Miracle-dz/UniFAR-FTUs huggingface dataset:
An Instrument for measuring users’ meta-intents

We propose the concept of meta-intents which represent high-level user preferences related to the interaction and decision-making in conversational recommender systems (CRS) and present a questionnaire instrument for measuring meta-intents. We conducted a two-stage user study, an exploratory study with 212 participants on Prolific, and a confirmatory study with 394 participants on Prolific. We obtained a reliable and stable meta-intents questionnaire with 22 question items, corresponding to seven latent factors (concepts). These seven factors cover important interaction preferences and are closely related to users’ decision-making process. For example, the factor dialog-initiative reflects whether users prefer to follow the system’s guidance or ask their own questions in a CRS. We conducted statistical analyses of meta-intents in two domains (smartphones and hotels), and a general chatbot scenario. We also investigated the influence of additional factors (demography, decision-making style) on meta-intents through Structural Equation Modeling (SEM). Our results provide preliminary evidence that the proposed meta-intents are domain and demography (gender, age) independent. They can be linked to the general decision-making style and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CRS and their potential personalization. Meta-intents also provide a basis for future analyses of interaction behavior in CRS and the development of a cognitively founded theoretical framework.
NosWalker: A Decoupled Architecture for Out-of-Core Random Walk Processing

Out-of-core random walk system has recently attracted a lot of attention as an economical way to run billions of walkers over large graphs. However, existing out-of-core random walk systems are all built upon general out-of-core graph processing frameworks, and hence do not take advantage of the unique properties of random walk applications. Different from traditional graph analysis algorithms, the sampling process of random walk can be decoupled from the processing of the walkers. It enables the system to reserve only pre-sample results in memory, which are typically much smaller than the entire edge set. Moreover, in random walk, it is not the number of walkers but the number of steps moved per second that dominates the overall performance. Thus, with independent walkers, there is no need to process all the walkers simultaneously. In this paper, we present NosWalker, an out-of-core random walk system that replaces the graph oriented scheduling with a decoupled system architecture that provides walker oriented scheduling. NosWalker is able to adaptively generate walkers and flexibly adjust the distribution of reserved pre-sample results in memory. Instead of processing all the walkers at once, NosWalker only tries its best to keep a few walkers able to continuously move forward. Experimental results show that NosWalker can achieve up to two orders of magnitude speedup compared to state-of-the-art out-of-core random walk systems. In particular, NosWalker demonstrates superior performance when the memory capacity can only hold about 10%-50% of the graph data, which can be a common case when the user needs to run billions of walkers over large graphs.
Toward A Two-Sided Fairness Framework in Search and Recommendation

As artificial intelligence (AI) assisted search and recommender systems have become ubiquitous in workplaces and everyday lives, understanding and accounting for fairness has gained increasing attention in the design and evaluation of such systems. While there is a growing body of computing research on measuring system fairness and biases associated with data and algorithms, the impact of human biases that go beyond traditional machine learning (ML) pipelines still remain understudied. In this Perspective Paper, we seek to develop a two-sided fairness framework that not only characterizes data and algorithmic biases, but also highlights the cognitive and perceptual biases that may exacerbate system biases and lead to unfair decisions. Within the framework, we also analyze the interactions between human and system biases in search and recommendation episodes. Built upon the two-sided framework, our research synthesizes intervention and intelligent nudging strategies applied in cognitive and algorithmic debiasing, and also proposes novel goals and measures for evaluating the performance of systems in addressing and proactively mitigating the risks associated with biases in data, algorithms, and bounded rationality. This paper uniquely integrates the insights regarding human biases and system biases into a cohesive framework and extends the concept of fairness from human-centered perspective. The extended fairness framework better reflects the challenges and opportunities in users’ interactions with search and recommender systems of varying modalities. Adopting the two-sided approach in information system design has the potential to enhancing both the effectiveness in online debiasing and the usefulness to boundedly rational users engaging in information-intensive decision-making.
It is an online platform and not the real world, I don’t care much: Investigating Twitter Profile Credibility With an Online Machine Learning-Based Tool

Social media is now an important source of everyday information. Given the plethora of scandals concerning the rapid spread of misinformation and disinformation on social media, the credibility of the content on these platforms is now a pivotal research area. Much of the existing work on social media credibility focuses on content credibility. In this study, however, we focus on the credibility of the profile as the virtual representation of the content author. We developed a real-time machine-learning-based online tool that assesses the credibility of profiles on Twitter, one of the most common and versatile social media platforms. To investigate user perceptions on credibility-related issues, we used our tool as a stimulus for people to reflect on their profile’s credibility and collected 100 responses. The combination of our quantitative and qualitative analysis reveals that the latest tweets and retweet behavior are two of the most critical factors for profile credibility. It is also observed that people demonstrate a limited interest in their profile credibility but agree that the author’s credibility is of paramount importance. With an open-source tool to assess user credibility on Twitter and a user study to establish its utility, we contribute a timely piece of research on the topic of online credibility.
Momentum: A New Look on the Old World

With the rapid growth of the stock market, many researchers examined the profitability and market efficiency of various trading strategies with the idea of Momentum Investing. This study optimizes the Moving Average Convergence-Divergence (MACD) oscillator and tests its performance under different trading strategies. The research simulates the trading process of 40 U.S. industry portfolios from 1926 to 2021 using different MACD oscillators constructed by the Exponential Moving Average (EMA) indicators of different decay parameters. To acquire the optimal MACD oscillator, the study performs the OLS linear regression analysis on each industry's stimulated excess returns and excess industry portfolio returns and uses the corresponding regression coefficients (alpha and beta) as the assessment criteria of the performance. To further optimize the model, the study improves the traditional trading strategy, the simple-crossover operation, to the n-day holding strategy, which aims to weaken the influence of false signals. The result shows that MACD strategies generally have positive alpha, hinting that investor can utilize this indicator to diversify portfolios and hedge their risks.
Cohort: Software-Oriented Acceleration for Heterogeneous SoCs

Philosophically, our approaches to acceleration focus on the extreme. We must optimise accelerators to the maximum, leaving software to fix any hardware-software mismatches. Today’s software abstractions for programming accelerators leak hardware details, requiring changes to data formats and manual memory and coherence management, among other issues. This harms generality and requires deep hardware knowledge to efficiently program accelerators, a state which we consider hardware-oriented. This paper proposes Software-Oriented Acceleration (SOA), where software uses existing abstractions, like software shared-memory queues, to interact with accelerators. We introduce the Cohort engine which exploits these queues’ standard semantics to efficiently connect producers and consumers in software with accelerators with minimal application changes. Accelerators are even usable in chains which can be runtime reconfigured by software. Cohort significantly reduces the burden to add new accelerators while maintaining system-level guarantees. We implement a Cohort FPGA prototype which supports SOA applications running on multicore Linux. Our evaluation shows speedups for Cohort over traditional approaches ranging from 1.83× to 8.38× over MMIO, and from 1.69× to 11.24× for DMA baselines. Our software-oriented batching optimisations within Cohort also improve performance from 2.32× to 8.10×, demonstrating the power of SOA.
Disaggregated RAID Storage in Modern Datacenters

RAID (Redundant Array of Independent Disks) has been widely adopted for decades, as it provides enhanced throughput and redundancy beyond what a single disk can offer. Today, enabled by fast datacenter networks, accessing remote block devices with acceptable overhead (i.e. disaggregated storage) becomes a reality (e.g., for serverless applications). Combining RAID with remote storage can provide the same benefits while creating better fault tolerance and flexibility than its monolithic counterparts. The key challenge of disaggregated RAID is to handle extra network traffic generated by RAID, which can consume a vast amount of NIC bandwidth. We present dRAID, a disaggregated RAID system that achieves near-optimal read and write throughput. dRAID exploits peer-to-peer disaggregated data access to reduce bandwidth consumption in both normal and degraded states. It employs non-blocking multi-stage writes to maximize inter-node parallelism, and applies pipelined I/O processing to maximize inter-device parallelism. We introduce bandwidth-aware reconstruction for better load balancing. We show that dRAID provides up to 3× bandwidth improvement. The results on a lightweight object store show that dRAID brings 1.5×-2.35× throughput improvement on various workloads.
Transparent Runtime Change Handling for Android Apps

Mobile devices often face runtime configuration changes, such as screen orientation changes, screen resizing, and language switching. The current Android design adopts a restarting-based solution to load the corresponding resources according to the new configuration. Therefore, application developers must explicitly deal with state preservation and restoration brought about by the activity restarting. Otherwise, the runtime change will cause state loss and even app crash issues. To solve the runtime change handling issues, we propose RCHDroid, a transparent runtime change handling approach for apps at the Android system level. When a configuration change occurs, we do not restart the activity, but instead, create a new activity according to the new configuration and migrate states from the old one to the new one, while putting the old activity into an inactive mode. We propose a lazy-migration scheme to handle asynchronous tasks that remain working on the old activity, which migrates the result events when the asynchronous tasks return. We have implemented a prototype of RCHDroid with real hardware and released the source code for public access. Overall, using RCHDroid, existing apps can handle runtime configuration changes without any modifications and save the runtime change handling time by 25.46%.
LEGO: Empowering Chip-Level Functionality Plug-and-Play for Next-Generation IoT Devices

Versatile Internet of Things (IoT) applications call for re-configurable IoT devices that can easily extend new functionality on demand. However, the heterogeneity of functional chips brings difficulties in device customization, leading to inadequate flexibility. In this paper, we propose LEGO, a novel architecture for chip-level re-configurable IoT devices that supports plug-and-play with Commercial Off-The-Shelf (COTS) chips. To combat the heterogeneity of functional chips, we first design a novel Unified Chip Description Language (UCDL) with meta-operation and chip specifications to access various types of functional chips uniformly. Then, to achieve chips plug-and-play, we build up a novel platform and shift all chip control logic to the gateway, which makes IoT devices entirely decoupled from specific applications and does not need to make any changes when plugging in new functional chips. Finally, to handle communications overheads, we built up a novel orchestration architecture for gateway instructions, which minimizes instruction transmission frequency in remote chip control. We implement the prototype and conduct extensive evaluations with 100+ types of COTS functional chips. The results show that new functional chips can be automatically accessed by the system within 0.13 seconds after being plugged in, and only bringing 0.53 kb of communication load on average, demonstrating the efficacy of LEGO design.
Accelerating Sparse Data Orchestration via Dynamic Reflexive Tiling

Tensor algebra involving multiple sparse operands is severely memory bound, making it a challenging target for acceleration. Furthermore, irregular sparsity complicates traditional techniques—such as tiling—for ameliorating memory bottlenecks. Prior sparse tiling schemes are sparsity unaware: they carve tensors into uniform coordinate-space shapes, which leads to low-occupancy tiles and thus lower exploitable reuse. To address these challenges, this paper proposes dynamic reflexive tiling (DRT), a novel tiling method that improves data reuse over prior art for sparse tensor kernels, unlocking significant performance improvement opportunities. DRT’s key idea is dynamic sparsity-aware tiling. DRT continuously re-tiles sparse tensors at runtime based on the current sparsity of the active regions of all input tensors, to maximize accelerator buffer utilization while retaining the ability to co-iterate through tiles of distinct tensors. Through an extensive evaluation over a set of SuiteSparse matrices, we show how DRT can be applied to multiple prior accelerators with different dataflows (ExTensor, OuterSPACE, MatRaptor), improving their performance (by 3.3×, 5.1× and 1.6×, respectively) while adding negligible area overhead. We apply DRT to higher-order tensor kernels to reduce DRAM traffic by 3.9× and 16.9× over a CPU implementation and prior-art tiling scheme, respectively. Finally, we show that the technique is portable to software, with an improvement of 7.29× and 2.94× in memory overhead compared to untiled sparse-sparse matrix multiplication (SpMSpM).
DrGPUM: Guiding Memory Optimization for GPU-Accelerated Applications

GPUs are widely used in today’s computing platforms to accelerate applications in various domains. However, scarce GPU memory resources are often the dominant limiting factor in strengthening the applicability of GPU computing. In this paper, we propose DrGPUM, the first profiler that systematically investigates patterns of memory inefficiencies in GPU-accelerated applications. The strength of DrGPUM, when compared to a large class of existing GPU profilers, is its ability to (1) correlate problematic memory usage with data objects and GPU APIs, (2) identify and categorize object-level and intra-object memory inefficiencies, and (3) provide rich insights to guide memory optimization. DrGPUM works on fully-optimized and unmodified GPU binaries, requires no modification to hardware or OS, and features a user-friendly GUI, which makes it attractive to use in production. Our evaluation with well-known benchmarks and applications shows DrGPUM’s effectiveness in identifying memory inefficiencies with moderate overhead. Eliminating these inefficiencies requires less than nine source lines of code modifications and yields significant reductions in peak memory usage (up to 83%) and/or significant performance improvements (up to 2.48×). Our optimization patches have been confirmed by application developers and upstreamed to their repositories.
RepCut: Superlinear Parallel RTL Simulation with Replication-Aided Partitioning

Register transfer level (RTL) simulation is an invaluable tool for developing, debugging, verifying, and validating hardware designs. Despite the parallel nature of hardware, existing parallel RTL simulators yield speedups unattractive for practical application due to high communication and synchronization costs incurred by typical circuit topologies. We present RepCut, a novel parallel RTL simulation methodology. RepCut is enabled by our replication-aided partitioning approach that cuts the circuit into balanced partitions with small overlaps. By replicating the overlaps, RepCut eliminates problematic data dependences between partitions and significantly reduces expensive synchronization overhead between parallel threads. RepCut outperforms state-of-the-art simulators, and when simulating a large system-on-chip with multiple out-of-order cores, it achieves a 27.10× speedup (superlinear) using 24 threads with only a 3.81% replication cost.
A Semi-Empirical Theory for Surface Mounted Aerodynamic Wall Shear Stress Gauges

A new semi-empirical theory relating aerodynamic wall shear stress to the mean voltage from a surface mounted constant temperature hot film gauge is presented. High spatial resolution thermal images of the sensor area are used to obtain values for the mean sensor temperature and the thermal dimensions of the sensor. These thermal dimensions are shown to be invariant with Reynolds number. This data is used in the derivation of a new theory that effectively decouples the convection and conduction and hence removes the need for a fully conjugate analysis. The correct form of the relationship between sensor voltage and wall shear stress is a necessary prerequisite for gauge calibration and hence the quantification of aerodynamic wall shear stress. A calibration methodology is presented in an accompanying paper.Copyright © 1995 by ASME
Physical limits of hyperthermia using magnetite fine particles

Structural and magnetic properties of fine particles of magnetite are investigated with respect to the application for hyperthermia. Magnetic hysteresis losses are measured in dependence on the field amplitude for selected commercial powders and are discussed in terms of grain size and structure of the particles. For ferromagnetic powders as well as for ferrofluids, results of heating experiments within organic gels in a magnetic high frequency field are reported. The heating effect depends strongly on the magnetic properties of the magnetite particles which may vary appreciably for different samples in dependence on the particle size and microstructure. In particular, the transition from ferromagnetic to superparamagnetic behavior causes changes of the loss mechanism, and accordingly, of the heating effect. The maximum attainable heating effect is discussed in terms of common theoretical models. Rise of temperature at the surface of a small heated sample as well as in its immediate neighborhood in the surrounding medium is measured in dependence on time and is compared with solutions of the corresponding heat conductivity problem. Conclusions with respect to clinical applications are given.
A CUG-initiated CATSPERθ functions in the CatSper channel assembly and serves as a checkpoint for flagellar trafficking

Calcium signaling is critical for successful fertilization. In spermatozoa, calcium influx into the sperm flagella mediated by the sperm specific CatSper calcium channel is necessary for hyperactivated motility and male fertility. CatSper is a macromolecular complex and is repeatedly arranged in zigzag rows within four linear nanodomains along the sperm flagella. Here, we report that the Tmem249-encoded transmembrane domain containing protein, CATSPERθ, is essential for the CatSper channel assembly during sperm tail formation. CATSPERθ facilitates the channel assembly by serving as a scaffold for a pore forming subunit CATSPER4. CATSPERθ is specifically localized at the interface of a CatSper dimer and can self-interact, suggesting its potential role in CatSper dimer formation. Male mice lacking CATSPERθ are infertile because the sperm lack the entire CatSper channel from sperm flagella, rendering sperm unable to hyperactivate, regardless of their normal expression in the testis. In contrast, genetic abrogation of any of the other CatSper transmembrane subunits results in loss of CATSPERθ protein in the spermatid cells during spermatogenesis. CATSPERθ might acts as a checkpoint for the properly assembled CatSper channel complex to traffic to sperm flagella. This study provides insights into the CatSper channel assembly and elucidates the physiological role of CATSPERθ in sperm motility and male fertility.
A general computational design strategy for stabilizing viral class I fusion proteins

Many pathogenic viruses, including influenza virus, Ebola virus, coronaviruses, and Pneumoviruses, rely on class I fusion proteins to fuse viral and cellular membranes. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more favorable and stable postfusion state. An increasing amount of evidence exists highlighting that antibodies targeting the prefusion conformation are the most potent. However, many mutations have to be evaluated before identifying prefusion-stabilizing substitutions. We therefore established a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. As a proof of concept, we applied this principle to the fusion protein of the RSV, hMPV, and SARS-CoV-2 viruses. For each protein, we tested less than a handful of designs to identify stable versions. Solved structures of designed proteins from the three different viruses evidenced the atomic accuracy of our approach. Furthermore, the immunological response of the RSV F design compared to a current clinical candidate in a mouse model. While the parallel design of two conformations allows identifying and selectively modifying energetically less optimized positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. We recaptured many approaches previously introduced manually for the stabilization of viral surface proteins, such as cavity-filling, optimization of polar interactions, as well as postfusion-disruptive strategies. Using our approach, it is possible to focus on the most impacting mutations and potentially preserve the immunogen as closely as possible to its native version. The latter is important as sequence re-design can cause perturbations to B and T cell epitopes. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.