Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflections and Generalizability in Mental Health Assessment

My research lies at the intersection of Computer Science, Computational Social Science, and Social Computing, employing ubiquitous technology to gain insights into human behavior with an emphasis on mental health and productivity . Our behavior significantly impacts our health, overall well-being, and productivity in the workplace. Factors like physical inactivity, social isolation, and limited self-awareness contribute to various ailments and diminish our motivation for productivity. Despite numerous theoretical models aimed at understanding and influencing behavior, their efficacy is often limited as they fail to capture the full range of human behavior and the dynamics that emerge within the real-world environment. Technologies like smartphones and wearables, equipped with sensors, provide a continuous, passive stream of behavioral and physiological data, such as sleep patterns and physical activity levels. This passive data collection can be complemented by active user input through momentary assessments, capturing states like mood and cognitive performance. By analyzing these varied data streams, we can develop a more comprehensive and dynamic understanding of individual behaviors in real-world contexts. I am passionate about harnessing the power of this technology in conjunction with AI to create tools and insights that empower individuals to lead their lives more fully, fostering an environment where everyone can achieve their best in both personal and professional spheres. As an interdisciplinary scholar, my research addresses complex social challenges with a focus on mental health. The multidisciplinary experience allows me to combine the human-centered approach of understanding and framing the challenges with a technical and quantitative approach to designing effective interventions and solutions. I employ diverse empirical methods, from extensive longitudinal data analysis, AI, machine learning, and deep learning to targeted interviews and surveys, to examine individual behaviors in various contexts. For instance, my work includes understanding the impact of COVID-19 on student behavior [1], analyzing the effects of job promotions [2], and even detecting depression through in-the-wild images [3]. Such investigations have enabled me to study the immediate factors influencing behavior and its evolution over time within individuals, leveraging the longitudinal nature of ubiquitous sensing studies and developing innovative methodologies and frameworks. My contributions range from uncovering digital markers of health and risk behavior to advancing translational science with more personalized and timely intervention models .
Raw Data, Rough Mix: Towards an Integrated Practice of Making, Performance and Pedagogy

This paper describes an extended intercontinental collaboration between multiple artists, institutions, and their publics, to develop an integrated musical practice which combines experimental making, performance, and pedagogy. We build on contributions to NIME which work with art and design-led methods to explore alternatives to, for example, more engineering-oriented approaches, without loss of practical utility and theoretical potential. We describe two week-long workshop-residencies and three performance-installations done under the provocative title Raw Data, Rough Mix which was intended to encourage exploration of basic processes in physical, mechanical, electrical, electronic and computational domains to develop musical artefacts that were frugal in their resource-demands but enabled the interrogation of human/non-human relationships, performativity, musical ecologies, aesthetics, and other matters. We close by elaborating our contribution to NIME as offering an integrated practice combining making, playing and learning, which is critically informed and practically productive.
Semantic State Prediction in Robotic Cloth Manipulation

State estimation of deformable objects such as textiles is notorious difficult due to its extreme high dimensionality and complexity. Lack of data and benchmarks is another challenge impeding progress in robotic cloth manipulation. In this paper, we make a first attempt to solve the problem of semantic state estimation through RGB-D data only in an end-to-end manner with the help of deep neural networks. Since neural networks require large amounts of labeled data, we introduce a novel Mujoco simulator to generate a large-scale fully annotated robotic textile manipulation dataset including bimanual actions. Finally, we provide a set of baseline deep neural networks and benchmark them on the problem of semantic state prediction on our proposed dataset.
GOETHE TEST PRO: GERMAN FOR PROFESSIONALS INVESTIGATION OF PSYCHOMETRIC QUALITY

The online German test – the Goethe Test PRO: German for Professionals – has been administered since April 2017 at the test centers of the Goethe Institute and at the sites of corporate customers around the world. Devised as an adaptive online test, it assesses language skills in reading and listening at the workplace at the A1–C2 levels of the Common European Framework of Reference for Languages. In this study, based on the test data of N = 5,636 test takers from 17 countries, the psychometric quality of the Goethe Test PRO was examined with regard to various criteria. The Goethe Test PRO delivered reliable test results, with a reliability of > . 9. The psychometric quality of the test was comparable between the different countries in which the Goethe Test PRO is administered.
WIP: Auditing Artist Style Pirate in Text-to-image Generation Models

—The text-to-image models based on diffusion processes, capable of transforming text descriptions into detailed images, have widespread applications in art, design, and beyond, such as DALL-E, Stable Diffusion, and Midjourney. However, they enable users without artistic training to create artwork comparable to professional quality, leading to concerns about copyright infringement. To tackle these issues, previous works have proposed strategies such as adversarial perturbation-based and watermarking-based methods. The former involves introducing subtle changes to disrupt the image generation process, while the latter involves embedding detectable marks in the artwork. The existing methods face limitations such as requiring modifications of the original image, being vulnerable to image pre-processing, and facing difficulties in applying them to the published artwork. To this end, we propose a new paradigm, called StyleAuditor , for artistic style auditing. StyleAuditor identifies if a suspect model has been fine-tuned using a specific artist’s artwork by analyzing style-related features. Specifically, StyleAuditor employs a style extractor to obtain the multi-granularity style representations and treats artwork as samples of an artist’s style. Then, StyleAuditor queries a trained discriminator to gain the auditing decisions. The results of the experiment on the artwork of thirty artists demonstrate the high accuracy of StyleAuditor , with an auditing accuracy of over 90% and a false positive rate of less than 1.3%.
Improved RRT* Algorithm for Disinfecting Robot Path Planning

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.
Assessment of Training Performance, Degradation and Robustness of Paraffin-Wax Impregnated Nb3Sn Demonstrator Under High Magnetic Field

In the context of high-energy physics, the use of Nb3Sn superconducting magnets as a cost-effective and reliable technology depends on improvements in the following areas: long development and manufacturing cycles, conductor degradation after thermal cycling, long training, as well as a demonstration in accelerator magnets with a beam aperture of the full potential of modern Nb3Sn conductors. In short, performance, robustness, and cost are the three issues to be addressed. The Magnet Development project (MagDev) of the Swiss Accelerator Research and Technology initiative (CHART) at the Paul Scherrer Institute (PSI) aims to contribute to the solutions to each of these issues, re-thinking the manufacturing and design process. In our program, every innovation is to be validated by means of a panoply of fast-turnaround tools: from non-powered and powered samples and coils, tested under background field, to low-field subscale magnets and high field short prototypes. This work presents one element in this panoply of R&D vehicles: a stress-managed Nb3Sn coil called BigBOX, impregnated with paraffin wax, and tested, through a collaboration with the Magnet Development Program of the United States (US-MDP), in the background field of Brookhaven National Laboratory (BNL)’s common coils dipole DCC17.
Numerical Analysis on the Effects of Binary Interaction between Typhoons Tembin and Bolaven in 2012

The binary interaction is one of the most challenging factors to improve the forecast accuracy of multiple tropical cyclones (TCs) in close vicinity. The effect of binary interaction usually results in anomalous track and variable intensity of TCs. A typical interaction type, one-way influence mode, has been investigated by many studies which mainly focused on the anomalous track and record-breaking precipitation, such as typhoons Morakot and Goni. In this paper, a typical case of this type, typhoons Tembin and Bolaven, occurred in the western North Pacific in August 2012, was selected to study how one typhoon impacts the track and intensity of the other one. The vortex of Tembin or Bolaven and the monsoon circulation were removed by a TC bogus scheme and a low-pass Lanczos filter, respectively, to carry out the numerical experiments. The results show that the presence of monsoon made the binary interaction more complex by affecting the tracks and the translation speeds of the TCs. The influence of Bolaven on the track of Tembin was more significant than the influence of Tembin on the track of Bolaven, and the looping track of Tembin was also affected by the external surrounding circulation associated with Bolaven. The absence of Tembin was not conducive to the development of Bolaven due to stronger vertical wind shear condition and the less kinetic energy being transported to upper troposphere. Note that the above analysis demonstrates the interacting processes between TCs and sheds some light on the prediction of binary TCs.
Atypical Endometrial Hyperplasia and Concurrent Cancer: A Comprehensive Overview on a Challenging Clinical Condition

Simple Summary Managing atypical endometrial hyperplasia (AEH) places the gynecologist in a challenging clinical situation. Therapeutic options include conservative or definitive treatment. The main concern is the possibility of concurrent endometrial cancer (EC). This occurrence can lead to undertreatment, whether in the case of conservative or surgical therapy. Therefore, the current literature has worked to identify a better diagnostic therapeutic work-up by focusing on variables predictive of occult cancer, the best preoperative endometrial sampling method, and the possibility of lymph node status assessment for women undergoing surgery. The present review aims to provide a complete overview of all these aspects to summarize the most relevant studies on these topics and better define the clinical management of women with AEH. Abstract The present review regarding atypical endometrial hyperplasia (AEH) focused on the main debated factors regarding this challenging clinical condition: (i) predictive variables of occult endometrial cancer (EC); (ii) the rate of EC underestimation according to different endometrial sampling methods; and (iii) the appropriateness of lymph node status assessment. When cancer is detected, approximately 90% of cases include low-risk EC, although intermediate/high-risk cases have been found in 10–13% of women with cancer. Older age, diabetes, high BMI, and increased endometrial thickness are the most recurrent factors in women with EC. However, the predictive power of these independent variables measured on internal validation sets showed disappointing results. Relative to endometrial sampling methods, hysteroscopic endometrial resection (Hys-res) provided the lowest EC underestimation, ranging between 6 and 11%. Further studies, including larger sample sizes of women undergoing Hys-res, are needed to confirm these findings. These data are urgently needed, especially for female candidates for conservative treatment. Finally, the evaluation of lymph node status measured on 660 of over 20,000 women showed a lymph node positivity of 2.3%. Although there has been an increase in the use of this procedure in AEH in recent years, the present data cannot recommend this option in AEH based on a cost/risk/benefit ratio.
A Passive Signal Focusing Algorithm Based on Synthetic Aperture Technique for Multiple Radiation Source Localization

The Doppler dispersion of the received signal is very severe when the beamwidth of the antenna is wide, resulting in a decrease in localization accuracy for multiple radiation source localization. To resolve the problem, we propose a passive signal focusing algorithm (PSFA) for multiple radiation sources localization based on a full-aperture model. The full-aperture model overcomes the resolution degradation in conventional subaperture processing. In the PSFA, the residual frequency correction (RFC) eliminates the localization bias in the azimuth domain and the instantaneous Doppler compensation (IDC) resolves the Doppler dispersion in the range domain, improving localization accuracy. The matched filtering is used to complete precise azimuthal focusing and the locations are obtained according to the focusing results. Moreover, the Cramer–Rao lower bound (CRLB) for synthetic aperture localization is derived. The CRLB is essential for evaluating algorithms in theoretical studies and designing system parameters in practical applications. Finally, the CRLB, simulation, and acquired data are used to evaluate the localization performance.
Genome-Based Taxonomy of Species in the Pseudomonas syringae and Pseudomonas lutea Phylogenetic Groups and Proposal of Pseudomonas maioricensis sp. nov., Isolated from Agricultural Soil

Species in the phylogenetic group Pseudomonas syringae are considered the most relevant plant pathogenic bacteria, but their taxonomy is still controversial. Twenty named species are validated in the current taxonomy of this group and in recent years many strains have been genome-sequenced, putative new species have been proposed and an update in the taxonomy is needed. A taxonomic study based on the core-genome phylogeny, genomic indices (ANI and GGDC) and gene content (phyletic pattern and Jaccard index) have been applied to clarify the taxonomy of the group. A phylogenomic analysis demonstrates that at least 50 phylogenomic species can be delineated within the group and that many strains whose genomes have been deposited in the databases are not correctly classified at the species level. Other species names, like “Pseudomonas coronafaciens”, have been proposed but are not validated yet. One of the putative new species is taxonomically described, and the name Pseudomonas maioricensis sp. nov. is proposed. The taxonomies of Pseudomonas avellanae and Pseudomonas viridiflava are discussed in detail as case studies. Correct strain identification is a prerequisite for many studies, and therefore, criteria are given to facilitate identification.
Exploring the Influence of Prompts in LLMs for Security-Related Tasks

—In recent years, large language models (LLMs) have been widely used in security-related tasks, such as security bug identification and patch analysis. The effectiveness of LLMs in these tasks is often influenced by the construction of appropriate prompts. Some state-of-the-art research has proposed multiple factors to improve the effectiveness of building prompts. However, the influence of prompt content on the accuracy and efficacy of LLMs in executing security tasks remains underexplored. Addressing this gap, our study conducts a comprehensive ex-periment, assessing various prompt methodologies in the context of security-related tasks. We employ diverse prompt structures and contents and evaluate their impact on the performance of LLMs in security-related tasks. Our findings suggest that appropriately modifying prompt structures and content can significantly enhance the performance of LLMs in specific security tasks. Conversely, improper prompt methods can markedly reduce LLM effectiveness. This research not only contributes to the understanding of prompt influence on LLMs but also serves as a valuable guide for future studies on prompt optimization for security tasks. Our code and dataset is available at Wayne-Bai/Prompt-Affection.
Development of a Model Performance Metric and Its Application to Assess Summer Precipitation over the U.S. Great Plains in Downscaled Climate Simulations

AbstractSeveral dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors’ efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmoller diagram, and di.
Tracing the temporal evolution of soil redistribution rates in an agricultural landscape using 239+240Pu and 10Be

Two principal groups of processes shape mass fluxes from and into a soil: vertical profile development and lateral soil redistribution. Periods having predominantly progressive soil forming processes (soil profile development) alternate with periods having predominantly regressive processes (erosion). As a result, short‐term soil redistribution – years to decades – can differ substantially from long‐term soil redistribution; i.e. centuries to millennia. However, the quantification of these processes is difficult and consequently their rates are poorly understood. To assess the competing roles of erosion and deposition we determined short‐ and long‐term soil redistribution rates in a formerly glaciated area of the Uckermark, northeast Germany. We compared short‐term erosion or accumulation rates using plutonium‐239 and ‐240 (239+240Pu) and long‐term rates using both in situ and meteoric cosmogenic beryllium‐10 (10Be). Three characteristic process domains have been analysed in detail: a flat landscape position having no erosion/deposition, an erosion‐dominated mid‐slope, and a deposition‐dominated lower‐slope site. We show that the short‐term mass erosion and accumulation rates are about one order of magnitude higher than long‐term redistribution rates. Both, in situ and meteoric 10Be provide comparable results. Depth functions, and therefore not only an average value of the topsoil, give the most meaningful rates. The long‐term soil redistribution rates were in the range of −2.1 t ha‐1 yr‐1 (erosion) and +0.26 t ha‐1 yr‐1 (accumulation) whereas the short‐term erosion rates indicated strong erosion of up to 25 t ha‐1 yr‐1 and accumulation of 7.6 t ha‐1 yr‐1. Our multi‐isotope method identifies periods of erosion and deposition, confirming the ‘time‐split approach’ of distinct different phases (progressive/regressive) in soil evolution. With such an approach, temporally‐changing processes can be disentangled, which allows the identification of both the dimensions of and the increase in soil erosion due to human influence. © 2019 John Wiley & Sons, Ltd.
Dynamic Thermal Management in SOI Transistors Using Holey Silicon-Based Thermoelectric Cooling

Silicon-on-insulator (SOI) technology offers many advantages for transistors while also presenting significant challenges in thermal management. The buried oxide (BOX) layer impedes vertical heat dissipation, rendering conventional cooling solutions based on air and liquid coolers inefficient. In this study, we propose a novel theoretical design of a holey silicon-based lateral thermoelectric cooler (TEC) that provides a unique cooling solution for SOI transistors. By redistributing heat laterally, this TEC overcomes the limitation of poor vertical heat dissipation caused by the BOX layer, meanwhile taking advantage of the sustained temperature gradient in the device layer. In addition, we present a novel precooling strategy which utilizes a long-time, optimal-voltage TEC cooling pulse to cool down a short-time, high-power-density heating pulse. We evaluate the cooling performance through simulation of two devices: an SOI transistor-TEC device and a <inline-formula> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> SOI transistor-TEC array device. Under a 10 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> transient heat pulse with high-power density of 11000 W/cm2 (1.6 W), the SOI transistor-TEC device with a 10 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> thick BOX layer can be cooled from 465.7 °C down to 407.4 °C (<inline-formula> <tex-math notation="LaTeX">$\Delta {T}$ </tex-math></inline-formula> = 58.3 °C) in peak hotspot temperature when precooled with a 1000 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> TEC pulse, consuming 0.20 W of TEC power. Furthermore, the SOI transistor-TEC array device provides spatial temperature control by incorporating multiple coolers which enable selective cooling for individual transistor units without significant loss of cooling performance. In summary, our TEC design exhibits great potential in offering dynamic thermal management for a wide range of advanced SOI devices.