Miracle-dz/UniFAR-FTUs huggingface dataset:
MESA: Microarchitecture Extensions for Spatial Architecture Generation

Modern heterogeneous CPUs incorporate hardware accelerators to enable domain-specialized execution and achieve improved efficiency. A well-known class among them, spatial accelerators, are designed with reconfigurability to accelerate a wide range of compute-heavy and data-parallel applications. Unlike CPU cores, however, they tend to require specialized compilers and software stacks, libraries, or languages to operate and cannot be utilized with ease by all applications. As a result, the accelerator's large pool of compute and memory resources sit wastefully idle when it is not explicitly programmed. Our goal is to dismantle this CPU-accelerator barrier by monitoring CPU threads for acceleration opportunities during execution and, if viable, dynamically reconfigure the accelerator to allow transparent offloading. We develop MESA (Microarchitecture Extensions for Spatial Architecture Generation), a hardware block on the CPU that translates machine code to build an accelerator configuration specialized for the running program. While such a dynamic translation/reconfiguration approach is challenging, it has a key advantage over ahead-of-time compilers: access to runtime information, revealing not only dynamic dependencies but also performance characteristics. MESA maintains a real-time performance model of the program mapped on the accelerator in the form of a spatial dataflow graph with nodes weighted by operation latency and edges weighted by data transfer latency. Features of this dataflow graph are continuously updated with runtime information captured by performance counters, allowing a feedback loop of optimization, reconfiguration, and acceleration. This performance model allows MESA to identify the accelerator's critical paths and pinpoint its bottlenecks, upon which we implement in hardware a data-driven instruction mapping algorithm that locally minimizes latency. Backed by a synthesized RTL implementation, we evaluate the feasibility of our microarchitectural solution with different accelerator configurations. Across the Rodinia benchmarks, results demonstrate an average 1.3× speedup in performance and 1.8× gain in energy efficiency against a multicore CPU baseline.
SmartShell: A Near-Field Reflective Surface Enhancing RSS

Reconfigurable reflective arrays can be used to program the radio propagation environment in order to form favorable wireless channel conditions. Previous designs have used large-scale arrays containing hundreds to thousands of reflecting elements located external to the receiving node, with the reflection coefficients of all array elements managed by a controller. However, these designs can be costly to deploy and are challenging to quickly adapt to the time-varying nature of wireless channels caused by mobility. In this paper, we propose using a small-sized and low-cost reflective surface attached to the mobile device, called SmartShell, to program the micro-environment of radio propagation near the device. We provide a systematic analysis of the near-field reflective surface, laying the theoretical foundation for the feasibility and effectiveness of SmartShell. We then present insight into controlling the surface units of SmartShell, showing that this is fundamentally different from existing far-field designs and can be formulated as a binary quadratic programming (BQP) problem, with performance bounds derived. We develop a prototype of the SmartShell system and conduct comprehensive experiments. Results show that the SmartShell can increase the received signal strength (RSS) of the wireless node by 5--10 dB, which is comparable to far-field large-scale reflective surfaces but at a much lower cost.
The Effect of Similarity Metric and Group Size on Outlier Selection & Satisfaction in Group Recommender Systems

Group recommender systems (GRS) are a specific case of recommender systems (RS), where recommendations are constructed to a group of users rather than an individual. GRS has diverse application areas including trip planning, recommending movies to watch together, or music in shared environments. However, due to the lack of large datasets with group decision-making feedback information, or even the group definitions, GRS approaches are often evaluated offline w.r. t. individual user feedback and artificially generated groups. These synthetic groups are usually constructed w.r. t. pre-defined group size and inter-user similarity metric. While numerous variants of synthetic group generation procedures were utilized so far, its impact on the evaluation results was not sufficiently discussed. In this paper, we address this research gap by investigating the impact of various synthetic group generation procedures, namely the usage of different user similarity metrics and the effect of group sizes. We consider them in the context of “outlier vs. majority” groups, where a group of similar users is extended with one or more diverse ones. Experimental results indicate a strong impact of the selected similarity metric on both the typical characteristics of selected outliers as well as the performance of individual GRS algorithms. Moreover, we show that certain algorithms better adapt to larger groups than others.
Instructors’ Social Presence and Intention of Using a Speaking application with ASR: An extension of the Technology Acceptance Model

This study intends to explore variables that affect the voluntary use of Speaking applications with automatic speech recognition (ASR) in the context of FIF by incorporating Instructors’ Social Presence as an antecedent and experience as a moderator with Technology Acceptance Model. FIF has been widely confirmed to improve learners’ Spoken English in China. A total number of Seventy students from three classes voluntarily enrolled in this study, correspondingly divided into Two Treatment Groups (N=22/27) and Control Group (N=20). They were exposed to different instructional means, including the teachers’ recommendations to the use of FIF, acknowledgement of students’ performance in the chatting group, and giving comments and feedback on students’ performance via FIF. Sixty-nine valid data were retained and analyzed by Smart-PLS. The findings demonstrate that Perceived Usefulness is the only predictor determining the adoption of new technology and significantly influenced by Perceived Ease of Use. However, the impact of Perceived Ease of Use on Behavior Intention is negative, though not significantly. In addition, a positively weak correlation does exist between Social Presence of Instructors and Behavioral Intention, but no significant difference among the three groups has been found. Moreover, Social Presence of Instructors’ impact on Perceived Usefulness becomes stronger with greater experience, while its effect on Behavioral intention neither weakens nor strengthens over time. The findings provide educational implications for instructors and ASR developers in digitalization.
Dynamics in the assembly of the 30S ribosomal subunit investigated by coarse-grained simulations

The ribosome is a large biomolecular complex responsible for protein synthesis. In Escherichia coli (E. coli), a complete ribosome is composed of a 30S small subunit and a 50S large subunit. For about half a century, the 30S subunit has been a key model system for studying the in vitro assembly of the ribosome, and an assembly map has been proposed. However, structural details in the assembly of this protein-RNA complex remain elusive. In this paper, we have conducted a series of coarse-grained simulations following the order of the assembly map, in order to investigate conformational dynamics during the assembly process of the 30S subunit. It has been found that, the tertiary structure of the naked 16S rRNA is very unstable, and that is the case after binding of the early-assembly proteins. The mid-assembly proteins can significantly restrict the mobility of the 16S rRNA and make the latter close to the native structure. The final binding of the late-assembly proteins would fully obtain the collective motion of the 16S rRNA. In particular, proteins S9 and S3 may have more important contributions to the assembly of the 30S subunit than other S proteins. Our strategy of coarse-grained simulations can be generally used to study assembly dynamics of large biomolecular complexes as long as the assembly map is available.
Susceptibility of Online Users to Persuasive Strategies to Curb the Spread of Misinformation

The spread of misinformation in online social media is a cause of concern to stakeholders. With many people going online for information about many aspects of their lives, any misinformation presented online can have detrimental effects on those that consume it. The use of persuasive strategies to curb misinformation online is an ongoing research area. There is little or no knowledge of how persuasive strategies can be applied to the different types of social media users to curb the spread of misinformation. To contribute to research in this area, we present the preliminary results of an ongoing user study of currently 113 social media users which investigates which type of social media users will likely spread misinformation and what persuasive strategies will likely influence the different types of social media users. We developed and tested a global model using structural equation modelling. Our results suggest that people that use social media because of friendship and role-playing are influenced by social proof while people who use social media to seek for relationships are influenced by liking and are likely to spread misinformation in the future.
Assessment of Mental Workloads using Differential Dermal Potentials Recorded from in and around Ear

Differential dermal potentials is a non-invasive bio-potential signal acquired from the skin surface is a relatively new mode for human activity and behavior monitoring. Literature suggests acquiring differential dermal potentials from either hand or leg, which might be uncomfortable for long term usage. In the present work, for the first time we have acquired the differential dermal potentials from in and around the ear and have used that for monitoring mental workload. Experimental data were acquired from ear lobes and ear canals using two different devices while the participants were engaged in a mental arithmetic task. Thereafter, the signals are analyzed and a set of the most discriminating features are identified. Using these features, a cognitive load prediction model is developed. Results show that, for rest vs. load classification, we achieved an accuracy of 89% for ear canal data and 94.2% for ear lobe data. We also observed a positive co-occurrence of signals collected from these two locations. Thus, our proposed approach can be used for monitoring mental workload condition in real-life applications and is suitable for long term usage.
Brief Announcement: Improved, Partially-Tight Multiplicity Queue Lower Bounds

A multiplicity queue is a concurrently-defined data type which relaxes the conditions of a linearizable FIFO queue by allowing concurrent Dequeue instances to return the same value. It would seem that this should allow faster message-passing implementations, as processes should not need to wait as long to learn about concurrent operations and previous work has shown that multiplicity queues are computationally less complex than the unrelaxed version. Intriguingly, recent work has shown that there is, in fact, little possible speedup versus an unrelaxed queue. Seeking to understand this difference between intuition and real behavior, we increase the lower bound for uniform algorithms. Further, we outline a path toward building proofs for even higher lower bounds, hypothesizing that the worst-case time to Dequeue approaches maximum message delay, which is similar to the time required for an unrelaxed Dequeue. We also give an upper bound for a special case to show that our bounds are tight at that point. To achieve our lower bounds, we use extended shifting arguments, which have been rarely used but allow larger lower bounds than traditional shifting arguments. We use these in series of inductive indistinguishability proofs which allow us to extend our proofs beyond the usual limitations of shifting arguments. This proof structure is an interesting contribution independently of the main result, as developing new lower bound proof techniques may have many uses in future work.
Towards Attack Detection in Multimodal Cyber-Physical Systems with Sticky HDP-HMM based Time Series Analysis

Automatic detection of the precise occurrence and duration of an attack reflected in time-series logs generated by cyber-physical systems is a challenging problem. This problem is exacerbated when performing this analysis using logs with limited system information. In a realistic scenario, multiple and differing attack methods may be employed in rapid succession. Modern or legacy systems operate in multiple modes and contain multiple devices recording a variety of continuous and categorical data streams. This work presents a non-parametric Bayesian framework that addresses these challenges using the sticky Hierarchical Dirichlet Process Hidden Markov Model (sHDP-HMM). Additionally, we explore metrics for measuring the accuracy of the detected events: their timings and durations and compares the computational efficiency of different inference implementations of the model. The efficacy of attack detection is demonstrated in two settings: an avionics testbed and a consumer robot.
Interaction of genital microbiota in infertile couples

Bacteria colonise most of the human body and the genital tract is not an exception. While it has been known for decades that a vaginal microbiota exists, other genital sites have traditionally been viewed as sterile environments, with bacterial presence associated only with pathological conditions. However, recent studies identified specific patterns of bacterial colonisation in most genital sites. Shifts in the bacterial colonisation of the female genital tract have been linked to impairment of reproduction and adverse pregnancy outcomes, such as preterm birth. The goal of this project is to understand the association between the genital microbiota of couples seeking assisted procreation aid and the outcome of this treatment. Male and female partners will be studied as a unit (“couple microbiota”) and the interaction between their microbiota will be evaluated. We have characterised microbial samples coming from vaginal and penile swabs, as well as follicular fluid and semen, using next generation sequencing (16S rRNA profiling). The results were linked to clinical data of the patients included in the study and particularly to the results of the fertility treatment process. With this project, we aim to gain a better understanding of how the male genital microbiota could influence the lower (vagina) and upper (follicular fluid) female genital tracts.
SARS-CoV-2 Envelope protein alters calcium signaling via SERCA interactions

The clinical management of severe COVID-19 cases is not yet well resolved. Therefore, it is important to identify and characterize cell signaling pathways involved in virus pathogenesis that can be targeted therapeutically. Envelope (E) protein is a structural protein of the virus, which is known to be highly expressed in the infected host cell and is a key virulence factor, however, its role is poorly characterized. The E protein is a single-pass transmembrane protein that can assemble into a pentamer forming a viroporin, perturbing Ca2+ homeostasis. Because it is structurally similar to regulins such as, for example, phospholamban, that regulate the sarco/endoplasmic reticulum calcium ATPases (SERCA), we investigated whether the SARS-CoV-2 E protein affects the SERCA system as an exoregulin. Using FRET experiments we demonstrate that E protein can form oligomers with regulins, and thus can alter the monomer/multimer regulin ratio and consequently influence their interactions with SERCAs. We also confirmed that a direct interaction between E protein and SERCA2b results in a decrease in SERCA-mediated ER Ca2+ reload. Structural modeling and molecular dynamics of the complexes indicates an overlapping interaction site for E protein and endogenous regulins. Our results reveal novel links in the host-virus interaction network that play an important role in viral pathogenesis and may provide a new therapeutic target for managing severe inflammatory responses induced by SARS-CoV-2.
Biophysical evolution of the receptor binding domains of SARS-CoVs

With hundreds of coronaviruses (CoVs) identified in bats that are capable of infecting humans, it is important to understand how CoVs that affected the human population have evolved. Seven known coronaviruses have infected humans, of which three CoVs caused severe disease with high mortality rates: SARS-CoV emerged in 2002, MERS-CoV in 2012, and SARS-CoV-2 in 2019. Both SARS-CoV and SARS-CoV-2 belong to the same family, follow the same receptor pathway, and use their receptor binding domain (RBD) of spike protein to bind to the ACE2 receptor on the human epithelial cell surface. The sequence of the two RBDs is divergent, especially in the receptor binding motif (RBM) that directly interacts with ACE2. We probed the biophysical differences between the two RBDs in terms of their structure, stability, aggregation, and function. Since RBD is being explored as an antigen in protein subunit vaccines against CoVs, determining these biophysical properties will also aid in developing stable protein subunit vaccines. Our results show that despite RBDs having a similar three-dimensional structure, they differ in their thermodynamic stability. RBD of SARS-CoV-2 is significantly less stable than that of SARS-CoV. Correspondingly, SARS-CoV-2 RBD shows a higher aggregation propensity. Regarding binding to ACE2, less stable SARS-CoV-2 RBD binds with a higher affinity than more stable SARS-CoV RBD. In addition, SARS-CoV-2 RBD is more homogenous in terms of its binding stoichiometry towards ACE2, compared to SARS-CoV RBD. These results indicate that SARS-CoV-2 RBD differs from SARS-CoV RBD in terms of its stability, aggregation, and function, possibly originating from the diverse RBMs. Higher aggregation propensity and decreased stability of SARS-CoV-2 RBD warrants further optimization of protein subunit vaccines that use RBD as an antigen either by inserting stabilizing mutations or formulation screening. Statement of Significance This study holds significant relevance in the context of the COVID-19 pandemic and the broader understanding of coronaviruses. A comparison of the receptor binding domains (RBDs) of SARS-CoV and SARS-CoV-2 reveals significant differences in their structure, stability, aggregation, and function. Despite divergent sequences, the RBDs share a similar fold and ACE2 receptor binding capability, likely through convergent evolution. These findings are crucial for understanding coronavirus evolution, interactions with human receptors, and the spillover of coronaviruses from animals to humans. The study also has implications for vaccine design strategies for SARS-CoVs, where the RBD is used as an antigen in protein subunit vaccines. By anticipating future outbreaks and enhancing our understanding of zoonotic spillover, this research contributes to safeguarding human health.
ETTE: Efficient Tensor-Train-based Computing Engine for Deep Neural Networks

Tensor-train (TT) decomposition enables ultra-high compression ratio, making the deep neural network (DNN) accelerators based on this method very attractive. TIE, the state-of-the-art TT based DNN accelerator, achieved high performance by leveraging a compact inference scheme to remove unnecessary computations and memory access. However, TIE increases memory costs for stage-wise intermediate results and additional intra-layer data transfer, leading to limited speedups even the models are highly compressed. To unleash the full potential of TT decomposition, this paper proposes ETTE, an algorithm and hardware co-optimization framework for Efficient Tensor-Train Engine. At the algorithm level, ETTE proposes new tensor core construction and computation ordering mechanism to reduce stage-wise computation and storage cost at the same time. At the hardware level, ETTE proposes a lookahead-style across-stage processing scheme to eliminate the unnecessary stage-wise data movement. By fully leveraging the decoupled input and output dimension factors, ETTE develops an efficient low-cost memory partition-free access scheme to efficiently support the desired matrix transformation. We demonstrate the effectiveness of ETTE via implementing a 16-PE hardware prototype with CMOS 28nm technology. Compared with GPU on various workloads, ETTE achieves 6.5× − 253.1× higher throughput and 189.2× − 9750.5× higher energy efficiency. Compared with the state-of-the-art DNN accelerators, ETTE brings 1.1× − 58.3×, 2.6× − 1170.4× and 1.8× − 2098.2× improvement on throughput, energy efficiency and area efficiency, respectively.
A Joint Entity and Relation Extraction Model based on Efficient Sampling and Explicit Interaction

Joint entity and relation extraction (RE) construct a framework for unifying entity recognition and relationship extraction, and the approach can exploit the dependencies between the two tasks to improve the performance of the task. However, the existing tasks still have the following two problems. First, when the model extracts entity information, the boundary is blurred. Secondly, there are mostly implicit interactions between modules, that is, the interactive information is hidden inside the model, and the implicit interactions are often insufficient in the degree of interaction and lack of interpretability. To this end, this study proposes a joint entity and relation extraction model (ESEI) based on Efficient Sampling and Explicit Interaction. We innovatively divide negative samples into sentences based on whether they overlap with positive samples, which improves the model’s ability to extract entity word boundary information by controlling the sampling ratio. In order to increase the explicit interaction ability between the models, we introduce a heterogeneous graph neural network (GNN) into the model, which will serve as a bridge linking the entity recognition module and the relation extraction module, and enhance the interaction between the modules through information transfer. Our method substantially improves the model’s discriminative power on entity extraction tasks and enhances the interaction between relation extraction tasks and entity extraction tasks. Experiments show that the method is effective, we validate our method on four datasets, and for joint entity and relation extraction, our model improves the F1 score on multiple datasets.