Miracle-dz/UniFAR-FTUs huggingface dataset:
3D-based Video Recognition Acceleration by Leveraging Temporal Locality

Recent years have seen an explosion of domain-specific accelerator for Convolutional Neural Networks (CNN). Most of the prior CNN accelerators target neural networks on image recognition, such as AlexNet, VGG, GoogleNet, ResNet, etc. In this paper, we take a different route and study the acceleration of 3D CNN, which are more computational-intensive than 2D CNN and exhibits more opportunities. After our characterization on representative 3D CNNs, we leverage differential convolution across the temporal dimension, which operates on the temporal delta of imaps for each layer and process the computation bit-serially using only the effectual bits of the temporal delta. To further leverage the spatial locality and temporal locality, and make the architecture general to all CNNs, we propose a control mechanism to dynamically switch across spatial delta dataflow and temporal delta dataflow. We call our design temporal-spatial value aware accelerator (TSVA). Evaluation on a set of representation NN networks shows that TSVA can achieve an average of 4.24x speedup and 1.42x energy efficiency. While we target 3D CNN for video recognition, TSVA could also benefit other general CNNs for continuous batch processing.
A Social Media Study on the Effects of Psychiatric Medication Use

Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual's psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.
Association mapping for salinity tolerance in cotton ('Gossypium hirsutum' L.) germplasm from US and diverse regions of China

Salinity affects cotton production worldwide. In our study, we assessed marker-trait associations for salinity tolerance in cotton using a set of 109 cotton variety germplasm (mainly from China and USA). Cotton varieties were screened for polymorphism with 250 SSR markers. Out of these 250 SSR markers, 98 were found to be polymorphic. Plant material was grown under normal versus saline (100mM and 200mM NaCl) conditions in greenhouse and data was collected for morpho-physiological traits at seedling stage. SSR markers linked with T1, T2, relative value of T1, and relative value of T2 treatments only were considered associated with salinity tolerance. On the basis of overall performance of cultivars judged by relative values, cultivars Jian mian 13, Si mian 4 and Gan mian 8 were found to be salt tolerant, whereas, Ke yi 2, Yan mian 48 and Zhong mian suo 49 were found to be salt sensitive. STRUCTURE software identified 5 sub-populations in this cotton germplasm. These sub-populations consisted of 10-30 varieties. At r 2 > 0.05, 3% SSR marker pairs showed significant pairwise linkage disequilibrium (LD). At the highly significant threshold of r 2 > 0.1, 1.82% of SSR marker pairs were remained in LD. Genome-wide LD at r 2 > 0.1 was reduced to ~4 – 7 cM, indicating a strong potential for association mapping. Markers BNL3103 (D6), NAU478 (D8) and BNL3140 (D9) were associated with salt treatment. These markers can be utilized in molecular breeding of cotton for the release of salt tolerant cultivars.
XPC: Architectural Support for Secure and Efficient Cross Process Call

Microkernel has many intriguing features like security, fault-tolerance, modularity and customizability, which recently stimulate a resurgent interest in both academia and industry (including seL4, QNX and Google's Fuchsia OS). However, IPC (inter-process communication), which is known as the Achilles' Heel of microkernels, is still the major factor for the overall (poor) OS performance. Besides, IPC also plays a vital role in monolithic kernels like Android Linux, as mobile applications frequently communicate with plenty of user-level services through IPC. Previous software optimizations of IPC usually cannot bypass the kernel which is responsible for domain switching and message copying/remapping; hardware solutions like tagged memory or capability replace page tables for isolation, but usually require non-trivial modification to existing software stack to adapt the new hardware primitives. In this paper, we propose a hardware-assisted OS primitive, XPC (Cross Process Call), for fast and secure synchronous IPC. XPC enables direct switch between IPC caller and callee without trapping into the kernel, and supports message passing across multiple processes through the invocation chain without copying. The primitive is compatible with the traditional address space based isolation mechanism and can be easily integrated into existing microkernels and monolithic kernels. We have implemented a prototype of XPC based on a Rocket RISC-V core with FPGA boards and ported two microkernel implementations, seL4 and Zircon, and one monolithic kernel implementation, Android Binder, for evaluation. We also implement XPC on GEM5 simulator to validate the generality. The result shows that XPC can reduce IPC call latency from 664 to 21 cycles, up to 54.2x improvement on Android Binder, and improve the performance of real-world applications on microkernels by 1.6x on Sqlite3 and 10x on an HTTP server with minimal hardware resource cost.
Master of None Acceleration: A Comparison of Accelerator Architectures for Analytical Query Processing

Hardware accelerators are one promising solution to contend with the end of Dennard scaling and the slowdown of Moore's law. For mature workloads that are regular and have high compute per byte, hardening an application into one or more hardware modules is a standard approach. However, for some applications, we find that a programmable homogeneous architecture is preferable. This paper compares a previously proposed heterogeneous hardware accelerator for analytical query processing to a homogeneous systolic array alternative. We find that the heterogeneous and homogeneous accelerators are equivalent for large designs, while for small designs the homogeneous is better. Our analysis explains this counter-intuitive result, finding that the homogeneous architecture has higher average resource utilization and lower relative costs for the communication infrastructure.
Who Should Be the Captain This Week?Leveraging Inferred Diversity-Enhanced Crowd Wisdom for a Fantasy Premier League Captain Prediction

Participants in Fantasy Sports make a critical decision: selecting productive players for their fantasy team. The wellestablished Wisdom of Crowd effect can predict productive, rewarding players; popular, frequently selected players are potentially good choices. Previous performance data permits the identification of a subset of participants who collectively predict productive players. However, performance data may not always be available. Here we study the assembly of a small subset of the crowd a priori using another important crowd property: semantic diversity. We infer diversity from participants’ Twitter posts (tweets) that users voluntarily, and naturally provide as part of their reasoning. We propose the SmartCrowd framework to select a small, smart crowd using participants’ Twitter posts. SmartCrowd includes three steps: 1) characterize participants using their social media posts with summary word vectors, 2) cluster participants based on these vectors, and 3) sample participants from these clusters, maximizing multiple diversity measures to form final diverse crowds. We evaluated our approach to diversity characterization for the Fantasy Premier League (FPL) captain prediction problem, in which participants predict a successful weekly captain among a set of soccer players. Empirical evaluation shows that SmartCrowd generates diverse crowds outperforming random crowds, 93% of individual participants, and crowds consisting of the top 10%, 20% experts identified from previous performance data. We provide converging evidence that social media based diversity supports the sampling of smarter crowds that collectively predict productive players. These results have implications for other domains, such as economics and geopolitical forecasting, that benefit from aggregated judgments.
View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and platforms. Finally, we show that one can predict if an article will be publicly shared to another platform by individuals with precision of approximately 80%. This research has implications for news organizations desiring to increase and to prioritize types of user engagement.
SgxPectre Attacks: Leaking Enclave Secrets via Speculative Execution

This paper presents SgxPectre Attacks that exploit the recently disclosed CPU bugs to subvert the confidentiality of SGX enclaves. Particularly, we show that when branch prediction of the enclave code can be influenced by programs outside the enclave, the control flow of the enclave program can be temporarily altered to execute instructions that lead to observable cache-state changes. An adversary observing such changes can learn secrets inside the enclave memory or its internal registers, thus completely defeating the confidentiality guarantee offered by SGX. To demonstrate the practicality of our SgxPectre Attacks, we have systematically explored the possible attack vectors of branch target injection, approaches to win the race condition during enclave's speculative execution, and techniques to automatically search for code patterns required for launching the attacks. Our study suggests that any enclave program could be vulnerable to SgxPectre Attacks since the desired code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX).
Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images

There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of image tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information ecosystem, facilitating new modes of visual communication and sharing. Recently, they have become widely available as Cognitive Services. But while tagging APIs offer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and proprietary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some offer more interpretation on images, they may exhibit less fairness toward the depicted persons, by misuse of gender-related tags and/or making judgments on a person’s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarked against a ground truth.
Explaining Multimodal Deceptive News Prediction Models

In this study we present in-depth quantitative and qualitative analyses of the behavior of multimodal deceptive news classification models. We present several neural network architectures trained on thousands of tweets that leverage combinations of text, lexical, and, most importantly, image input signals. The behavior of these models is analyzed across four deceptive news prediction tasks. Our quantitative analysis reveals that text only models outperform those leveraging only the image signals (by 3-13% absolute in F-measure). Neural network models that combine image and text signals with lexical features e.g., biased and subjective language markers perform even better e.g., F-measure is as high as 0.74 for binary classification setup for distinguishing between verified and deceptive content identified as disinformation and propaganda. Our qualitative analysis of model performance, that goes beyond the F-score, performed using a novel interactive tool ERRFILTER1 allows a user to characterize text and image traits of suspicious news content and analyze patterns of errors made by the various models, which in turn will inform the design of future deceptive news prediction models.
Cryogenic Computer Architecture Modeling with Memory-Side Case Studies

Modern computer architectures suffer from lack of architectural innovations, mainly due to the power wall and the memory wall. That is, architectural innovations become infeasible because they can prohibitively increase power consumption and their performance impacts are eventually bounded by slow memory accesses. To address the challenges, making computer systems run at ultralow temperatures (or cryogenic computer systems) has emerged as a highly promising solution as both power consumption and wire resistivity are expected to significantly reduce at ultra-low temperatures. However, cryogenic computers have not been yet realized as computer architects do not fully understand the behaviors of existing computer systems and their cost effectiveness at such ultra-low temperatures. In this paper, we first develop CryoRAM, a validated computer architecture simulation tool to incorporate cryogenic memory devices. For this work, we focus on 77K temperature (easily achieved by applying low-cost liquid nitrogen), at which modern CMOS devices still reliably operate. We also focus on reducing the temperature of memory devices only as a pilot study prior to building a full cryogenic computer. Next, driven by the modeling tool, we propose our temperature-aware memory device and architecture designs to improve the DRAM access speed by 3.8 times or reduce the power consumption to 9.2%. Finally, we provide three promising case studies using cryogenic memories to significantly improve (1) server performance (up to 2.5 times), (2) server power (down to 6% on average), and (3) datacenter's power cost (by 8.4%). We will release our modeling and simulation tools deliberately implemented on top of only open-source simulators combined, even though some experiments were conducted under industry-confidential environments.
Stream-based Memory Access Specialization for General Purpose Processors

Because of severe limitations in technology scaling, architects have innovated in specializing general purpose processors for computation primitives (e.g. vector instructions, loop accelerators). The general principle is exposing rich semantics to the ISA. An opportunity to explore is whether richer semantics of memory access patterns could also be used to improve the efficiency of memory and communication. Two important open questions are how to convey higher level memory information and how to take advantage of this information in hardware. We find that a majority of memory accesses follow a small number of simple patterns; we term these streams (e.g. affine, indirect). Streams can often be decoupled from core execution, and patterns persist long enough to express useful behavior. Our approach is therefore to express streams as ISA primitives, which we argue can enable: prefetch stream accesses to hide memory latency, semi- binding decoupled access to remove address computation and optimize the memory interface, and finally inform cache policies. In this work, we propose ISA-extensions for decoupled-streams, which interact with the core using a FIFO-based interface. We implement optimizations for each of the aforementioned opportunities on an aggressive wide-issue OOO core and evaluate with SPEC CPU 2017 and CortexSuite[1], [2]. Across all workloads, we observe about 1.37x speedup and energy efficiency improvement over hardware stride prefetching.
ST Reliability and Connectivity of VANETs for Different Mobility Environments

Vehicular ad-hoc network (VANET) is the name of technology, which uses ‘mobile internet' to facilitate communication between vehicles. The aim is to ensure road safety and achieve secure communication. Therefore, the reliability of this type of networks is a serious concern. The reliability of VANET is dependent upon proper communication between vehicles within a given amount of time. Therefore a new formula is introduced, the terms of the new formula correspond 1 by 1 to a class special ST route (SRORT). The new formula terms are much lesser than the Inclusion-Exclusion principle. An algorithm for the Source-to-Terminal reliability was presented, the algorithm produced Source-to-Terminal reliability or computed a Source-to-Terminal reliability expression by calculating a class of special networks of the given network. Since the architecture of this class of networks which need to be computed was comparatively trivial, the performance of the new algorithm was superior to the Inclusion-Exclusion principle. Also, we introduce a mobility metric called universal speed factor (USF) which is the extension of the existing speed factor, that suppose same speed of all vehicles at every time. The USF describes an exact relation between the relative speed of consecutive vehicles and the headway distance. The connectivity of vehicles in different mobile situations is analyzed using USF i.e., slow mobility connectivity, static connectivity, and high mobility connectivity. It is observed that
Leveraging Program Invariants to Promote Population Diversity in Search-Based Automatic Program Repair

Search-based automatic program repair has shown promise in reducing the cost of defects in real-world software. However, to date, such techniques have typically been most successful when constructing short or single-edit repairs. This is true even when techniques make use of heuristic search strategies, like genetic programming, that in principle support the construction of patches of arbitrary length. One key reason is that the fitness function traditionally depends entirely on test cases, which are poor at identifying partially correct solutions and lead to a fitness landscape with many plateaus. We propose a novel fitness function that optimizes for both functionality and semantic diversity, characterized using learned invariants over intermediate behavior. Our early results show that this new approach improves semantic diversity and fitness granularity, but does not statistically significantly improve repair performance.
Detecting Journalism in the Age of Social Media: Three Experiments in Classifying Journalists on Twitter

The widespread adoption of networked information and communications technologies (i.e. ICTs) blurs traditional boundaries between journalist and citizen. The role of the journalist is adapting to structural changes in the news industry and dynamic audience expectations. For researchers who seek to understand what, if any, distinct role journalists play in the production and propagation of breaking news, it is vital to be able to identify journalists in social media spaces. In many cases, this can be challenging due to the limited information and metadata about social media users. In this work, we use a supervised machine learning model to automatically distinguish journalists from non-journalists in social media spaces. Leveraging Twitter data collected from three crisis events of different types, we examine how profile information, social network structure, posting behavior and language distinguish journalists from others. Additionally, we evaluate how the performance of the journalist classification model varies by context (i.e. types of crisis events) and by journalism outlets (i.e. print versus broadcast journalism), and discuss challenges in automatic journalist detection. Implications of this work are discussed; in particular we argue for the value of such methods for scaling analysis in journalism studies beyond the capacity of human coders. Employing classification methods in this context allows for systematic, large-scale studies of the role of journalists online.
ERG Tumor Type is Less Frequent in High Grade and High Stage Prostate Cancers of Chinese Men

Background: The incidence of prostatic adenocarcinoma has been rapidly increasing among Chinese men. This alarming trend prompted evaluations of early causal genomic alterations known to drive prostate tumorigenesis. Recurrent activation of the ETS-Related Gene (ERG) by genomic rearrangements is the most recognized early event in prostate cancer. Following the initial detection of ERG rearrangement at gene expression and genomic and levels, development of diagnostic quality antibodies against ERG oncoprotein have streamlined the rapid assessment of ERG frequencies world-wide. Unexpectedly, these studies revealed highest frequencies of ERG among Caucasian descents, lower frequencies among African Americans and even lower prevalence of ERG among Asian men. Objective: To asses in a prospective study ERG frequencies, clinico-pathological and prognostic associations of ERG among prostate cancer patients of the Dalian region of Northeast China, by an established immunohistochemical procedure that have been used in studies world-wide. Methods: Formalin fixed paraffin embedded specimens donated by patients (N=50) diagnosed with prostatic adenocarcinoma who underwent transurethral resection of the prostate (TURP) between 2007 and 2012 were evaluated for ERG by immunohistochemistry. Results: Of the 50 cases, 13/50 (26.0%) tumors were positive for ERG. In all cases, normal prostatic epithelial were ERG negative. ERG was more frequently detected in the lower Gleason score (≤7) and low T-stage. Consistent with reports from Asian countries the results of our study shows lower overall frequencies of ERG positive tumors when compared to reports from Western countries. Conclusion: The intriguing association of even lower ERG frequencies with high Gleason scores and higher T-stages provides impetus for current driver gene discoveries focused on the predominantly ERG negative prostate cancers of Asian men.