Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Neuromorphic Circuit in Reconfigurable Technology for the Simulation of Synapses

In last decades, neuromorphic circuits have received widespread attention across various scientific fields. Such circuits mathematically model the behaviour of biological neurons, synapses as well as their interaction. This work implements a neuromorphic synapse on an FPGA board and it improves previously proposed synapses in terms of performance and synchronization to novel neuron implementations. The proposed architecture is designed with the goal of being compatible with neuromorphic neurons based on the mathematical equations of the Izhikevich neuron model. The implementation consists of two computation cores; one core is responsible for computing the update of currents and the second core is computing the exponential decays of currents. Compared to similar neuromorphic synapses, the proposed retains low complexity and can calculate the needed synaptic currents of the connected neurons quickly and reliably. The speed of computation achieved by the parallel execution of instructions indicates that the system can function in real time.
ProSE: the architecture and design of a protein discovery engine

Protein language models have enabled breakthrough approaches to protein structure prediction, function annotation, and drug discovery. A primary limitation to the widespread adoption of these powerful models is the high computational cost associated with the training and inference of these models, especially at longer sequence lengths. We present the architecture, microarchitecture, and hardware implementation of a protein design and discovery accelerator, ProSE (Protein Systolic Engine). ProSE has a collection of custom heterogeneous systolic arrays and special functions that process transfer learning model inferences efficiently. The architecture marries SIMD-style computations with systolic array architectures, optimizing coarse-grained operation sequences across model layers to achieve efficiency without sacrificing generality. ProSE performs Protein BERT inference at up to 6.9× speedup and 48× power efficiency (performance/Watt) compared to one NVIDIA A100 GPU. ProSE achieves up to 5.5 × (12.7×) speedup and 173× (249×) power efficiency compared to TPUv3 (TPUv2).
Bin Packing Heuristics for the Multiple Workflow Scheduling Problem

In the multiple workflow scheduling problem a set of workflows has to be scheduled concurrently onto system’s available resources. Workflows exhibit different characteristics e.g., topological structure, size and computation-communication demands while they can have different or conflicting optimization goals. The above results in scheduling decisions of high complexity which in turn may adversely affect the quality of solutions. In this paper we present a fast scheduling algorithm (i.e., the Multiple Workflow Complementary Packing algorithm, MWCP algorithm) for the management of multiple workflows. MWCP combines list scheduling methodologies and Bin Packing techniques to minimize the overall execution time of the workflows. For each workflow the scheduler decides on the best policy considering only the information provided by the workflow in question. We evaluate the performance of the proposed algorithm using real workflow applications being tested under different system heterogeneity levels. Results indicate that performance gains over existing studies are up to 9% while different workflow characteristics reveal different trade-offs on the performance of MWCP.
DAGguise: mitigating memory timing side channels

This paper studies the mitigation of memory timing side channels, where attackers utilize contention within DRAM controllers to infer a victim’s secrets. Already practical, this class of channels poses an important challenge to secure computing in shared memory environments. Existing state-of-the-art memory timing side channel mitigations have several key performance and security limitations. Prior schemes require onerous static bandwidth partitioning, extensive profiling phases, or simply fail to protect against attacks which exploit fine-grained timing and bank information. We present DAGguise, a defense mechanism which fully protects against memory timing side channels while allowing for dynamic traffic contention in order to achieve good performance. DAGguise utilizes a novel abstract memory access representation, the Directed Acyclic Request Graph (rDAG for short), to model memory access patterns which experience contention. DAGguise shapes a victim’s memory access patterns according to a publicly known rDAG obtained through a lightweight profiling stage, completely eliminating information leakage. We formally verify the security of DAGguise, proving that it maintains strong security guarantees. Moreover, by allowing dynamic traffic contention, DAGguise achieves a 12% overall system speedup relative to Fixed Service, which is the state-of-the-art mitigation mechanism, with up to a 20% relative speedup for co-located applications which do not require protection. We further claim that the principles of DAGguise can be generalized to protect against other types of scheduler-based timing side channels, such as those targeting on-chip networks, or functional units in SMT cores.
Assessing Workload Perception in Introductory Computer Science Projects using NASA-TLX

Introductory computer science courses are characterized by difficulty, which may contribute to the low success rate, diversity, and retention in these key courses. Difficulty in programming projects was found to result in negative self-efficacy perception among students, in addition to affecting underrepresented students disproportionately. In this paper, we focus on perceived workload in introductory computer science projects and report on the use of NASA Task Load Index (NASA-TLX) as a subjective measure of student workload. Through two experiments involving six CS1 and CS2 courses, we demonstrate how NASA-TLX can be used to gain insights on the contributors and components of workload in programming projects. We show how when combined with race/ethnicity and gender data, NASA-TLX is useful in understanding the experience of underrepresented students. Our results suggest that perceived workload is only partially influenced by actual workload as measured in lines of code, function, and class count. Moreover, we found that the time spent on programming projects in comparison to other courses is a predictor of perceived workload. Finally, we discuss how educators can use NASA-TLX to identify at-risk students and make difficult projects more accessible without sacrificing quality.
Efficiently detecting concurrency bugs in persistent memory programs

Due to the salient DRAM-comparable performance, TB-scale capacity, and non-volatility, persistent memory (PM) provides new opportunities for large-scale in-memory computing with instant crash recovery. However, programming PM systems is error-prone due to the existence of crash-consistency bugs, which are challenging to diagnose especially with concurrent programming widely adopted in PM applications to exploit hardware parallelism. Existing bug detection tools for DRAM-based concurrency issues cannot detect PM crash-consistency bugs because they are oblivious to PM operations and PM consistency. On the other hand, existing PM-specific debugging tools only focus on sequential PM programs and cannot effectively detect crash-consistency issues hidden in concurrent executions. In order to effectively detect crash-consistency bugs that only manifest in concurrent executions, we propose PMRace, the first PM-specific concurrency bug detection tool. We identify and define two new types of concurrent crash-consistency bugs: PM Inter-thread Inconsistency and PM Synchronization Inconsistency. In particular, PMRace adopts PM-aware and coverage-guided fuzz testing to explore PM program executions. For PM Inter-thread Inconsistency, which denotes the data inconsistency hidden in thread interleavings, PMRace performs PM-aware interleaving exploration and thread scheduling to drive the execution towards executions that reveal such inconsistencies. For PM Synchronization Inconsistency between persisted synchronization variables and program data, PMRace identifies the inconsistency during interleaving exploration. The post-failure validation reduces the false positives that come from custom crash recovery mechanisms. PMRace has found 14 bugs (10 new bugs) in real-world concurrent PM systems including PM-version memcached.
Randomized row-swap: mitigating Row Hammer by breaking spatial correlation between aggressor and victim rows

Row Hammer is a fault-injection attack in which rapid activations to a single DRAM row causes bit-flips in nearby rows. Several recent defenses propose tracking aggressor-rows and applying mitigating action on neighboring victim rows by refreshing them. However, all such proposals using victim-focused mitigation preserve the spatial connection between victim and aggressor rows. Therefore, these proposals are susceptible to access patterns causing bit-flips in rows beyond the immediate neighbor. For example, the Half-Double attack causes bit-flips in the presence of victim-focused mitigation. We propose Randomized Row-Swap (RRS), a novel mitigation action that breaks the spatial connection between the aggressor and victim DRAM rows. This enables RRS to provide robust defense against even complex Row Hammer access patterns. RRS is an aggressor-focused mitigation that periodically swaps aggressor-rows with other randomly selected rows in memory. This limits the possible damage in any one locality within the DRAM memory. While RRS can be used with any tracking mechanism, we implement it with a Misra-Gries tracker and target a Row Hammer Threshold of 4.8K activations (similar to the state-of-the-art attacks). Our evaluations show that RRS has negligible slowdown (0.4% on average) and provides strong security guarantees for avoiding Row Hammer bit flips even under several years of continuous attack.
Who Belongs in Computer Science?

Women's participation rates in the Computer Science (CS) field are alarmingly lower than men's in K-12 and higher education as well as in the workforce. Research has been done to explore when young women's potential interest in the field typically develops, but these studies primarily target the high school and undergraduate levels. This mixed methods study examined middle school students' perceptions of computer scientists' looks and character traits, students' sense of belonging in the CS community, and the relationship between stereotype adoption, gender, and sense of belonging. Ninety-five (95) participants depicted computer scientists through drawing and a written explanation of their depiction. A subset of participants also had their sense of belonging in CS measured through the CS Sense of Belongingness Scale. A 2 (gender) x 2 (stereotype adoption) ANOVA was used to examine the relationships between students' perceptions, their sense of belonging, and gender. Results from the study suggest that there were significant differences between girls' and boys' perceptions of computer scientists' appearance; however, there were no differences in their perceptions of computer scientists' character traits. There were also no differences in students' sense of belonging based on the stereotype adoption levels and gender. Despite its limited sample size, this study provides insights on how middle school students think about computer scientists and the stereotypes they hold.
Exploring Design Choices to Support Novices' Example Use During Creative Open-Ended Programming

Open-ended programming engages students by connecting computing with their real-world experience and personal interest. However, such open-ended programming tasks can be challenging, as they require students to implement features that they may be unfamiliar with. Code examples help students to generate ideas and implement program features, but students also encounter many learning barriers when using them. We explore how to design code examples to support novices' effective example use by presenting our experience of building and deploying Example Helper, a system that supports students with a gallery of code examples during open-ended programming. We deployed Example Helper in an undergraduate CS0 classroom to investigate students' example usage experience, finding that students used different strategies to browse, understand, experiment with, and integrate code examples, and that students who make more sophisticated plans also used more examples in their projects.
A scoping review of Future Skills frameworks

ABSTRACT Many agree that education needs new goals that reflect the demands of the future. These are often called ‘Future Skills’, referring to the knowledge, attitudes, values, skills, and competencies intended to prepare learners for the future. The need to teach such Future Skills is often cited, justified by the perception that the future will present new challenges for society. However, the various frameworks discussing Future Skills, often created and published without consensus, use hundreds of terms to refer to such skills and competencies, presenting a barrier to discussion of education futures. If we are to design a better future for education, then a cohesive analysis must link and synthesise these isolated frameworks published worldwide. This scoping review utilises thematic analysis and Social Network Analysis to develop meta-categories representing clusters of future skills reported by extant research. Having started with 99 frameworks identified following a systematic search of the literature, which together included 341 different terms, our review identifies nine categories that provide a valuable overview of the field to inform the conceptualisation of Future Skills. Educational practitioners, human resource professionals, policy makers, and educational technology developers can use the meta-categories to prioritise the integration of certain skills into teaching, learning, and retraining. This will help ensure that students and professionals are better prepared to thrive in an uncertain future.
scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data

Motivation Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, dropout information is not explicitly used by any current cell annotation method. Fully utilizing dropout information for cell type annotation motivated this work. Results We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using fourteen real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate’s misclassified cells are very different from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy. Availability We implemented scAnnotate as an R package and made it publicly available from CRAN. Contact Xuekui Zhang: xuekui@uvic.ca and Li Xing: li.xing@math.usask. ca
Latitudinal cline does not predict variation in the microbiome of wild Drosophila melanogaster

Microbiomes affect many aspects of host biology, but the eco-evolutionary forces that shape their diversity in natural populations remain poorly understood. Geographic gradients, like latitudinal clines, generate predictable patterns in biodiversity at macroecological scales, but whether these macro-scale processes apply to host-microbiome interactions is an open question. To address this question, we sampled the microbiomes of 13 natural populations of Drosophila melanogaster along a latitudinal cline in eastern United States. The microbiomes were surprisingly consistent across the cline–latitude did not predict either alpha or beta diversity. Only a narrow taxonomic range of bacteria were present in all microbiomes, indicating that strict taxonomic filtering by the host and neutral ecological dynamics are the primary factors shaping the fly microbiome. Additional temporal sampling across two independent sites revealed significant differences in microbial communities over time, suggesting that local environmental differences that vary at fine spatiotemporal scales are more likely to shape the microbiome. Our findings reveal the complexity of eco-evolutionary interactions shaping microbial variation in D. melanogaster and highlight the need for additional sampling of the microbiomes in natural populations along environmental gradients.
Keratinocyte-Associated Protein 3 is a novel gene for adiposity with differential effects in males and females

Objective Despite the obesity crisis in the United States, the underlying genetics are poorly understood. Our lab previously identified Keratinocyte-associated protein 3, Krtcap3, as a candidate gene for adiposity where increased expression of Krtcap3 correlated with decreased fat mass. Here we seek to confirm that Krtcap3 expression affects adiposity traits. Methods We developed an in vivo whole-body Krtcap3 knock-out (KO) rat model. Wild-type (WT) and KO rats were placed onto a high-fat or low-fat diet at six weeks of age and were maintained on diet for 13 weeks, followed by assessments of metabolic health. We hypothesized that Krtcap3-KO rats will have increased adiposity and a worsened metabolic phenotype relative to WT. Results We found that KO male and female rats have significantly increased body weight versus WT. KO females ate more, had more fat mass, but were also more insulin sensitive than WT. Alternatively, KO males weighed more and were more insulin resistant than WT, with no differences in eating or fat mass. Conclusions This study validates Krtcap3 in body weight regulation and demonstrates sex-specific effects on food intake, adiposity, and insulin sensitivity. Future studies will investigate how Krtcap3 is acting and seek to better understand these sex differences. Study Importance Questions What is already known about this subject? Over 900 low-risk, common genetic variants for BMI have been identified, but these still only explain a fraction of the heritability and many of the underlying causal genes remain unknown Krtcap3 has been identified as a candidate gene for obesity in both rats and humans, but no verification or functional studies have been done What are the new findings in your manuscript? Identified Krtcap3 as a novel gene that impacts feeding behavior and adiposity in female rats Determined that Krtcap3 impacts insulin sensitivity differentially in male and female rats How might your results change the direction of research or the focus of clinical practice? This work may lead to identification of new pathways that contribute to obesity without metabolic complications, which will advance understanding of the biology of obesity and potentially identify novel drug targets This work highlights the need to investigate sex differences in the genetics of obesity
Modulation scaling for energy efficiency

The energy consumption of wireless communicating devices as well as their autonomy are two important factors in energy constrained system such asWireless Sensor Networks (WSNs). The spectral efficiency is another important metric. The main goal is to study the trade-off between the energy consumption of the systemand it’s spectral efficiency when varying system parameters. In this paper, an uncoded system using M-ary Quadrature Amplitude Modulation (M-QAM) in which the spectral efficiency is assumed proportional to its bandwidth efficiency is used. In order to find the balance between spectral and energy requirements, the considered system parameters are studied. First, a general and comprehensive power model for both transmitter and receiver is developed. Next, by considering Additive White Gaussian Noise (AWGN) channel for point-to-point communication, the total energy consumption is evaluated. A new approximation for the Bit Error Rate (BER) is adopted. Then, the effect of communication distance, bandwidth and circuit power consumption on the optimum spectral efficiency is studied. For a fixed BER, we demonstrate that for each of those parameters, an optimumspectral efficiency can be found.
Experimental Climate Warming Reduces Floral Resources and Alters Insect Visitation and Wildflower Seed Set in a Cereal Agro-Ecosystem

Declines in pollinating insects and wildflowers have been well documented in recent years. Climate change is an emerging threat to insect pollinators and their food plants, but little is known about how whole communities of interacting species will be affected or what impacts there may be on ecosystem services such as pollination. Using a novel open-air field experiment, we simulated an increase in temperature of 1.5°C and rainwater of 40% for two growing seasons to investigate how climate change may impact several within-field features of temperate arable agro-ecosystems: (1) wildflower floral resources; (2) insect visitation; (3) flower-visitor network structure; and (4) wildflower seed set. Experimental warming reduced total floral abundance by nearly 40%, and nectar volumes by over 60% for two species. The species richness of the visiting insects and flowering plants (dominated by annuals) were unaffected by warming, and while a negative impact on visitor abundance was observed, this effect appears to have been mediated by different community compositions between years. Warming increased the frequency of visits to flowers and the complexity of the flower-visitor interaction networks. Wildflower seed set was reduced in terms of seed number and/or weight in four of the five species examined. Increased rainwater did not ameliorate any of these effects. These findings demonstrate the adverse impacts that climate warming might have on annual wildflowers in arable systems and the pollinating insects that feed on them, highlighting several mechanisms that could drive changes in community composition over time. The results also reveal how cascading impacts within communities can accumulate to affect ecosystem functioning.
A Study on Second Harmonic Excitation in Electron Beam–Plasma Instability

The electron beam–plasma interaction is a fundamental nonlinear plasma process that is frequently taking place in space and laboratory plasmas. Such an interaction is found to generate electromagnetic waves observed in space plasmas. Using the two-dimensional electromagnetic particle in cell simulations, we investigated the second harmonic excitation in the electron beam–plasma instability. Results showed that, first, the Langmuir waves are excited in the system; then at longer time scales, following the excitation of ion acoustic waves, the second harmonic electromagnetic waves are excited. We found that in the case of background pair plasmas, where the masses are the same, there is no signature of waves in the second harmonic, which is a direct verification of the three-wave coupling mechanism for the generation of electromagnetic waves in the second harmonic.