DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data

With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information. Edge-based machine learning (ML) methodologies are often employed to analyze locally collected data. However, a fundamental issue across data-driven ML approaches is distribution shift. It occurs when a model is deployed on a data distribution different from what it was trained on, and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) have been proposed to capture spatial and temporal dependencies in multi-sensor time series data, requiring intensive computational resources beyond the capacity of today's edge devices. While brain-inspired hyperdimensional computing (HDC) has been introduced as a lightweight solution for edge-based learning, existing HDCs are also vulnerable to the distribution shift challenge. In this paper, we propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO leverages efficient and parallel matrix operations on high-dimensional space to dynamically identify and filter out domain-variant dimensions. Our evaluation on a wide range of multi-sensor time series classification tasks shows that DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers 16.34x faster training and 2.89x faster inference. More importantly, DOMINO performs notably better when learning from partially labeled and highly imbalanced data, providing 10.93x higher robustness against hardware noises than SOTA DNNs

AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition

The paper describes our proposed methodology for the six basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression recognition (FER) methods aim to learn the representation of expression from the artificially generated data and generalise to real data. Because of the ambiguous of the synthetic data and the objectivity of the facial Action Unit (AU), we resort to the AU information for performance boosting, and make contributions as follows. First, to adapt the model to synthetic scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose a conceptually-new framework, termed as AU-Supervised Convolutional Vision Transformers (AU-CVT), which clearly improves the performance of FER by jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The source code of our work is publicly available online: https://github.com/msy1412/ABAW

Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis

With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions. Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate three-dimensional (3D) structures. The novel view synthesis quality drops dramatically given sparse input due to the implicitly reconstructed inaccurate 3D-scene structure. We propose SfMNeRF, a method to better synthesize novel views as well as reconstruct the 3D-scene geometry. SfMNeRF leverages the knowledge from the self-supervised depth estimation methods to constrain the 3D-scene geometry during view synthesis training. Specifically, SfMNeRF employs the epipolar, photometric consistency, depth smoothness, and position-of-matches constraints to explicitly reconstruct the 3D-scene structure. Through these explicit constraints and the implicit constraint from NeRF, our method improves the view synthesis as well as the 3D-scene geometry performance of NeRF at the same time. In addition, SfMNeRF synthesizes novel sub-pixels in which the ground truth is obtained by image interpolation. This strategy enables SfMNeRF to include more samples to improve generalization performance. Experiments on two public datasets demonstrate that SfMNeRF surpasses state-of-the-art approaches. Code is available at https://github.com/XTU-PR-LAB/SfMNeR

Role of barrier layer in the developing phase of "Category 6" super typhoon Haiyan

With the remarkable intensity of 170 knots, Typhoon Haiyan starts as a tropical depression on November 3 and develops to the peak as super tropical cyclone (TC) on November 7 in 2013. This intensity makes Haiyan one of the strongest TCs record ever observed and 35 knots higher than the maximum of the existing highest category. Haiyan originated from the eastern part of the Northwest Pacific Warm Pool and moved westward over warm water with a thick barrier layer (BL). The BL reduced the vertical mixing and entrainment caused by Haiyan and prevented the cold thermocline water into the mixed layer (ML). As a result, sea temperature cooling associated with wind stirring was suppressed. Relative high sea surface temperature (SST) kept fueling Haiyan via latent heat flux release, which favored the rapid development of a "Category 6" super typhoon

Clinical dilemma and systemic treatment strategy of triple-negative breast cancer in the elderly

There is no specific biomarker for triple-negative breast cancer (TNBC), and chemotherapy remains as backbone but with limited efficacy and more side effects. 10%-21% TNBC are elderly patients with high prevalence of concomitant cardio-cerebrovascular and renal complications, which may lead to intolerance of chemotherapy. How to properly treat these elderly TNBC patients becomes a big challenge during daily clinical practice. To date, few clinical trials specifically focus on elderly TNBC patients, thus no enough evidence-based safety and efficacy data are provided to support the proper therapy to this population. There are also challenges and controversies in the diagnosis and treatment. Elderly TNBC patients have special age-related disease characteristics and high non-cancer related mortality. Therefore, it is very important to balance survival benefit and quality of life during the treatment. This paper summarized data of the epidemiology, tumor biological behavior, current diagnosis and treatment status and the huge unmet medical needs of elderly TNBC patients, and explored the benefits of novel antibody-drug conjugate (ADC), to provide certain guidance on systemic treatment strategies for elderly TNBC patients

DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms

This paper proposes DISQ to craft a stable landscape for VQA training and tackle the noise drift challenge. DISQ adopts a "drift detector" with a reference circuit to identify and skip iterations that are severely affected by noise drift errors. Specifically, the circuits from the previous training iteration are re-executed as a reference circuit in the current iteration to estimate noise drift impacts. The iteration is deemed compromised by noise drift errors and thus skipped if noise drift flips the direction of the ideal optimization gradient. To enhance noise drift detection reliability, we further propose to leverage multiple reference circuits from previous iterations to provide a well founded judge of current noise drift. Nevertheless, multiple reference circuits also introduce considerable execution overhead. To mitigate extra overhead, we propose Pauli-term subsetting (prime and minor subsets) to execute only observable circuits with large coefficient magnitudes (prime subset) during drift detection. Only this minor subset is executed when the current iteration is drift-free. Evaluations across various applications and QPUs demonstrate that DISQ can mitigate a significant portion of the noise drift impact on VQAs and achieve 1.51-2.24x fidelity improvement over the traditional baseline. DISQ's benefit is 1.1-1.9x over the best alternative approach while boosting average noise detection speed by 2.07

Piercing Through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields: I. X-ray Spectral and Long-term Variability Analyses

We present a detailed X-ray spectral analysis of 1152 AGNs selected in the Chandra Deep Fields (CDFs), in order to identify highly obscured AGNs ($N_{\rm H} > 10^{23}\ \rm cm^{-2}$). By fitting spectra with physical models, 436 (38%) sources with $L_{\rm X} > 10^{42}\ \rm erg\ s^{-1}$ are confirmed to be highly obscured, including 102 Compton-thick (CT) candidates. We propose a new hardness-ratio measure of the obscuration level which can be used to select highly obscured AGN candidates. The completeness and accuracy of applying this method to our AGNs are 88% and 80%, respectively. The observed logN-logS relation favors cosmic X-ray background models that predict moderate (i.e., between optimistic and pessimistic) CT number counts. 19% (6/31) of our highly obscured AGNs that have optical classifications are labeled as broad-line AGNs, suggesting that, at least for part of the AGN population, the heavy X-ray obscuration is largely a line-of-sight effect, i.e., some high-column-density clouds on various scales (but not necessarily a dust-enshrouded torus) along our sightline may obscure the compact X-ray emitter. After correcting for several observational biases, we obtain the intrinsic NH distribution and its evolution. The CT-to-highly-obscured fraction is roughly 52% and is consistent with no evident redshift evolution. We also perform long-term (~17 years in the observed frame) variability analyses for 31 sources with the largest number of counts available. Among them, 17 sources show flux variabilities: 31% (5/17) are caused by the change of NH, 53% (9/17) are caused by the intrinsic luminosity variability, 6% (1/17) are driven by both effects, and 2 are not classified due to large spectral fitting errors.Comment: 32 pages, 21 figures, 9 tables, accepted for publication in Ap

Varstrometry for Off-nucleus and Dual sub-Kpc AGN (VODKA). SDSS J1608+2716: A Sub-arcsec Quadruply Lensed Quasar at z=2.575z=2.575

We report Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) deep IR (F160W) imaging of SDSS J1608+2716. This system, located at a redshift of $z=2.575$, was recently reported as a triple quasar candidate with subarcsecond separations ($\sim0.25''$) based on selection from Gaia astrometry and follow-up Keck adaptive optics-assisted integral field unit spectroscopy. Our new HST deep IR imaging reveals the presence of a fourth point-like component located $\sim0.9''$ away from the triple system. Additionally, we detect an edge-on disk galaxy located in between the four point sources, which appears to be in the process of merging with a fainter companion galaxy. The entire system exhibits a characteristic cusp structure in the context of strong gravitational lensing, and the observed image configuration can be successfully reproduced using a lens model based on a singular isothermal ellipsoid mass profile. These findings indicate that this system is a quadruply lensed quasar. Our results highlight the challenges associated with identifying dual/multiple quasars on $\sim$kpc scales at high redshifts, and emphasize the crucial role of deep, high-resolution IR imaging in robustly confirming such systems.Comment: 9 pages, 3 figures, submitted to ApJ

Methanol extract of Inonotus obliquus improves type 2 diabetes mellitus through modifying intestinal flora

Type 2 diabetes mellitus (T2DM) poses a significant risk to human health. Previous research demonstrated that Inonotus obliquus possesses good hypolipidemic, anti-inflammatory, and anti-tumor properties. In this research, we aim to investigate the potential treatment outcomes of Inonotus obliquus for T2DM and discuss its favourable influences on the intestinal flora. The chemical composition of Inonotus obliquus methanol extracts (IO) was analyzed by ultra-high-performance liquid chromatography-Q extractive-mass spectrometry. IO significantly improved the blood glucose level, blood lipid level, and inflammatory factor level in T2DM mice, and effectively alleviated the morphological changes of colon, liver and renal. Acetic acid, propionic acid, and butyric acid levels in the feces of the IO group were restored. 16S rRNA gene sequencing revealed that the intestinal flora composition of mice in the IO group was significantly modulated. Inonotus obliquus showed significant hypoglycemic and hypolipidemic effects with evident anti-inflammatory activity and improved the morphological structure of various organs and cells. Inonotus obliquus increased the levels of short-chain fatty acids in the environment by increasing the population of certain bacteria that produce acid, such as Alistipes and Akkermansia, which are beneficial to improve intestinal flora disorders and maintain intestinal flora homeostasis. Meanwhile, Inonotus obliquus further alleviated T2DM symptoms in db/db mice by down-regulating the high number of microorganisms that are dangerous, such as Proteobacteria and Rikenellaceae_RC9_gut_group and up-regulating the abundance of beneficial bacteria such as Odoribacter and Rikenella. Therefore, this study provides a new perspective for the treatment of T2DM by demonstrating that drug and food homologous active substances could relieve inflammation via regulating intestinal flora