Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle \emph{domain-invariant} crowd and \emph{domain-specific} background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a weakly-supervised manner. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide crowd features transfer across domains beyond background distractions. The CDA module dedicates to regularising target-domain crowd density generation by its own crowd density distribution. Our method outperforms previous approaches consistently in the widely-used adaptation scenarios.Comment: 10 pages, 5 figures, and 9 table

Emergent order in the spin-frustrated system DyxTb2-xTi2O7 studied by ac susceptibility measurements

We report the a.c. susceptibility study of Dy_xTb_{2-x}Ti_2O_7 with x in [0, 2]. In addition to the single-ion effect at Ts (single-ion effect peak temperature) corresponding to the Dy3+ spins as that in spin ice Dy_2Ti_2O_7 and a possible spin freezing peak at Tf (Tf < 3 K), a new peak associated with Tb^{3+} is observed in $\chi_{ac}(T)$ at nonzero magnetic field with a characteristic temperature T^* (Tf < T^* < Ts). T^* increases linearly with x in a wide composition range (0 < x < 1.5 at 5 kOe). Both application of a magnetic field and increasing doping with Dy3+ enhance T^*. The T^* peak is found to be thermally driven with an unusually large energy barrier as indicated from its frequency dependence. These effects are closely related to the crystal field levels, and the underlying mechanism remains to be understood.Comment: 7 pages, 5 figure

Does Incident Solar Ultraviolet Radiation Lower Blood Pressure?

Background Hypertension remains a leading global cause for premature death and disease. Most treatment guidelines emphasize the importance of risk factors, but not all are known, modifiable, or easily avoided. Population blood pressure correlates with latitude and is lower in summer than winter. Seasonal variations in sunlight exposure account for these differences, with temperature believed to be the main contributor. Recent research indicates that UV light enhances nitric oxide availability by mobilizing storage forms in the skin, suggesting incident solar UV radiation may lower blood pressure. We tested this hypothesis by exploring the association between environmental UV exposure and systolic blood pressure (SBP) in a large cohort of chronic hemodialysis patients in whom SBP is determined regularly. Methods and Results We studied 342 457 patients (36% black, 64% white) at 2178 US dialysis centers over 3 years. Incident UV radiation and temperature data for each clinic location were retrieved from the National Oceanic and Atmospheric Administration database. Linear mixed effects models with adjustment for ambient temperature, sex/age, body mass index, serum Na+/K+ and other covariates were fitted to each location and combined estimates of associations calculated using the DerSimonian and Laird procedure. Pre-dialysis SBP varied by season and was ≈4 mm Hg higher in black patients. Temperature, UVA and UVB were all linearly and inversely associated with SBP. This relationship remained statistically significant after correcting for temperature. Conclusions In hemodialysis patients, in addition to environmental temperature, incident solar UV radiation is associated with lower SBP. This raises the possibility that insufficient sunlight is a new risk factor for hypertension, perhaps even in the general population.</p

Internet addiction, loneliness, and academic burnout among Chinese college students: a mediation model

BackgroundThe dynamics of education and student life have changed since the COVID-19 pandemic. Our society, especially the education system, has become largely dependent on the Internet. This paradigm shifts largely took place in the last few decades. As such, there are various ways in which we cannot comprehend the impact that the Internet can have on student psychology, and how multiple other factors could influence that. Internet addiction and its relationship with academic burnout, along with the impact of loneliness, are all essential factors that must be discussed candidly in the post-COVID-19 era. Hence, the objective of this study was, therefore, to explore the relationship between Internet addiction, loneliness, and academic burnout among Chinese college students as well as the mediating role of loneliness.MethodsWe conducted a cross-sectional questionnaire survey at a Chinese university from October to November 2022. In total, 810 valid respondents were selected via random cluster sampling using the well-established Internet Addiction, Loneliness, and Academic Burnout Scale. The primary approach of mediation analysis and structural equation modeling testing examined the relationships among the three components.ResultsInternet addiction could be responsible for academic burnout among college students. Loneliness partially mediates the relationship between Internet addiction and academic burnout. In a mediated way, different types of loneliness contribute to different types of academic burnout.ConclusionPsychological interventions for loneliness, especially emotional loneliness prevention, are the critical aspects of the problem of Internet addiction accompanied with academic burnout. The causal relationship between Internet addiction and academic burnout, possibly of a two-way nature, needs to be further explored in the next future

Towards Evaluating Pitch-Related Phonation Function in Speech Communication Using High-Density Surface Electromyography

Pitch, as a sensation of the sound frequency, is a crucial attribute toward constructing a natural voice for communication. Producing intelligible sounds with normal pitches depend on substantive interdependencies among facial and neck muscles. Clarifying the interrelations between the pitches and the corresponding muscular activities would be helpful for evaluating the pitch-related phonating functions, which would play a significant role both in training pronunciation and in assessing dysphonia. In this study, the speech signals and the high-density surface electromyography (HD sEMG) signals were synchronously acquired when phonating [a:], [i:], and [ә:] vowels with increasing pitches, respectively. The HD sEMG energy maps were constructed based on the root mean square values to visualize spatiotemporal characteristics of facial and neck muscle activities. Normalized median frequency (nMF) and root-mean square (nRMS) were correspondingly extracted from the speech and sEMG recordings to quantitatively investigate the correlations between sound frequencies and myoelectric characteristics. The results showed that the frame-wise energy maps built from sEMG recordings presented that the muscle contraction strength increased monotonously across pitch-rising, with left-right symmetrical distribution for the face/neck. Furthermore, the nRMS increased at a similar rate to the nMF when there were rising pitches, and the two parameters had a significant correlation across different vowel tasks [(a:) (0.88 ± 0.04), (i:) (0.89 ± 0.04), and (ә:) (0.87 ± 0.05)]. These findings suggested the possibility of utilizing muscle contraction patterns as a reference for evaluating pitch-related phonation functions. The proposed method could open a new window for developing a clinical approach for assessing the muscular functions of dysphonia

Two Homologous Putative Protein Tyrosine Phosphatases, OsPFA-DSP2 and AtPFA-DSP4, Negatively Regulate the Pathogen Response in Transgenic Plants

Protein phosphatases, together with protein kinases, regulate protein phosphorylation and dephosphorylation, and play critical roles in plant growth and biotic stress responses. However, little is known about the biological functions of plant protein tyrosine dual-specificity phosphatase (PFA-DSP) in biotic stresses. Here, we found that OsPFA-DSP2 was mainly expressed in calli, seedlings, roots, and young panicles, and localized in cytoplasm and nucleus. Ectopic overexpression of OsPFA-DSP2 in rice increased sensitivity to Magnaporthe grisea (M. grisea Z1 strain), inhibited the accumulation of hydrogen peroxide (H2O2) and suppressed the expression of pathogenesis-related (PR) genes after fungal infection. Interestingly, transgenic Arabidopsis plants overexpressing AtPFA-DSP4, which is homologous to OsPFA-DSP2, also exhibited sensitivity to Pseudomonas syringae pv. tomato DC3000 (Pst DC3000), reduced accumulation of H2O2 and decreased photosynthesic capacity after infection compared with Col-0. These results indicate that OsPFA-DSP2 and AtPFA-DSP4 act as negative regulators of the pathogen response in transgenic plants

Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model

A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively

Effects of reinforcement instructions and BSC presentation format on performance evaluation judgments

A study carried out using experiments to examine how reinforcement instruction and chunking treatments on the Balanced scorecard affect performance evaluation of test subjects