Miracle-dz/UniFAR-FTUs huggingface dataset:
What makes you feel attached to smartwatches? The stimulus-organism-response (S-O-R) perspectives

PurposeThe purpose of this paper is to answer the question of whether smartwatches will survive and gain their own niche within the consumer electronics market. Based on the stimulus–organism–response (S–O–R) framework, this study identifies and validates the impacts of both technological and fashion-related factors (interactivity, autonomy, visual aesthetics and self-expression) on product attachment towards smartwatches through user satisfaction and pleasure derived from their smartwatches.Design/methodology/approachThe authors collected the survey data via online surveys from 198 respondents and tested measurement and structural models with the partial least square technique.FindingsThe authors found that both technological characteristics (interactivity and autonomy) and fashion-related characteristics (visual aesthetics and self-expression) have an impact on product attachment through pleasure.Research limitations/implicationsSeveral other important characteristics of traditional wrist-watches such as durability or workmanship are not considered in this study, but should be included in future studies. The three-item measure of autonomy may be insufficient for more sophisticated wearable devices in the future. In future studies, the impact of product attachment on users’ continued usage should be examined.Practical implicationsThis study provides important practical implications for smartwatch makers interested in product development, as users were found to consider fashion-related characteristics to be as important as technological characteristics.Originality/valueThis study is the first study that considers both aesthetic and technological factors for IT acceptance in the context of wearable devices. Also, instead of traditional IT acceptance measures such as continued use, this study investigates users’ product attachment, which is more relevant to the case of wearable devices.
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning

We present a novel recommendation system designed to provide real-time treatment strategies to therapists during psychotherapy sessions. Our system utilizes a turn-level rating mechanism that forecasts the therapeutic outcome by calculating a similarity score between the profound representation of a scoring inventory and the patient's current spoken sentence. By transcribing and segmenting the continuous audio stream into patient and therapist turns, our system conducts immediate evaluation of their therapeutic working alliance. The resulting dialogue pairs, along with their computed working alliance ratings, are then utilized in a deep reinforcement learning recommendation system. In this system, the sessions are treated as users, while the topics are treated as items. To showcase the system's effectiveness, we not only evaluate its performance using an existing dataset of psychotherapy sessions but also demonstrate its practicality through a web app. Through this demo, we aim to provide a tangible and engaging experience of our recommendation system in action.
What Does the Gradient Tell When Attacking the Graph Structure

Recent research has revealed that Graph Neural Networks (GNNs) are susceptible to adversarial attacks targeting the graph structure. A malicious attacker can manipulate a limited number of edges, given the training labels, to impair the victim model's performance. Previous empirical studies indicate that gradient-based attackers tend to add edges rather than remove them. In this paper, we present a theoretical demonstration revealing that attackers tend to increase inter-class edges due to the message passing mechanism of GNNs, which explains some previous empirical observations. By connecting dissimilar nodes, attackers can more effectively corrupt node features, making such attacks more advantageous. However, we demonstrate that the inherent smoothness of GNN's message passing tends to blur node dissimilarity in the feature space, leading to the loss of crucial information during the forward process. To address this issue, we propose a novel surrogate model with multi-level propagation that preserves the node dissimilarity information. This model parallelizes the propagation of unaggregated raw features and multi-hop aggregated features, while introducing batch normalization to enhance the dissimilarity in node representations and counteract the smoothness resulting from topological aggregation. Our experiments show significant improvement with our approach.Furthermore, both theoretical and experimental evidence suggest that adding inter-class edges constitutes an easily observable attack pattern. We propose an innovative attack loss that balances attack effectiveness and imperceptibility, sacrificing some attack effectiveness to attain greater imperceptibility. We also provide experiments to validate the compromise performance achieved through this attack loss.
Metastable mixing of Markov chains: Efficiently sampling low temperature exponential random graphs

In this paper we consider the problem of sampling from the low-temperature exponential random graph model (ERGM). The usual approach is via Markov chain Monte Carlo, but Bhamidi et al. showed that any local Markov chain suffers from an exponentially large mixing time due to metastable states. We instead consider metastable mixing, a notion of approximate mixing relative to the stationary distribution, for which it turns out to suffice to mix only within a collection of metastable states. We show that the Glauber dynamics for the ERGM at any temperature -- except at a lower-dimensional critical set of parameters -- when initialized at $G(n,p)$ for the right choice of $p$ has a metastable mixing time of $O(n^2\log n)$ to within total variation distance $\exp(-\Omega(n))$.
Artificial Neural Networks for Finger Vein Recognition: A Survey

Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to be stolen and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 149 related papers. First, we introduce the background of finger vein recognition and the motivation of this survey. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. The public datasets that are widely used in finger vein recognition are then described. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. Finally, the challenges and potential development directions in finger vein recognition are discussed. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
YouTube COVID-19 Vaccine Misinformation on Twitter: Platform Interactions and Moderation Blind Spots

While most social media companies have attempted to address the challenge of COVID-19 misinformation, the success of those policies is difficult to assess, especially when focusing on individual platforms. This study explores the relationship between Twitter and YouTube in spreading COVID-19 vaccine-related misinformation through a mixed-methods approach to analyzing a collection of tweets in 2021 sharing YouTube videos where those Twitter accounts had also linked to deleted YouTube videos. Principal components, cluster and network analyses are used to group the videos and tweets into interpretable groups by shared tweet dates, terms and sharing patterns; content analysis is employed to assess the orientation of tweets and videos to COVID-19 messages. From this we observe that a preponderance of anti-vaccine messaging remains among users who previously shared suspect information, in which a dissident political framing dominates, and which suggests moderation policy inefficacy where the platforms interact.
6D Robotic Assembly Based on RGB-only Object Pose Estimation

Vision-based robotic assembly is a crucial yet challenging task as the interaction with multiple objects requires high levels of precision. In this paper, we propose an integrated 6D robotic system to perceive, grasp, manipulate and assemble blocks with tight tolerances. Aiming to provide an off-the-shelf RGB-only solution, our system is built upon a monocular 6D object pose estimation network trained solely with synthetic images leveraging physically-based rendering. Subsequently, pose-guided 6D transformation along with collision-free assembly is proposed to construct any designed structure with arbitrary initial poses. Our novel 3-axis calibration operation further enhances the precision and robustness by disentangling 6D pose estimation and robotic assembly. Both quantitative and qualitative results demonstrate the effectiveness of our proposed 6D robotic assembly system.
Simple and Rigorous Proof Method for Practical Security of Quantum Key Distribution in the Single-Qubit Regime Using Mismatched Basis Measurements

Quantum key distribution (QKD) protocols aim at allowing two parties to generate a secret shared key. While many QKD protocols have been proven unconditionally secure in theory, practical security analyses of experimental QKD implementations typically do not take into account all possible loopholes, and practical devices are still not fully characterized for obtaining tight and realistic key rates. We present a simple method of computing secure key rates for any practical implementation of discrete-variable QKD (which can also apply to measurement-device-independent QKD), initially in the single-qubit regime, and we rigorously prove its unconditional security against any possible attack. We hope our method becomes one of the standard tools used for analyzing, benchmarking, and stan-dardizing all practical realizations of QKD.
Efficient liver segmentation with 3D CNN using computed tomography scans

The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in the early stages of tumor development is highly required as a critical part of medical treatment. Many imaging modalities can be used as aiding tools to detect liver tumors. Computed tomography (CT) is the most used imaging modality for soft tissue organs such as the liver. This is because it is an invasive modality that can be captured relatively quickly. This paper proposed an efficient automatic liver segmentation framework to detect and segment the liver out of CT abdomen scans using the 3D CNN DeepMedic network model. Segmenting the liver region accurately and then using the segmented liver region as input to tumors segmentation method is adopted by many studies as it reduces the false rates resulted from segmenting abdomen organs as tumors. The proposed 3D CNN DeepMedic model has two pathways of input rather than one pathway, as in the original 3D CNN model. In this paper, the network was supplied with multiple abdomen CT versions, which helped improve the segmentation quality. The proposed model achieved 94.36%, 94.57%, 91.86%, and 93.14% for accuracy, sensitivity, specificity, and Dice similarity score, respectively. The experimental results indicate the applicability of the proposed method.
Quantum gravity with ghost pairs

: We explicitly show that general local higher-derivative theories with only complex conjugate ghosts and normal real particles are unitary at any perturbative order in the loop expansion. The proof presented here relies on integrating the loop energies on complex paths resulting from the deformation of the purely imaginary paths, when the external energies are continued from imaginary to real values. Contrary to the case of nonlocal theories, where the same integration path was ﬁrst proposed, for the classes of theories studied here the same procedure is not analytic, but the resulting theory is unitary and unique when the complex ghosts are present in pairs. As an explicit application, a special class of higher-derivative super-renormalizable or ﬁnite gravitational and gauge theories turns out to be unitary at any perturbative order if we exclude the complex ghosts from the spectrum of the theory, as it is normally accepted for Becchi–Rouet–Stora–Tyutin (BRST) ghosts. Finally, we propose an analogy between conﬁned gluons in quantum Yang– Mills theory and classical complex pairs in local higher-derivative theories. According to such interpretation, complex ghosts will not appear on shell as asymptotic states because conﬁned in what is natural to name “ghostballs. ”
Decentralized Coordination in Partially Observable Queueing Networks

We consider communication in a fully cooperative multi-agent system, where the agents have partial observation of the environment and must act jointly to maximize the overall reward. We have a discrete-time queueing network where agents route packets to queues based only on the partial information of the current queue lengths. The queues have limited buffer capacity, so packet drops happen when they are sent to a full queue. In this work, we implemented a communication channel for the agents to share their information in order to reduce the packet drop rate. For efficient information sharing we use an attention-based communication model, called ATVC, to select informative messages from other agents. The agents then infer the state of queues using a combination of the variational autoencoder, VAE, and product-of-experts, PoE, model. Ultimately, the agents learn what they need to communicate and with whom, instead of communicating all the time with everyone. We also show empirically that ATVC is able to infer the true state of the queues and leads to a policy which outperforms existing baselines.
Simple Rigs Hold Fast

An important use of computational systems is updating the state of an object while preserving some set of invariants. That object might be a file, a row in a database, or perhaps an entry in a distributed system. Its invariants may place limits on its relationships with other objects, and generally include maintaining a unique identity across updates. The system maintaining these invariants is responsible for not equivocating, or presenting conflicting versions of an object. We call this absence of equivocation integrity. Today the system providing integrity to an object also manages its state. Indeed, it has long been assumed that this must be the case, and that only the system managing an object's state can provide it with integrity. We show that this assumption is wrong. Objects can maintain the integrity of one system while having their state fully managed by another.
Deep Depth Completion From Extremely Sparse Data: A Survey

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the first time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies with a proposal of a novel taxonomy that categorizes existing methods. Besides, we present a quantitative comparison of model performance on three widely used benchmarks, including indoor and outdoor datasets. Finally, we discuss the challenges of prior works and provide readers with some insights for future research directions.
Vehicle Routing Problem of the Car Distribution in Central Europe

Today’s freight transport market in Central Europe is characterized by a stagnating total transport volume combined with an increasing volatility of the transport requests. At the same time, in order to reduce the emission of greenhouse gases, more and more transports should be transferred from the road to more environmentally friendly transport modes. This, however, conflicts with the increasing volatility of the transport requests as a truck is more flexible in comparison to a train or a barge. In this thesis, on basis of the example of the car distribution in Central Europe we develop a model describing the underlying vehicle routing problem to be able to evaluate different fleet compositions of transport vehicles and to show up more environmentally friendly ways of transport. To solve the resulting mixed integer linear program, we present an exact optimization algorithm as well as a tabu-search heuristic. At the end of the thesis we apply both algorithms on an example case of real-world transportation data of the car distribution and compare the performance of the algorithms.
CMAR_A_262017 12337..12348

Department of Breast Diseases, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China Background: Previous studies have shown that kinesin family proteins (KIFs) play an indispensable roles in several types of cancer. However, the expression and clinical significance of KIFs in triple-negative breast cancer remain unclear. Methods: In this study, the role of KIF15, including gene expression analysis, methylation characteristic, CNV characteristic, and miRNA target regulation, was evaluated using multiple bioinformatic tools based on TCGA database. Quantitative real-time PCR and Western blot were used to determine the expression level of KIF15 in triple-negative breast cancer cell lines. Then, functional experiments were employed to explore the effects of KIF15 on tumor growth and metastasis in triple-negative breast cancer. Results: Our data showed that KIF15 was significantly upregulated in triple-negative breast cancer (TNBC). Functionally, downregulation of KIF15 significantly facilitated apoptosis and G2/M phase arrest, and inhibited the migration and invasion of TNBC cells. The mechanism of action of KIF15 was closely related to DNA replication checkpoint and cell cycle regulation in TNBC based on GSEA. In addition, bioinformatics analysis demonstrated that high expression of KIF15 in TNBC was correlated with copy number aberration and DNA methylation levels. Conclusion: Our findings suggest that KIF15 is a novel oncogene in TNBC and provide us a strong evidence that it might be served as a potential clinical target and biomarker in triplenegative breast cancer.
An Online Joint Optimization–Estimation Architecture for Distribution Networks

In this article, we propose an optimal joint optimization–estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the OPF decisions and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization and estimation usually operate at distinct layers and different time scales. Hence, it enables a computationally affordable, efficient, and robust online operational framework for distribution networks under time-varying settings.
TopicFM: Robust and Interpretable Topic-Assisted Feature Matching

This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a scene via graph neural networks or transformers. However, these contexts do not explicitly represent high-level contextual information, such as structural shapes or semantic instances; therefore, the encoded features are still not sufficiently discriminative in challenging scenes. We propose a novel image-matching method that applies a topic-modeling strategy to encode high-level contexts in images. The proposed method trains latent semantic instances called topics. It explicitly models an image as a multinomial distribution of topics, and then performs probabilistic feature matching. This approach improves the robustness of matching by focusing on the same semantic areas between the images. In addition, the inferred topics provide interpretability for matching the results, making our method explainable. Extensive experiments on outdoor and indoor datasets show that our method outperforms other state-of-the-art methods, particularly in challenging cases.
Euclid: Searching for pair-instability supernovae 
with the Deep Survey

Pair-instability supernovae are theorized supernovae that have not yet been observationally confirmed. They are predicted to exist in low-metallicity environments. Because overall metallicity becomes lower at higher redshifts, deep near-infrared transient surveys probing high-redshift supernovae are suitable to discover pair-instability supernovae. The Euclid satellite, which is planned to be launched in 2023, has a near-infrared wide-field instrument that is suitable for a high-redshift supernova survey. The Euclid Deep Survey is planned to make regular observations of three Euclid Deep Fields (40 deg2 in total) spanning the Euclid's 6 year primary mission period. While the observations of the Euclid Deep Fields are not frequent, we show that the predicted long duration of pair-instability supernovae would allow us to search for high-redshift pair-instability supernovae with the Euclid Deep Survey. Based on the current observational plan of the Euclid mission, we conduct survey simulations in order to estimate the expected numbers of pair-instability supernova discoveries. We find that up to several hundred pair-instability supernovae at z<~ 3.5 can be discovered within the Euclid Deep Survey. We also show that pair-instability supernova candidates can be efficiently identified by their duration and color that can be determined with the current Euclid Deep Survey plan. We conclude that the Euclid mission can lead to the first confirmation of pair-instability supernovae if their event rates are as high as those predicted by recent theoretical studies. We also update the expected numbers of superluminous supernova discoveries in the Euclid Deep Survey based on the latest observational plan.