretical lower bound EDP, with the best case being only 1.29× higher.