Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach

The ability to sense, localize, and estimate the 3D position and orientation of the human body is critical in virtual reality (VR) and extended reality (XR) applications. This becomes more important and challenging with the deployment of VR/XR applications over the next generation of wireless systems such as 5G and beyond. In this paper, we propose a novel framework that can reconstruct the 3D human body pose of the user given sparse measurements from Inertial Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically, our framework enables reliable transmission of compressed IMU signals through noisy wireless channels and effective recovery of such signals at the receiver, e.g., an edge server. This task is very challenging due to the constraints of transmit power, recovery accuracy, and recovery latency. To address these challenges, we first develop a deep generative model at the receiver to recover the data from linear measurements of IMU signals. The linear measurements of the IMU signals are obtained by a linear projection with a measurement matrix based on the compressive sensing theory. The key to the success of our framework lies in the novel design of the measurement matrix at the transmitter, which can not only satisfy power constraints for the IMU devices but also obtain a highly accurate recovery for the IMU signals at the receiver. This can be achieved by extending the set-restricted eigenvalue condition of the measurement matrix and combining it with an upper bound for the power transmission constraint. Our framework can achieve robust performance for recovering 3D human poses from noisy compressed IMU signals. Additionally, our pre-trained deep generative model achieves signal reconstruction accuracy comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster.
Fundamental Characteristics of Slipper Bearings in Swash Plate Type Axial Piston Pumps and Motors

Fundamental concepts for analyzing the characteristics of slipper bearings operating in swash plate type axial piston pumps and motors are developed. On the basis of the calculated bearing characteristics, the generation mechanism of load-carrying capacity of the overclamped slipper bearing is clarified. The attitudes, i. e. floating height and tilt angle, of the slipper bearing are calculated under the quasi-static condition, assuming that the contributions of hydrodynamic oil film force and oil shearing force due to relative sliding motion of the slipper bearing can be neglected. It is also suggested that wear at the periphery of the bearing surface, which may be due to contacts of the bearing with the swash plate, is essential for successful bearing operation.
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats

Over the past decade, there has been extensive research aimed at enhancing the robustness of neural networks, yet this problem remains vastly unsolved. Here, one major impediment has been the overestimation of the robustness of new defense approaches due to faulty defense evaluations. Flawed robustness evaluations necessitate rectifications in subsequent works, dangerously slowing down the research and providing a false sense of security. In this context, we will face substantial challenges associated with an impending adversarial arms race in natural language processing, specifically with closed-source Large Language Models (LLMs), such as ChatGPT, Google Bard, or Anthropic's Claude. We provide a first set of prerequisites to improve the robustness assessment of new approaches and reduce the amount of faulty evaluations. Additionally, we identify embedding space attacks on LLMs as another viable threat model for the purposes of generating malicious content in open-sourced models. Finally, we demonstrate on a recently proposed defense that, without LLM-specific best practices in place, it is easy to overestimate the robustness of a new approach.
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification

Among applications of deep learning (DL) involving low cost sensors, remote image classification involves a physical channel that separates edge sensors and cloud classifiers. Traditional DL models must be divided between an encoder for the sensor and the decoder + classifier at the edge server. An important challenge is to effectively train such distributed models when the connecting channels have limited rate/capacity. Our goal is to optimize DL models such that the encoder latent requires low channel bandwidth while still delivers feature information for high classification accuracy. This work proposes a three-step joint learning strategy to guide encoders to extract features that are compact, discriminative, and amenable to common augmentations/transformations. We optimize latent dimension through an initial screening phase before end-to-end (E2E) training. To obtain an adjustable bit rate via a single pre-deployed encoder, we apply entropy-based quantization and/or manual truncation on the latent representations. Tests show that our proposed method achieves accuracy improvement of up to 1.5% on CIFAR-10 and 3% on CIFAR-100 over conventional E2E cross-entropy training.
CHARACTERIZING MAJOR FRONTAL SYSTEMS: A NOWCAST/FORECAST SYSTEM FOR THE NORTHWEST ATLANTIC

T H E U.S. NAVY expends a considerable effort to determine the locations and properties of most major ocean frontal systems across the globe. This synoptic picture, called a nowcast, is partly constructed using expendable bathythermographs dropped from ships (XBTs) and airplanes (AXBTs). Such in situ measurements can give very detailed information, but the cost limits their utility to relatively localized and short-lived surveys. Fortunately, given an adequate database of earlier measurements, simply knowing the surface location of a front is often sufficient to reconstruct an accurate picture of the three-dimensional thermal structure of the water column to depths of thousands of meters. Obtaining that surface information is not a trivial task however. Satellite infrared-radiometer (IR) images provide locations of important mesoscale features over large areas, but clouds can obscure important fronts for long periods, and the surface thermal structure is not always an accurate guide to the actual location of the predominant currents. An unknown geoid and other problems not withstanding (Mitchell et al. 1990), satellite altimetry can provide accurate locations of currents and related features. In many areas of interest, the time and space scales on which the oceans evolve are not adequately sampled, however (Hurlburt. 1984: Kindle, 1986; Thompson, 1986). For example, the U.S. Navy's Geodetic Earth Orbiting Satellite (GEOSAT) (Born et al., 1987) with its 17-day repeat cycle and equatorial track separation of less than 150 km was adequate to sample regions such as the Gulf of Mexico, but was not sufficient to provide a synoptic picture for the evolution of the Gulf Stream. One approach to the sampling problem is to combine computer models of the oceans with data assimilation methods to extract the most infor-
Analysis of Employee Engagement with Transformational Leadership and Organizational Climate as Predictors

Employees who have a high level of work engagement will show their best performance; this is because these employees enjoy the work they do. When employees are attached to a company, employees have an awareness of the business. The main objective of this research is to analyze the influence of transformational leadership and organizational climate on employee engagement. The research methods include library and field research designs with a quantitative approach. The number of samples analyzed was 140 employees. This study uses a research instrument test consisting of validity and reliability tests. The quantitative analysis consists of a normality test, regression test, hypothesis test, correlation test, and coefficient of determination. The results of the data analysis conclude that transformational leadership, organizational climate, and employee engagement positively and significantly influence innovative work behavior. The results of further research confirm that transformational leadership and organizational climate significantly affect employee engagement. The research conclusion states that by combining transformational leadership and a positive organizational climate, organizations can create an environment where employees feel encouraged to give their best and feel that their contributions positively impact the organization's success. High employee engagement contributes to better productivity, retention, and continued organizational success.
Empresas multinacionais e inovação tecnológica no Brasil

The article examines the technological efforts carried out _ presently and prospectively _ by multinational enterprises in Brazil. It argues that these enterprises are willing to increase their R&D activities abroad which may represent an opportunity to Brazil to develop its national system of innovation. However, the adoption of appropriate policies is an important condition to succeed in this endeavour.
Predicting Soil Drainage Class Using Remotely Sensed and Digital Elevation Data

Soil drainage strongly affects the patterns and processes of ecosystems, including biomass production, vegetation community distribution, soil development, aeration, hydrologic processes, and trace gas fluxes. To obviate the need for extensive field surveys, we present a technique to use a remotely sensed optical image and digital elevation data to predict soil drainage class at a 6- by 4-km research site in a mixed conifer forest in Howland, Maine. Elevation, detrended elevation, slope, aspect, and pow accumulation were determined from a 10-m resolution digital elevation model (DEM) of the site. Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Visible and Infrared Imaging Spectrometer (AVIRIS) were used to represent differences in vegetation cover. Classification tree analysis predicted soil drainage class with an average of 78 percent accuracy.
Science Arts & Métiers (SAM)

Several reasons for the use of multidisciplinary teams composed of individuals with natural science and engineering background in problem ‐ solving processes exist. The most important are the integration of science ‐ based technologies into products and processes, and benefits for the problem ‐ solving process thanks to new knowledge and new perspectives on problems. In this study we analyse the implications of interdisciplinary (science – engineering) group problem solving from a managerial as well as from a cognitive perspective. We then report on an experiment investigating the impact of problem ‐ relevant disciplinary group composition and methodological support on the problem ‐ solving process and its outcome. The findings of the experiment have managerial, theoretical, and pedagogical implications related to early phases of New Product/Process Design processes in high ‐ technology and scientific knowledge ‐ related domains.
Scalable Two-Minute Feedback: Digital, Lecture-Accompanying Survey as a Continuous Feedback Instrument

Detailed feedback on courses and lecture content is essential for their improvement and also serves as a tool for reflection. However, feedback methods are often only used sporadically, especially in mass courses, because collecting and analyzing feedback in a timely manner is often a challenge for teachers. Moreover, the current situation of the students or the changing workload during the semester are usually not taken into account either. For a holistic investigation, the article used a digital survey format as formative feedback which attempts to measure student stress in a quantitative part and to address the participants' reflection in a qualitative part, as well as to collect general suggestions for improvement (based on the so-called One-Minute Paper) at two educational institutions. The feedback during the semester is evaluated qualitatively and discussed on a meta-level and special features (e.g. reflections on student work ethic or other courses) are addressed. The results show a low, but constant rate of feedback. Responses mostly cover topics of the lecture content or organizational aspects and were intensively used to report issues within the lecture. In addition, artificial intelligence (AI) support in the form of a large language model was tested and showed promising results in summarizing the open-ended responses for the teacher. Finally, the experiences from the lecturers are reflected upon and the results as well as possibilities for improvement are discussed.
Petrogenesis of Cenozoic, Temperate Water Calcarenites, South Australia: A Model for Meteoric/Shallow Burial Diagenesis of Shallow Water Calcite Sediments

ABSTRACT The Gambler Limestone is one of several extensive, shallow-water shelf carbonates of Eocene to Miocene age exposed along the southern margin of Australia. It is a muddy to grainy bryozoan calcarenite, with accessory benthonic foraminifers and echinoids. The sediments, originally composed almost entirely of calcite or Mg-calcite, have been in vadose and phreatic environments for over 10 Ma, yet are virtually unlithified. The only cements of any consequence are epitaxial on echinoids. Numerous karst features, dolines, caves, speleothems and surface karren attest to prolonged residence in the meteoric zone. The Gambler Limestone is presently one of the best fresh water aquuifers in Australia. Most flow occurs through intergranular pores in sediments with over 30% porosity. Cementation is by minor intergranular pressure solution which has developed under overburden of less than 100 m. The overlying Naracoorte Limestone (Miocene), a calcarenite of warmer water aspect, contains numerous aragonite molds and is well-cemented. We propose that such cool water limestones are a better model for the meteoric diagenesis of calcite sediments of all ages than are aragonite-rich tropical sediments. It is probable that many similar early and middle Paleozoic calcite limestones may have been in the meteoric zone for prolonged periods yet contain little or no petrographic or geochemical record of such exposure.
Pet ownership and mental health in United States adults during COVID-19

The COVID-19 pandemic was associated with declines in mental health and increased interest in pet ownership. We aimed to extend past theories and research linking pet ownership and mental health by investigating whether pet ownership was associated with mental health during the initial phases of the COVID-19 pandemic in a sample of American adults. We also tested whether the association of pet ownership and mental health was moderated by relationship status. Participants were 2,906 American adults who were recruited for an online survey study between May 2020 and May 2021. Pet ownership was assessed via dichotomous self-report (yes/no) and mental health was assessed using a 13-item questionnaire. The sample was 69.2% female with an average age of 46.0 years. 36.1% of the sample owned a pet and 68.5% of the sample was currently partnered. There was no overall association of pet ownership and mental health during the COVID-19 pandemic (estimated mean difference (EMD) = 0.35, 95CI = −0.10, 0.80, p = 0.12). However, we found evidence for an association that was moderated by relationship status. Pet ownership was associated with better mental health among partnered individuals (EMD = 0.76, 95CI = 0.21, 1.30, p = 0.006). There was no association of pet ownership and mental health among unpartnered individuals (EMD = −0.41, 95CI = −1.20, 0.37, p = 0.30). Our findings suggest that relationship status may represent a critical moderator of the link between pet ownership and mental health. Future studies are needed to identify specific mechanisms of pet ownership that could explain its varied impact on the mental health of partnered and unpartnered individuals.
Long-term efficacy and adverse effects of cannabidiol in adjuvant treatment of drug-resistant epilepsy: a systematic review and meta-analysis

Background: Epilepsy is one of the most common chronic brain diseases. Almost one-third of patients have drug-resistant epilepsy (DRE). Cannabidiol is being considered as a potential novel drug for treating DRE. Objectives: To investigate long-term efficacy and safety of cannabidiol in treatment of DRE and the differences in cannabidiol treatment among patients with different characteristics. Design: Systematic review and meta-analysis. Data sources and methods: Medline, Embase, and CENTRAL were searched for literature. RevMan5.4 was used for meta-analysis. The Intention-to-treat set and the random effect were used as the main analysis. Subgroup analyses were performed according to age, dose, concomitant antiseizure medications (ASMs), epilepsy syndromes, and study designs. Results: Fifty studies were included in this systematic review. A total of 4791 participants were collected. The responder rates (seizure frequency reduced at least 50%) at 12-, 24-, 48-, 72-, 96-, and 144-week were 0.40 [0.36, 0.45], 0.39 [0.34, 0.44], 0.37 [0.30, 0.44], 0.27 [0.17, 0.37], 0.22 [0.14, 0.30], and 0.38 [0.23, 0.53]. Seizure-free rates were 0.04 [0.03, 0.06], 0.04 [0.03, 0.05], 0.03 [0.02, 0.05], 0.03 [0.02, 0.03], 0.02 [0.01, 0.03], and 0.04 [0.01, 0.06]. Proportion of adverse events were 0.72 [0.61, 0.83], 0.62 [0.42, 0.81], 0.60 [0.41, 0.79], 0.35 [0.14, 0.56], 0.83 [0.75, 0.90], and 0.96 [0.94, 0.99]. The pooled 12-, 24-, 48-, 96-, and 144-week proportion of serious adverse events were 0.15 [0.09, 0.21], 0.23 [0.14, 0.31], 0.10 [0.06, 0.15], 0.31 [0.24, 0.38], and 0.40 [0.35, 0.45]. Subgroup analyses showed that there was no significant difference on efficacy and safety among age subgroups and epilepsy syndromes subgroups. For most periods, there were no significant difference on efficacy among subgroups of dose and concomitant ASMs. However, higher doses and more concomitant ASMs were associated with higher proportion of adverse events. Conclusion: Cannabidiol treatment of DRE has stable efficacy and fewer adverse events in early period. Long-term use may have decreased efficacy and increased adverse events. Dose escalation may not increase efficacy, but may increase adverse events. Furthermore, cannabidiol use may reduce dosage of other ASMs without reducing efficacy, thereby reducing adverse effects. Cannabidiol may have similar effects in various epilepsy syndromes. Trial registration: PROSPERO (CRD42022351250).
Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

Simple Summary This study demonstrated a correlation between the apparent diffusion coefficient (ADC) of diffusion-weighted images and potentially malignant prostate lesions, with even better correlation when using the ratio of ADC and prostate-specific antigen density (PSAD), or ADC/PSAD, as a biomarker. Threshold values were determined in order to distinguish between histological cancer grades. Retrospectively, 403 patients with a total of 468 prostate lesions were enrolled. All patients had undergone multiparametric magnetic resonance imaging (mpMRI) and a subsequent biopsy of their prostate lesions, whereby their histological cancer grade was determined. Lower ADC values and lower ADC/PSAD ratios correlated with higher histological cancer stages. Therefore, it was possible to distinguish between histological cancer stages, and thus to reduce the number of unnecessary biopsies. Abstract The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.
THE INTERACTION OF BRENT GEESE BRANTA BERNICLA AND SEA PLANTAIN PLANTAGO MARITIMA DURING SPRING STAGING: FIELD OBSERVATIONS AND EXPERIMENTS

SUMMARY In spring, migrating Brent Geese (Branta bernicla) stage on the salt-marsh of Schiermonnikoog, a Dutch island in the Wadden Sea. During May, their principal food plants on the marsh are Puccinellia maritima and Plantago maritima. The Brent graze in large groups, and regraze the same places every four days. This interval seems to be related to the rate of regrowth of their food plants. A close study of the grazing of Plantago by the Brent revealed that the bite size is about 14 mm and is almost exclusively confined to the youngest leaves. Each time the geese harvest about 30 % of the rosette's leaf material. A clipping experiment on the salt-marsh with Plantago, showed that a regime of harvesting about 30 % of the rosette once every four days, like the geese do, gave the highest regrowth of new plant tissue, compared with no clipping, or with other clipping treatments. The selective advantage to the individual in taking part in flock grazing movements is discussed in relation to the effects of Brent grazing on the vegetation.
PENGARUH ASIMETRI INFORMASI TERHADAP PRAKTIK MANAJEMEN LABA DENGAN MEKANISME CORPORATE GOERNANCE SEBAGAI VARIABEL MODERASI

Penelitian ini bertujuan untuk menguji pengaruh asimetri informasi terhadap manajemen laba serta menguji pengaruh mekanisme corporate governance terhadap hubungan antara asimetri informasi dan manajemen laba. Asimetri informasi diukur dengan relative bid-ask spread , manajemen laba diukur dengan model discretionary revenue (Stubben, 2010) dan mekanisme corporate governance sebagai variable moderasi diukur dengan ukuran dewan komisaris, komite audit dan kualitas audit. Variabel kontrol dalam penelitian ini adalan ukuran perusahaan. Penelitian ini menggunakan 24 sampel perusahaan yang termasuk dalam indeks LQ-45 secara konsisten selama enam periode atau tiga tahun dari tahun 2009 sampai tahun 2011 sehingga didapatkan total 72 sampel. Pengambilan sampel menggunakan metode purposive sampling. Penelitian ini menggunakan metode analisis regresi untuk menguji pengaruh antar variable. Hasil penelitian menunjukkan bahwa asimetri informasi berpengaruh positif dan signifikan terhadap manajemen laba. Ukuran dewan komisaris dapat memoderasi hubungan antara asimetri informasi dan manajemen laba. Sedangakan ukuran komite audit dan kualitas audit tidak berpengaruh terhadap hubungan antara asimetri informasi dan manajemen laba. Ukuran perusahaan sebagai variable control berpengaruh positif dan signifikan terhadap manajemen laba.