Miracle-dz/UniFAR-FTUs huggingface dataset:
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design

Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e.g., consumption, socialization, and entertainment. However, limited research focuses on how values should be extensively considered in industrial deployments of recommender systems, the ignorance of which can be problematic. To fill this gap, in this paper, we adopt Value Sensitive Design to comprehensively explore how practitioners and users recognize different values of current industrial recommender systems. Based on conceptual and empirical investigations, we focus on five values: recommendation quality, privacy, transparency, fairness, and trustworthiness. We further conduct in-depth qualitative interviews with 20 users and 10 practitioners to delve into their opinions about these values. Our results reveal the existence and sources of tensions between practitioners and users in terms of value interpretation, evaluation, and practice, which provide novel implications for designing more human-centric and value-sensitive recommender systems.
Adaptive incomplete multi-view learning via tensor graph completion

With the advancement of the data acquisition techniques, multi-view learning has become a hot topic. Some multi-view learning methods assume that the multi-view data is complete, which means that all instances are present, but this too ideal. Certain tensor-based methods for handing incomplete multi-view data have emerged and have achieved better result. However, there are still some problems, such as use of traditional tensor norm which makes the computation high and is not able to handle out-of-sample. To solve these two problems, we proposed a new incomplete multiview learning method. A new tensor norm is deﬁned to implement graph tensor data recover. The recovered graphs are then regularized to a consistent low-dimensional representation of the samples. In addition, adaptive weights are equipped to each view to adjust the importance of diﬀerent views. Compared with the existing methods, our method nor only explores the consistency among views, but also obtains the low-dimensional representation of the new samples by using the learned projection matrix. An eﬃcient algorithm based on inexact augmented Lagrange multiplier (ALM) method are designed to solve the model and convergence is proved. Experimental results on four datasets show the eﬀectiveness of our method.
Computer Methods and Programs in Biomedicine

cient 3D architectures are investigated. Results : Experiments on both heart (Stanford AIMI Coronary Calcium) and brain (Alzheimer’s Disease Neuroimaging Initiative) datasets show that our method leads to improved 3D image generation quality (14.7 improvements on Fr´echet inception distance) with significantly fewer parameters (only 48.5% of the baseline method). Conclusions : We built a parameter-e ffi cient 3D medical image generation model. Due to the e ffi ciency and e ff ectiveness, it has the potential to generate high-quality 3D brain and heart images for real use cases.
Fuzzing Microservices In Industry: Experience of Applying EvoMaster at Meituan

With several microservice architectures comprising of thousands of web services in total, used to serve 630 million customers, companies like Meituan face several challenges in the verification and validation of their software. The use of automated techniques, especially advanced AI-based ones, could bring significant benefits here. EvoMaster is an open-source test case generation tool for web services, that exploits the latest advances in the field of Search-Based Software Testing research. This paper reports on our experience of integrating the EvoMaster tool in the testing processes at Meituan. Experiments were carried out to evaluate its performance in detail on two industrial web services which are parts of a large e-commerce microservices. A questionnaire and interviews were carried out with the engineers and managers at Meituan, to better understand the applicability and usability of fuzzing tools like EvoMaster in real industrial settings. On the one hand, the results of these analyses clearly show that existing tools like EvoMaster are already of benefits for practitioners in industry, e.g., EvoMaster detected 21 real faults and achieved an average of 71.3% coverage for code defining endpoints and 51.7% coverage for code implementing business logic, on two industrial APIs. On the other hand, there are still many critical challenges that the research community has to investigate.
Deciphering the Nature of Temperature-Induced Phases of MAPbBr3 by Ab Initio Molecular Dynamics

We present an \textit{ab initio} molecular dynamics study of the temperature-induced phases of methylammonium lead bromide (MAPbBr$_3$). We confirm that the low-temperature phase is not ferroelectric, and rule out the existence of any overall polarization arising from the motion of the individual sub-lattices. Our simulations at room temperature resulted in a cubic \textit{Pm-3m} phase with no discernible local orthorhombic distortions. At low temperatures, such distortions are shown to originate from octahedral scissoring modes, but they vanish at room temperature. The predicted timescales of MA motion agree very well with experimental estimates, establishing dynamic disordering of the molecular dipoles over several orientational minima at room temperature. We also identify the key modes of the inorganic and organic sub-lattices that are coupled at all temperatures mainly through the N-H$\cdots$Br hydrogen-bonds. Estimated lifetimes of the H-bonds correlate well with MA dynamics indicating a strong connection between these two aspects of organic inorganic hybrid perovskites. We also confirm that, in addition to disordering of MA orientations, the transition to the cubic phase is also associated with displacive characteristics arising from both MA as well as Br ions in the lattice.
Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems

Recommendation dialogue systems aim to build social bonds with usersandprovidehigh-qualityrecommendations. Thispaperpushes forward towards a promising paradigm called target-driven recommendation dialogue systems, which is highly desired yet under-explored. We focus on how to naturally lead users to accept the designated targets gradually through conversations. To this end, we propose a Target-driven Conversation Planning (TCP) framework to plan a sequence of dialogue actions and topics, driving the system to transit between different conversation stages proactively. We then apply our TCP with planned content to guide dialogue generation. Experimental results show that our conversation planning significantly improves the performance of target-driven recommendation dialogue systems.
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration

For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance. ‡
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning

As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks. This process requires numerous unlabeled training data and computational resources, which makes the pre-trained encoder become the valuable intellectual property of the owner. However, the lack of a priori knowledge of downstream tasks makes it non-trivial to protect the intellectual property of the pre-trained encoder by applying conventional watermarking methods. To deal with this problem, in this paper, we introduce AWEncoder, an adversarial method for watermarking the pre-trained encoder in contrastive learning. First, as an adversarial perturbation, the watermark is generated by enforcing the training samples to be marked to deviate respective location and surround a randomly selected key image in the embedding space. Then, the watermark is embedded into the pre-trained encoder by further optimizing a joint loss function. As a result, the watermarked encoder not only performs very well for downstream tasks, but also enables us to verify its ownership by analyzing the discrepancy of output provided using the encoder as the backbone under both white-box and black-box conditions. Extensive experiments demonstrate that the proposed work enjoys quite good effectiveness and robustness on different contrastive learning algorithms and downstream tasks, which has verified the superiority and applicability of the proposed work.
SINR Analysis of an IRS Assisted MU-MISO System

In this work, we characterize the outage probability (OP) of an intelligent reflecting surface (IRS) assisted multi-user multiple-input-single-output (MU-MISO) communication system. Using a two-step approximation method, we approximate the signal-to-interference-plus-noise ratio (SINR) for any downlink user by a Log-Normal random variable. The impact of various system parameters is studied using the closed-form expression of OP. It is concluded that the position of IRS has a critical role, but an appropriate increase in the number of IRS elements would help to compensate for the loss in performance if the position of IRS is suboptimal.
Pure-state tomography with parallel unentangled measurements

Quantum state tomography (QST) aims at estimating a quantum state from averaged quantum measurements made on copies of the state. Most quantum algorithms rely on QST at some point and it is a well explored topic in the literature, mostly for mixed states. In this paper we focus on the QST of a pure quantum state using parallel unentangled measurements. Pure states are a small but useful subset of all quantum states, their tomography requires fewer measurements and is essentially a phase recovery problem. Parallel unentangled measurements are easy to implement in practice because they allow the user to measure each qubit individually. We propose two sets of quantum measurements that one can make on a pure state as well as the algorithms that use the measurements outcomes in order to identify the state. We also discuss how those estimates can be fined tuned by finding the state that maximizes the likelihood of the measurements with different variants of the likelihood. The performances of the proposed three types of QST methods are validated by means of detailed numerical tests.
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation

Trajectory prediction is one of the essential tasks for autonomous vehicles. Recent progress in machine learning gave birth to a series of advanced trajectory prediction algorithms. Lately, the effectiveness of using graph neural networks (GNNs) with vectorized representations for trajec-tory prediction has been demonstrated by many researchers. Nonetheless, these algorithms either pay little attention to models' generalizability across various scenarios or simply assume training and test data follow similar statistics. In fact, when test scenarios are unseen or Out-of-Distribution (OOD), the resulting train-test domain shift usually leads to significant degradation in prediction performance, which will impact downstream modules and eventually lead to severe accidents. Therefore, it is of great importance to thoroughly investigation of the prediction models in terms of their generalizability, which can not only help identify their weaknesses but also provide insights on how to improve these models. This paper proposes a generalizability analysis framework using feature attribution methods to help interpret black-box models. For the case study, we provide an in-depth generalizability analysis of one of the state-of-the-art graph-based trajectory predictors that utilize vectorized representation. Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems. Finally, we conclude the common prediction challenges and how weighting biases induced by the training process can deteriorate the accuracy.
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals

Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the muscle tissue of the heart). The electrocardiogram (ECG) plays a critical role in identifying SA since it might reveal abnormal cardiac activity. Recent research on ECG-based SA detection has focused on feature engineering techniques that extract specific characteristics from multiple-lead ECG signals and use them as classification model inputs. In this study, a novel method of feature extraction which based on the detection of $S$ peaks is proposed to enhance the detection of adjacent SA segments using a single-lead ECG. In particular, ECG features collected from a single lead (V2) are used to identify SA episodes. On the extracted features, a CNN model is trained to detect SA. Experimental results demonstrate that the proposed method detects SA from single-lead ECG data is more accurate than existing state-of-the-art methods, with 91.13% classification accuracy, 92.58% sensitivity, and 88.75% specificity. Moreover, the further usage of features associated with the $S$ peaks enhances the classification accuracy by 0.85 %. Our findings indicate that the proposed machine learning system has the potential to be an effective method for detecting SA episodes.
Omni-Directional Pathloss Measurement Based on Virtual Antenna Array With Directional Antennas

Omni-directional pathloss, which refers to the pathloss when omni-directional antennas are used at the link ends, are essential for system design and evaluation. In the millimeter-wave (mm-Wave) and beyond bands, high gain directional antennas are widely used for channel measurements due to the significant signal attenuation. Conventional methods for omni-directional pathloss estimation are based on directional scanning sounding (DSS) system, i.e., a single directional antenna placed at the center of a rotator capturing signals from different rotation angles. The omni-directional pathloss is obtained by either summing up all the powers above the noise level or just summing up the powers of detected propagation paths. However, both methods are problematic with relatively wide main beams and high side-lobes provided by the directional antennas. In this correspondence, directional antenna based virtual antenna array (VAA) system is implemented for omni-directional pathloss estimation. The VAA scheme uses the same measurement system as the DSS, yet it offers high angular resolution (i.e. narrow main beam) and low side-lobes, which is essential for achieving accurate multipath detection in the power angle delay profiles (PADPs) and thereby obtaining accurate omni-directional pathloss. A measurement campaign was designed and conducted in an indoor corridor at 28-30 GHz to verify the effectiveness of the proposed method.
A primal-dual majorization-minimization method for large-scale linear programs

We present a primal-dual majorization-minimization method for solving large-scale linear programs. A smooth barrier augmented Lagrangian (SBAL) function with strict convexity for the dual linear program is derived. The majorization-minimization approach is naturally introduced to develop the smoothness and convexity of the SBAL function. Our method only depends on a factorization of the constant matrix independent of iterations and does not need any computation on step sizes, thus can be expected to be particularly appropriate for large-scale linear programs. The method shares some similar properties to the ﬁrst-order methods for linear programs, but its convergence analysis is established on the diﬀerentiability and convexity of our SBAL function. The global convergence is analyzed without prior requiring either the primal or dual linear program to be feasible. Under the regular conditions, our method is proved to be globally linearly convergent, and a new iteration complexity result is given.
Scaling limits and fluctuations of a family of N-urn branching processes

In this paper we are concerned with a family of $N$-urn branching processes, where some particles are put into $N$ urns initially and then each particle gives birth to several new particles in some urn when dies. This model includes the $N$-urn Ehrenfest model and the $N$-urn branching random walk as special cases. We show that the scaling limit of the process is driven by a $C(\mathbb{T})$-valued linear ordinary differential equation and the fluctuation of the process is driven by a generalized Ornstein-Uhlenbeck process in the dual of $C^\infty(\mathbb{T})$, where $\mathbb{T}=(0, 1]$ is the one-dimensional torus. A crucial step for proofs of above main results is to show that numbers of particles in different urns are approximately independent. As applications of our main results, limit theorems of hitting times of the process are also discussed.
The Organic food price premium and its susceptibility to news media coverage: evidence from the US milk industry

ABSTRACT This paper investigates the extent to which media coverage on organic dairy issues influences consumers’ willingness to pay (WTP) for the organic attribute of milk. We find that news with contents most often viewed as negative towards organic dairy are more powerful in decreasing consumers’ WTP for the organic attribute of milk compared to the positive WTP impact of news articles with contents most often viewed as positive towards organic dairy. Interestingly, consumers’ increasing exposure to organic dairy news that even take a neutral stance on the organic attribute also increases their WTP for the organic attribute.
Expression and Characterization of Human Factor IX FACTOR IXthr.3g7 AND FACTOR IXv,~.3g7*

Factor IXL,,,,g&a& has a single amino acid substitution at 397 (Ile to Thr) in the catalytic domain which results in severe hemophilia B. Recent investigations have shown that the substitution of threonine for isoleucine at 397 may affect a part of the macromolecular sub- strate binding site. Because threonine has a hydroxyl group in its side chain, it is possible that this hydroxyl group makes new hydrogen bonds and disturbs the substrate binding site. We used three techniques: mo- lecular biology, which includes site-directed mutagenesis and recombinant protein expression in tissue culture; computer-aided kinetic data analysis; and molec- ular modeling to study this mutation site. We have produced two mutant factor IX molecules that have isoleucine 397 replaced by valine or threonine. Factor IXwild type and the two mutants (factor IXv-1 and factor IXThr) were expressed in human kidney cells and purified using a conformation-specific monoclonal antibody column. After the activation by factor XIa, these three molecules were able to bind p-aminobenzamidine
Accident analysis of gas cylinder handling work based on occupational injuries data

This study is concerned with the characteristics of occupational injuries and sick leave for gas cylinder handling workers. Possible incidents and sick leave have been identified and analyzed for 223 occupational accidents in the gas cylinder handling work. Management level of accidents for prioritizing prevention measures is induced for the combination of accident agencies, types, and gas cylinder handling work processes. Accidents occurring during the specific gas cylinder handling work process showed different characteristics, depending on the type and agency of the accident. Most critical accidents that require corrective actions for prevention were slips and trips caused by floors, walkways, steps, or ground surfaces and overexertion and bodily reaction and posture caused by gas cylinder in the manual delivery of heavy cylinders process. Also, fall to lower level caused by floors, walkways, steps, or ground surfaces in the loading to and unloading from vehicles process and struck by or against caused by fixtures in the manual delivery of heavy cylinders process were also ranked high. The findings of this study can be used to develop more effective accident prevention policies to reduce occupational accidents in gas cylinder handling works.
Large Scale Evaluation of 2,4-D Choline Oﬀ-Target Movement and Injury in 2,4-D Susceptible Soybean 1

® and Enlist Duo ® with Colex-D ® are labeled for use PRE without any plant back interval and POST up to full ﬂowering (R2 growth stage) in Enlist E3 soybean. The Enlist One ® and Enlist Duo ® labels list 2,4-D-susceptible soybean as a compatible crop (Anonymous 2020b), therefore permitting applications of 2,4-D choline in 2,4-D-resistant soybean immediately adjacent to 2,4-D-susceptible soybean, regardless of wind direction. Field background and meterological conditions during 2,4-D choline application at Sun Prairie, WI in 2019 and Arlington, WI in 2020. c Meterological conditions during application. Sun Prairie, 2019: air temperature (77.7 ◦ F), relative humidity (51.9%), wind speed (2.9 mph), wind direction (east/southeast). Arlington, 2020: air temperature (83.7 ◦ F), relative humidity (59.5%), wind speed (2.9 mph), wind direction (north/northeast). No temperature inversions were present during application.
Samrómur Children: An Icelandic Speech Corpus

Samrómur Children is an Icelandic speech corpus intended for the field of automatic speech recognition. It contains 131 hours of read speech from Icelandic children aged between 4 to 17 years. The test portion was meticulously selected to cover a wide range of ages as possible; we aimed to have exactly the same amount of data per age range. The speech was collected with the crowd-sourcing platform Samrómur. is, which is inspired on the “Mozilla’s Common Voice Project”. The corpus was developed within the framework of the “Language Technology Programme for Icelandic 2019 − 2023”; the goal of the project is to make Icelandic available in language-technology applications. Samrómur Children is the first corpus in Icelandic with children’s voices for public use under a Creative Commons license. Additionally, we present baseline experiments and results using Kaldi.