Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%.
Code Synthesis for Sparse Tensor Format Conversion and Optimization

Many scientific applications compute using sparse data and store that data in a variety of sparse formats because each format has unique space and performance benefits. Optimizing applications that use sparse data involves translating the sparse data into the chosen format and transforming the computation to iterate over that format. This paper presents a formal definition of sparse tensor formats and an automated approach to synthesize the transformation between formats. This approach is unique in that it supports ordering constraints not supported by other approaches and synthesizes the transformation code in a high-level intermediate representation suitable for applying composable transformations such as loop fusion and temporary storage reduction. We demonstrate that the synthesized code for COO to CSR with optimizations is 2.85x faster than TACO, Intel MKL, and SPARSKIT while the more complex COO to DIA is 1.4x slower than TACO but faster than SPARSKIT and Intel MKL using the geometric average of execution time.
Can We Empower Attentive E-reading with a Social Robot? An Introductory Study with a Novel Multimodal Dataset and Deep Learning Approaches

Reading on digital devices has become more commonplace, while it often poses challenges to learners’ attention. In this study, we hypothesized that allowing learners to reflect on their reading phases with an empathic social robot companion might enhance learners’ attention in e-reading. To verify our assumption, we collected a novel dataset (SKEP) in an e-reading setting with social robot support. It contains 25 multimodal features from various sensors and logged data that are direct and indirect cues of attention. Based on the SKEP dataset, we comprehensively compared the difference between HRI-based (treatment) and GUI-based (control) feedback and obtained insights for intervention design. Based on the human annotation of the nearly 40 hours of video data streams from 60 subjects, we developed a machine learning model to capture attention-regulation behaviors in e-reading. We exploited a two-stage framework to recognize learners’ observable self-regulatory behaviors and conducted attention analysis. The proposed system showed a promising performance with high prediction results of e-reading with HRI, such as 72.97% accuracy in recognizing attention regulation behaviors, 74.29% accuracy in predicting knowledge gain, 75.00% for perceived interaction experience, and 75.00% for perceived social presence. We believe our work can inspire the future design of HRI-based e-reading and its analysis.
Compiling Functions onto Digital Microfluidics

Digital Microfluidic Biochips (DMFBs) have the potential to fundamentally transform biochemical disciplines through automation, miniaturization, and the ability to facilitate repeatable chemical experimentation. Programming DMFBs has historically been accomplished by writing low-level bit manipulations to select which electrodes should activate in sequence. Recent research on high-level programming languages and compilers for DMFBs have begun to address the programmability challenge, but important capabilities such as loading and executing pre-compiled libraries and function calls, are absent from the literature. A primary driver of this oversight is the lack of a memory hierarchy to store physical chemicals off-chip to jump to and from function calls. This paper addresses the complexities involved in compiling function calls within the technology's unique boundaries, and provides a proof-of-concept implementation from language to code generation, with solutions evaluated using a cycle-accurate DMFB simulator as well as physical execution on an open-hardware DMFB.
OpenCilk: A Modular and Extensible Software Infrastructure for Fast Task-Parallel Code

This paper presents OpenCilk, an open-source software infrastructure for task-parallel programming that allows for substantial code reuse and easy exploration of design choices in language abstraction, compilation strategy, runtime mechanism, and productivity-tool development. The OpenCilk infrastructure consists of three main components: a compiler designed to compile fork-join task-parallel code, an efficient work-stealing runtime scheduler, and a productivity-tool development framework based on compiler instrumentation designed for fork-join parallel computations. OpenCilk is modular --- modifying one component for the most part does not necessitate modifications to the other components --- and easy to extend --- its construction naturally encourages code reuse. Despite being modular and easy to extend, OpenCilk produces high-performing code. We investigated OpenCilk's modularity, extensibility, and performance through several case studies, including a study to extend OpenCilk to support multiple parallel runtime systems, including Cilk Plus, OpenMP, and oneTBB. OpenCilk's design enables rapid prototyping of new compiler back ends to target different parallel-runtime ABIs. Each back end required fewer than 2000 new lines of code. We examined the OpenCilk runtime's performance empirically on 15 benchmark Cilk programs and found that it outperforms the other runtimes by a geometric mean of 4%--26% on 1 core and 10%--120% on 48 cores.
METS: Multimodal Learning Analytics of Embodied Teamwork Learning

Embodied team learning is a form of group learning that occurs in co-located settings where students need to interact with others while actively using resources in the physical learning space to achieve a common goal. In such situations, communication dynamics can be complex as team discourse segments can happen in parallel at different locations of the physical space with varied team member configurations. This can make it hard for teachers to assess the effectiveness of teamwork and for students to reflect on their own experiences. To address this problem, we propose METS (Multimodal Embodied Teamwork Signature), a method to model team dialogue content in combination with spatial and temporal data to generate a signature of embodied teamwork. We present a study in the context of a highly dynamic healthcare team simulation space where students can freely move. We illustrate how signatures of embodied teamwork can help to identify key differences between high and low performing teams: i) across the whole learning session; ii) at different phases of learning sessions; and iii) at particular spaces of interest in the learning space.
Instruction-Embedded Assessment for Reading Ability in Adaptive Mathematics Software

Adaptive educational software is likely to better support broader and more diverse sets of learners by considering more comprehensive views (or models) of such learners. For example, recent work proposed making inferences about “non-math” factors like reading comprehension while students used adaptive software for mathematics to better support and adapt to learners. We build on this proposed approach to more comprehensive learning modeling by providing an empirical basis for making inferences about students’ reading ability from their performance on activities in adaptive software for mathematics. We lay out an approach to predicting middle school students’ reading ability using their performance on activities within Carnegie Learning’s MATHia, a widely used intelligent tutoring system for mathematics. We focus on how performance in an early, introductory activity as an especially powerful place to consider instruction-embedded assessment of non-math factors like reading comprehension to guide adaptation based on factors like reading ability. We close by discussing opportunities to extend this work by focusing on particular knowledge components or skills tracked by MATHia that may provide important “levers” for driving adaptation based on students’ reading ability while they learn and practice mathematics.
Cluster-Based Performance of Student Dropout Prediction as a Solution for Large Scale Models in a Moodle LMS

Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS? ” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.
iQAN: Fast and Accurate Vector Search with Efficient Intra-Query Parallelism on Multi-Core Architectures

Vector search has drawn a rapid increase of interest in the research community due to its application in novel AI applications. Maximizing its performance is essential for many tasks but remains preliminary understood. In this work, we investigate the root causes of the scalability bottleneck of using intra-query parallelism to speedup the state-of-the-art graph-based vector search systems on multi-core architectures. Our in-depth analysis reveals several scalability challenges from both system and algorithm perspectives. Based on the insights, we propose iQAN, a parallel search algorithm with a set of optimizations that boost convergence, avoid redundant computations, and mitigate synchronization overhead. Our evaluation results on a wide range of real-world datasets show that iQAN achieves up to 37.7× and 76.6× lower latency than state-of-the-art sequential baselines on datasets ranging from a million to a hundred million datasets. We also show that iQAN achieves outstanding scalability as the graph size or the accuracy target increases, allowing it to outperform the state-of-the-art baseline on two billion-scale datasets by up to 16.0× with up to 64 cores.
Electromagnetic fields in the presence of rotating bodies

Field calculations in the presence of rotating bodies with symmetry of revolution can be performed in the (inertial) laboratory frame of reference. Specific results are presented for a rotating circular cylinder immersed in a plane wave of the E or H type. Particular emphasis is put on the low-frequency limit, but some numerical data are also given for a typical frequency in the "resonance" region. The analysis becomes more complicated in the absence of symmetry of revolution. It is then necessary to solve the problem in a rotating system of coordinates. Maxwell's equations are written in these coordinates, together with the relevant constitutive equations and boundary conditions. The general formalism is applied to a typical two-dimensional configuration, viz., a cylinder immersed in an incident E wave. Considerable simplification obtains if all material velocities are negligible with respect to c, a condition which is always met in practice. Even simpler results are obtained if the cross-sectional dimensions of the cylinder are small with respect to λ. Some numerical results are presented, at low frequencies, for a dielectric cylinder of rectangular cross section.
The State-of-the-Art LCRQ Concurrent Queue Algorithm Does NOT Require CAS2

Concurrent queues are, arguably, one of the most important data structures in high-load applications, which require them to be extremely fast and scalable. Achieving these properties is non-trivial. The early solutions, such as the classic queue by Michael and Scott, store elements in a concurrent linked list. Reputedly, this design is non-scalable and memory-inefficient. Modern solutions utilize the Fetch-and-Add instruction to improve the algorithm's scalability and store elements in arrays to reduce the memory pressure. One of the most famous and fast such algorithms is LCRQ. The main disadvantage of its design is that it relies on the atomic CAS2 instruction, which is unavailable in most modern programming languages, such as Java, Kotlin, or Go, let alone some architectures. This paper presents the LPRQ algorithm, a portable modification of the original LCRQ design that eliminates all CAS2 usages. In contrast, it performs the synchronization utilizing only the standard Compare-and-Swap and Fetch-and-Add atomic instructions. Our experiments show that LPRQ provides the same performance as the classic LCRQ algorithm, outrunning the fastest of the existing solutions that do not use CAS2 by up to 1.6×.
The Student Zipf Theory: Inferring Latent Structures in Open-Ended Student Work To Help Educators

Are there structures underlying student work that are universal across every open-ended task? We demonstrate that, across many subjects and assignment types, the probability distribution underlying student-generated open-ended work is close to Zipf’s Law. Inferring this latent structure for classroom assignments can help learning analytics researchers, instruction designers, and educators understand the landscape of various student approaches, assess the complexity of assignments, and prioritise pedagogical attention. However, typical classrooms are way too small to witness even the contour of the Zipfian pattern, and it is generally impossible to perform inference for Zipf’s law from such small number of samples. We formalise this difficult task as the Zipf Inference Challenge: (1) Infer the ordering of student-generated works by their underlying probabilities, and (2) Estimate the shape parameter of the underlying distribution in a typical-sized classroom. Our key insight in addressing this challenge is to leverage the densities of the student response landscapes represented by semantic similarity. We show that our “Semantic Density Estimation” method is able to do a much better job at inferring the latent Zipf shape and the probability-ordering of student responses for real world education datasets.
Moral Machines or Tyranny of the Majority? A Systematic Review on Predictive Bias in Education

Machine Learning (ML) techniques have been increasingly adopted to support various activities in education, including being applied in important contexts such as college admission and scholarship allocation. In addition to being accurate, the application of these techniques has to be fair, i.e., displaying no discrimination towards any group of stakeholders in education (mainly students and instructors) based on their protective attributes (e.g., gender and age). The past few years have witnessed an explosion of attention given to the predictive bias of ML techniques in education. Though certain endeavors have been made to detect and alleviate predictive bias in learning analytics, it is still hard for newcomers to penetrate. To address this, we systematically reviewed existing studies on predictive bias in education, and a total of 49 peer-reviewed empirical papers published after 2010 were included in this study. In particular, these papers were reviewed and summarized from the following three perspectives: (i) protective attributes, (ii) fairness measures and their applications in various educational tasks, and (iii) strategies for enhancing predictive fairness. These findings were summarized into recommendations to guide future endeavors in this strand of research, e.g., collecting and sharing more quality data containing protective attributes, developing fairness-enhancing approaches which do not require the explicit use of protective attributes, validating the effectiveness of fairness-enhancing on students and instructors in real-world settings.
Precise genome editing with base editors

Abstract Single-nucleotide variants account for about half of known pathogenic genetic variants in human. Genome editing strategies by reversing pathogenic point mutations with minimum side effects have great therapeutic potential and are now being actively pursued. The emerge of precise and efficient genome editing strategies such as base editing and prime editing provide powerful tools for nucleotide conversion without inducing double-stranded DNA breaks (DSBs), which have shown great potential for curing genetic disorders. A diverse toolkit of base editors has been developed to improve the editing efficiency and accuracy in different context of application. Here, we summarized the evolving of base editors (BEs), their limitations and future perspective of base editing-based therapeutic strategies.
Diabetes disease prediction system using HNB classifier based on discretization method

Abstract Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.
DSP: Efficient GNN Training with Multiple GPUs

Jointly utilizing multiple GPUs to train graph neural networks (GNNs) is crucial for handling large graphs and achieving high efficiency. However, we find that existing systems suffer from high communication costs and low GPU utilization due to improper data layout and training procedures. Thus, we propose a system dubbed Distributed Sampling and Pipelining (DSP) for multi-GPU GNN training. DSP adopts a tailored data layout to utilize the fast NVLink connections among the GPUs, which stores the graph topology and popular node features in GPU memory. For efficient graph sampling with multiple GPUs, we introduce a collective sampling primitive (CSP), which pushes the sampling tasks to data to reduce communication. We also design a producer-consumer-based pipeline, which allows tasks from different mini-batches to run congruently to improve GPU utilization. We compare DSP with state-of-the-art GNN training frameworks, and the results show that DSP consistently outperforms the baselines under different datasets, GNN models and GPU counts. The speedup of DSP can be up to 26x and is over 2x in most cases.
Inference and the introductory statistics course

This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its hypothetical probabilistic reasoning process is examined in some depth. We argue that the revolution in the teaching of inference must begin. We also discuss some perplexing issues, problematic areas and some new insights into language conundrums associated with introducing the logic of inference through randomization methods.
An Evaluation of Econometric Models of Adaptive Learning

This paper evaluates the effectiveness of four econometric approaches intended to identify the learning rules being used by subjects in experiments with normal form games. This is done by simulating experimental data and then estimating the econometric models on the simulated data to determine if they can correctly identify the rule that was used to generate the data. The results show that all of the models examined possess difficulties in accurately distinguishing between the data generating processes. Copyright The Econometric Society.
On local and zonal pulses of atmospheric heat transport in reanalysis data

The present study analyses large values (or pulses) of local and zonally integrated meridional atmospheric heat transport due to transient eddies. The data used is the European Centre for Medium‐range Weather Forecasts ERA‐Interim reanalysis with daily, 0.7° latitude and longitude resolution. The domain of interest is the extratropics. First, the circulation associated with local pulses of heat transport is described. This is found to match many of the features found in warm conveyor belts, although important regional differences exist. The large values of heat transport are seen to be associated with co‐varying meridional velocity and moist static energy anomalies. Next, it is shown that there exist strong pulses of meridional heat transport when a zonal integral around a given latitude circle is considered. These zonal pulses are only partly driven by the synchronized occurrence of a large number of local pulses. The existence of such pronounced variability in zonally integrated meridional heat transport can have important consequences for the energy balance of the high latitudes.