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

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

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

Active Galactic Nuclei and Host Galaxies in COSMOS-Web. II. First Look at the Kpc-scale Dual and Offset AGN Population

Kpc-scale dual and offset Active Galactic Nuclei (AGNs) are signposts of accreting supermassive black holes (SMBHs) triggered during late-stage galaxy mergers, offering crucial insights into the coevolution of SMBHs and galaxies. However, robustly confirmed systems at high redshift (e.g., $z>1$) are scarce and biased towards the most luminous and unobscured systems. In this study, we systematically search for kpc-scale (projected separation $<15$ kpc) dual and offset AGNs around 571 moderate-luminosity, X-ray-selected AGNs including the obscured population, utilizing deep HST ACS/F814W and multiband JWST NIRCam imaging from the COSMOS-Web survey. We identify 59 dual and 30 offset AGN candidates in late stage major mergers based on spatially-resolved spectral energy distribution analyses. This translates to $\sim28$ and $\sim10$ bona-fide dual and offset AGNs using a probabilistic pair counting scheme to minimize chance superpositions. Notably, the fraction of dual and offset AGNs among moderate-luminosity ($L_{\rm bol}\sim10^{43}-10^{46}\ \rm erg\ s^{-1}$), obscured AGNs is nearly two orders of magnitude higher than that of the most luminous, unobscured quasar pairs. We find tentative evidence for an increasing pair fraction among AGNs with redshift (from a few percent at $z\sim 0.5$ to $\sim22.9_{-17.7}^{+27.5}\%$ at $z\sim4.5$) and a higher occurrence rate of dual over offset AGNs. There is no pileup of dual/offset AGNs below $\sim 2~{\rm kpc}$ separations. These results generally align with predictions from the ASTRID and Horizon-AGN cosmological simulations when matching sample selection criteria, implying a high probability of both BHs being active simultaneously in late-stage major mergers.Comment: 20 pages, 8 figures, submitted to Ap

StyleShot: A Snapshot on Any Style

In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning. We achieve this through constructing a style-aware encoder and a well-organized style dataset called StyleGallery. With dedicated design for style learning, this style-aware encoder is trained to extract expressive style representation with decoupling training strategy, and StyleGallery enables the generalization ability. We further employ a content-fusion encoder to enhance image-driven style transfer. We highlight that, our approach, named StyleShot, is simple yet effective in mimicking various desired styles, i.e., 3D, flat, abstract or even fine-grained styles, without test-time tuning. Rigorous experiments validate that, StyleShot achieves superior performance across a wide range of styles compared to existing state-of-the-art methods. The project page is available at: https://styleshot.github.io/.Comment: project page:https://styleshot.github.io

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