Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Documentation Use Through Log Analysis: An Exploratory Case Study of Four Cloud Services

Almost no modern software system is written from scratch, and developers are required to effectively learn to use third-party libraries or software services. Thus, many practitioners and researchers have looked for ways to create effective documentation that supports developers' learning. However, few efforts have focused on how people actually use the documentation. In this paper, we report on an exploratory, multi-phase, mixed methods empirical study of documentation page-view logs from four cloud-based industrial services. By analyzing page-view logs for over 100,000 users, we find diverse patterns of documentation page visits. Moreover, we show statistically that which documentation pages people visit often correlates with user characteristics such as past experience with the specific product, on the one hand, and with future adoption of the API on the other hand. We discuss the implications of these results on documentation design and propose documentation page-view log analysis as a feasible technique for design audits of documentation, from ones written for software developers to ones designed to support end users (e.g., Adobe Photoshop).
Manta Ray Inspired Flapping-Wing Blimp

Lighter-than-air vehicles or blimps, are an evolving platform in robotics with several beneficial properties such as energy efficiency, collision resistance, and ability to work in close proximity to human users. While existing blimp designs have mainly used propeller-based propulsion, we focus our attention to an alternate locomotion method, flapping wings. Specifically, this paper introduces a flapping-wing blimp inspired by manta rays, in contrast to existing research on flapping-wing vehicles that draw inspiration from insects or birds. We present the overall design and control scheme of the blimp as well as the analysis on how the wing performs. The effects of wing shape and flapping characteristics on the thrust generation are studied experimentally. We also demonstrate that the flapping-wing blimp has a significant range advantage over a propeller-based system.
Scattered field formalism in the particle-in-cell method for plasma acceleration in tightly focused ultrashort laser beams

The scattered field formalism is combined to the particle-in-cell method to model relativistic laser-plasma dynamics in complex field configurations. Despite the strong nonlinearity of the interactions, we demonstrate the validity of this technique theoretically, by using the semi-linear property of Maxwell-Vlasov equations, and empirically, by performing a systematic convergence analysis with simple 1D and 2D examples. Then, we apply this method to the description of electron acceleration in undercritical plasmas from tightly focused linear and radially polarized beams. For this purpose, an analytical solution for a linearly-polarized tightly focused beam is derived. According to our simulations and their analysis, this approach looks very promising for the optimization of laser-induced relativistic electron beams at ultrahigh intensities because it allows for tightly-focused geometries in reduced calculation volumes.
Measurement of cryoelectronics heating using a local quantum dot thermometer in silicon

Silicon technology offers the enticing opportunity for monolithic integration of quantum and classical electronic circuits. However, the power consumption levels of classical electronics may compromise the local chip temperature and hence the fidelity of qubit operations. Here, we utilize a quantum-dot-based thermometer embedded in an industry-standard silicon field-effect transistor (FET), to assess the local temperature increase produced by an active FET placed in close proximity. We study the impact of both static and dynamic operation regimes. When the FET is operated statically, we find a power budget of 45 nW at 100 nm separation whereas at 216 $\mu$m the power budget raises to 150 $\mu$W. When operated dynamically, we observe negligible temperature increase for the switch frequencies tested up to 10 MHz. Our work describes a method to accurately map out the available power budget at a distance from a solid-state quantum processor and indicate under which conditions cryoelectronics circuits may allow the operation of hybrid quantum-classical systems.
Efficiently Visualizing Large Graphs

Most existing graph visualization methods based on dimension reduction are limited to relatively small graphs due to performance issues. In this work, we propose a novel dimension reduction method for graph visualization, called t-Distributed Stochastic Graph Neighbor Embedding (t-SGNE). t-SGNE is specifically designed to visualize cluster structures in the graph. As a variant of the standard t-SNE method, t-SGNE avoids the time-consuming computations of pairwise similarity. Instead, it uses the neighbor structures of the graph to reduce the time complexity from quadratic to linear, thus supporting larger graphs. In addition, to suit t-SGNE, we combined Laplacian Eigenmaps with the shortest path algorithm in graphs to form the graph embedding algorithm ShortestPath Laplacian Eigenmaps Embedding (SPLEE). Performing SPLEE to obtain a high-dimensional embedding of the large-scale graph and then using t-SGNE to reduce its dimension for visualization, we are able to visualize graphs with up to 300K nodes and 1M edges within 5 minutes and achieve approximately 10% improvement in visualization quality. Codes and data are available at https://github.com/Charlie-XIAO/embedding-visualization-test.
Long-form Simultaneous Speech Translation: Thesis Proposal

Simultaneous speech translation (SST) aims to provide real-time translation of spoken language, even before the speaker finishes their sentence. Traditionally, SST has been addressed primarily by cascaded systems that decompose the task into subtasks, including speech recognition, segmentation, and machine translation. However, the advent of deep learning has sparked significant interest in end-to-end (E2E) systems. Nevertheless, a major limitation of most approaches to E2E SST reported in the current literature is that they assume that the source speech is pre-segmented into sentences, which is a significant obstacle for practical, real-world applications. This thesis proposal addresses end-to-end simultaneous speech translation, particularly in the long-form setting, i.e., without pre-segmentation. We present a survey of the latest advancements in E2E SST, assess the primary obstacles in SST and its relevance to long-form scenarios, and suggest approaches to tackle these challenges.
An Exploration Into Web Session Security- A Systematic Literature Review

The most common attacks against web sessions are reviewed in this paper, for example, some attacks against web browsers' honest users attempting to create session with trusted web browser application legally. We have assessed with four different ways to judge the viability of a certain solution by reviewing existing security solutions which prevent or halt the different attacks. Then we have pointed out some guidelines that have been taken into account by the designers of the proposals we reviewed. The guidelines we have identified will be helpful for the creative solutions proceeding web security in a more structured and holistic way.
DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis

Generating controllable and photorealistic digital human avatars is a long-standing and important problem in Vision and Graphics. Recent methods have shown great progress in terms of either photorealism or inference speed while the combination of the two desired properties still remains unsolved. To this end, we propose a novel method, called DELIFFAS, which parameterizes the appearance of the human as a surface light field that is attached to a controllable and deforming human mesh model. At the core, we represent the light field around the human with a deformable two-surface parameterization, which enables fast and accurate inference of the human appearance. This allows perceptual supervision on the full image compared to previous approaches that could only supervise individual pixels or small patches due to their slow runtime. Our carefully designed human representation and supervision strategy leads to state-of-the-art synthesis results and inference time. The video results and code are available at https://vcai.mpi-inf.mpg. de/projects/DELIFFAS.
Enhancing Neural Machine Translation with Semantic Units

Conventional neural machine translation (NMT) models typically use subwords and words as the basic units for model input and comprehension. However, complete words and phrases composed of several tokens are often the fundamental units for expressing semantics, referred to as semantic units. To address this issue, we propose a method Semantic Units for Machine Translation (SU4MT) which models the integral meanings of semantic units within a sentence, and then leverages them to provide a new perspective for understanding the sentence. Specifically, we first propose Word Pair Encoding (WPE), a phrase extraction method to help identify the boundaries of semantic units. Next, we design an Attentive Semantic Fusion (ASF) layer to integrate the semantics of multiple subwords into a single vector: the semantic unit representation. Lastly, the semantic-unit-level sentence representation is concatenated to the token-level one, and they are combined as the input of encoder. Experimental results demonstrate that our method effectively models and leverages semantic-unit-level information and outperforms the strong baselines. The code is available at https://github.com/ictnlp/SU4MT.
Revisiting Map Relations for Unsupervised Non-Rigid Shape Matching

We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functional map methods and can be applied to a broad range of different challenging scenarios. Previous deep functional map methods mainly focus on feature extraction and aim exclusively at obtaining more expressive features for functional map computation. However, the importance of the functional map computation itself is often neglected and the relationship between the functional map and point-wise map is underexplored. In this paper, we systematically investigate the coupling relationship between the functional map from the functional map solver and the point-wise map based on feature similarity. To this end, we propose a self-adaptive functional map solver to adjust the functional map regularisation for different shape matching scenarios, together with a vertex-wise contrastive loss to obtain more discriminative features. Using different challenging datasets (including non-isometry, topological noise and partiality), we demonstrate that our method substantially outperforms previous state-of-the-art methods.
Checking and Automating Confidentiality Theory in Isabelle/UTP

The severity of recent vulnerabilities discovered on modern CPUs, e.g., Spectre [1], highlights how information leakage can have devas-tating effects to the security of computer systems. At the same time, it suggests that confidentiality should be promoted as a normal part of program verification, to discover and mitigate such vulnerabili-ties early in development. The theory we propose is primarily based on Bank's theory [2], a framework for reasoning about confidentiali-ty properties formalised in the Unifying Theories of Programming (UTP) [3]. We mechanised our encoding in the current implementa-tion of UTP in the Isabelle theorem prover, Isabelle/UTP [4]. We have identified some theoretical issues in Bank's original framework. Finally, we demonstrate how our mechanisation can be used to for-mally verify of some of the examples from Bank's work.
BRC-20: Hope or Hype

BRC-20 (short for Bitcoin Request for Comment 20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional ERC-20 token standards on Ethereum, BRC-20 introduces non-fungibility to Bitcoin through an editable field in each satoshi (0.00000001 Bitcoin, the smallest unit), making them unique. In this paper, we pioneer the exploration of this concept, covering its intricate mechanisms, features, and state-of-the-art applications. By analyzing the multi-dimensional data spanning over months with factual investigations, we conservatively comment that while BRC-20 expands Bitcoin's functionality and applicability, it may still not match Ethereum's abundance of decentralized applications and similar ecosystems.
Feasibility of Frequency Sharing in Satellite/Terrestrial Integrated Mobile Communication Systems

Satellite and terrestrial integrated mobile communication systems have been recently drawing a great deal of attentions, where a terrestrial network and a satellite network supplement each other to enable ubiquitous communications economically. In such an integrated mobile communication system, a small hand-held mobile terminal can be used in both satellite and terrestrial networks in a seamless manner. One of the key challenges of the satellite and terrestrial integrated mobile communication system is to share frequency spectrum by satellite networks and terrestrial networks, aiming at efficient frequency utilization and easy implementation of small size satellite/terrestrial dual mode mobile terminal. This paper proposes a simple system model to study the feasibility of frequency sharing between satellite and the terrestrial networks. The evaluation results show that the frequency sharing between satellite and terrestrial networks is feasible with careful network design and traffic control.
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction

T cell receptors (TCRs) are critical components of adaptive immune systems, responsible for responding to threats by recognizing epitope sequences presented on host cell surface. Computational prediction of binding affinity between TCRs and epitope sequences using machine/deep learning has attracted intense attention recently. However, its success is hindered by the lack of large collections of annotated TCR-epitope pairs. Annotating their binding affinity requires expensive and time-consuming wet-lab evaluation. To reduce annotation cost, we present ActiveTCR, a framework that incorporates active learning and TCR-epitope binding affinity prediction models. Starting with a small set of labeled training pairs, ActiveTCR iteratively searches for unlabeled TCR-epitope pairs that are "worthy" for annotation. It aims to maximize performance gains while minimizing the cost of annotation. We compared four query strategies with a random sampling baseline and demonstrated that ActiveTCR reduces annotation costs by approximately 40%. Furthermore, we showed that providing ground truth labels of TCR-epitope pairs to query strategies can help identify and reduce more than 40% redundancy among already annotated pairs without compromising model performance, enabling users to train equally powerful prediction models with less training data. Our work is the first systematic investigation of data optimization for TCR-epitope binding affinity prediction.
Near to Mid-infrared Spectroscopy of (65803) Didymos as Observed by JWST: Characterization Observations Supporting the Double Asteroid Redirection Test

The Didymos binary asteroid was the target of the Double Asteroid Redirection Test (DART) mission, which intentionally impacted Dimorphos, the smaller member of the binary system. We used the Near-Infrared Spectrograph and Mid-Infrared Instrument instruments on JWST to measure the 0.6–5 and 5–20 μm spectra of Didymos approximately two months after the DART impact. These observations confirm that Didymos belongs to the S asteroid class and is most consistent with LL chondrite composition, as was previously determined from its 0.6–2.5 μm reflectance spectrum. Measurements at wavelengths >2.5 μm show Didymos to have thermal properties typical for an S-complex asteroid of its size and to be lacking absorptions deeper than ∼2% due to OH or H2O. Didymos’ mid-infrared emissivity spectrum is within the range of what has been measured on S-complex asteroids observed with the Spitzer Space Telescope and is most consistent with emission from small (<25 μm) surface particles. We conclude that the observed reflectance and physical properties make the Didymos system a good proxy for the type of ordinary chondrite asteroids that cross near-Earth space, and a good representative of likely future impactors.
Towards Operationalizing Social Bonding in Human-Robot Dyads

With momentum increasing in the use of social robots as long-term assistive and collaborative partners, humans developing social bonds with these artificial agents appears to be inevitable. In human-human dyads, social bonding plays a powerful role in regulating behaviours, emotions, and even health. If this is to extend to human-robot dyads, the phenomenology of such relationships (including their emergence and stability) must be better understood. In this paper, we discuss potential approaches towards operationalizing the phenomenon of social bonding between human-robot dyads. We will discuss a number of biobehavioural proxies of social bonding, moving away from existing approaches that use subjective, psychological measures, and instead grounding our approach in some of the evolutionary, neurobiological and physiological correlates of social bond formation in natural systems: (a) reductions in physiological stress (the ''social buffering'' phenomenon), (b) narrowing of spatial proximity between dyads, and (c) inter-dyad behavioural synchrony. We provide relevant evolutionary support for each proposed component, with suggestions and considerations for how they can be recorded in (real-time) human-robot interaction scenarios. With this, we aim to inspire more robust operationalisation of ''social bonding'' between human and artificial (robotic) agents.
Aerial-Aided mmWave VANETs Using NOMA: Performance Analysis, Comparison, and Insights

In this paper, we propose the integration of tethered flying platforms in cooperative vehicular ad hoc networks (VANETs) to alleviate the problems of rapid urbanization. In this context, we study the performance of VANETs by deriving approximate outage probability and average achievable rate expressions using tools from stochastic geometry. We compare between the usage of networked tethered flying platforms (NTFPs) and traditional roadside units (RSUs). On the other hand, the rapid increase of smart devices in vehicles and the upcoming urban air mobility (UAM) vision will congest the spectrum and require increased data rates. Hence, we use non-orthogonal multiple access (NOMA) to improve spectral efficiency and compare its performance to orthogonal access schemes. Furthermore, we utilize millimeter-wave (mmWave) frequencies for high data rates and implement a sectored beamforming model. We extensively study the system using three transmission schemes: direct, relay, and hybrid transmission. The results show that when acting as relays, NTFPs outperform RSUs for larger distances between the transmitting and the receiving vehicles, while RSUs outperform NTFPs for short distances. However, NTFPs are the best solution when acting as a source. Moreover, we find that, in most cases, direct transmission is preferred to achieve a high rate compared to other schemes. Finally, the results are summarized in two tables that provide insights into connecting VANETs by selecting the most suitable platform and type of communication for a given set of parameters, configurations, and requirements.
Hamiltonicity and related properties in $K_{r+1}$-free graphs

We prove best-possible edge density conditions sufficient to guarantee traceability, Hamiltonicity, chorded pancyclicity, Hamiltonian-connectedness, $k$-path Hamiltonicity, and $k$-Hamiltonicity in $K_{r+1}$-free graphs. We characterize the extremal graphs. Then we extend these edge density conditions to clique density conditions. Equivalently, we introduce variants of the extremal number $\mathrm{ex}(n,F)$ and the generalized extremal number $\mathrm{ex}(n,H,F)$ -- that is, the maximum numbers of edges and copies of $H$, respectively, in $n$-vertex, $F$-free graphs -- in which we require that these graphs not be traceable, Hamiltonian, chorded pancyclic, Hamiltonian-connected, $k$-path Hamiltonian, or $k$-Hamiltonian, and we determine their values for $F=K_{r+1}$ and $H=K_t$.
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care

Telehealth is a valuable tool for primary health care (PHC), where depression is a common condition. PHC is the first point of contact for most people with depression, but about 25% of diagnoses made by PHC physicians are inaccurate. Many other barriers also hinder depression detection and treatment in PHC. Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC and improve overall diagnosis and treatment outcomes. Telehealth consultations often have video issues, such as poor connectivity or dropped calls. Audio-only telehealth is often more practical for lower-income patients who may lack stable internet connections. Thus, our study focused on using audio data to predict depression risk. The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk. TPOT, an autoML tool, was used to select the best machine learning algorithm, which was the K-nearest neighbors classifier. The selected model had high performance in classifying depression risk (Precision: 0.98, Recall: 0.93, F1-Score: 0.96). These findings may lead to a range of tools to help screen for and treat depression. By developing tools to detect depression risk, patients can be routed to AI-driven chatbots for initial screenings. Partnerships with a range of stakeholders are crucial to implementing these solutions. Moreover, ethical considerations, especially around data privacy and potential biases in AI models, need to be at the forefront of any AI-driven intervention in mental health care.
USDC: Unified Static and Dynamic Compression for Visual Transformer

Visual Transformers have achieved great success in almost all vision tasks, such as classification, detection, and so on. However, the model complexity and the inference speed of the visual transformers hinder their deployments in industrial products. Various model compression techniques focus on directly compressing the visual transformers into a smaller one while maintaining the model performance, however, the performance drops dramatically when the compression ratio is large. Furthermore, several dynamic network techniques have also been applied to dynamically compress the visual transformers to obtain input-adaptive efficient sub-structures during the inference stage, which can achieve a better trade-off between the compression ratio and the model performance. The upper bound of memory of dynamic models is not reduced in the practical deployment since the whole original visual transformer model and the additional control gating modules should be loaded onto devices together for inference. To alleviate two disadvantages of two categories of methods, we propose to unify the static compression and dynamic compression techniques jointly to obtain an input-adaptive compressed model, which can further better balance the total compression ratios and the model performances. Moreover, in practical deployment, the batch sizes of the training and inference stage are usually different, which will cause the model inference performance to be worse than the model training performance, which is not touched by all previous dynamic network papers. We propose a sub-group gates augmentation technique to solve this performance drop problem. Extensive experiments demonstrate the superiority of our method on various baseline visual transformers such as DeiT, T2T-ViT, and so on.