Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of encoder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below $10^{-2}$.
Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation

As the globalization of semiconductor design and manufacturing processes continues, the demand for defect detection during integrated circuit fabrication stages is becoming increasingly critical, playing a significant role in enhancing the yield of semiconductor products. Traditional wafer map defect pattern detection methods involve manual inspection using electron microscopes to collect sample images, which are then assessed by experts for defects. This approach is labor-intensive and inefficient. Consequently, there is a pressing need to develop a model capable of automatically detecting defects as an alternative to manual operations. In this paper, we propose a method that initially employs a pre-trained VAE model to obtain the fault distribution information of the wafer map. This information serves as guidance, combined with the original image set for semi-supervised model training. During the semi-supervised training, we utilize a teacher-student network for iterative learning. The model presented in this paper is validated on the benchmark dataset WM-811K wafer dataset. The experimental results demonstrate superior classification accuracy and detection performance compared to state-of-the-art models, fulfilling the requirements for industrial applications. Compared to the original architecture, we have achieved significant performance improvement.
A Cosheaf Theory of Reciprocal Figures: Planar and Higher Genus Graphic Statics

This paper introduces cellular sheaf theory to graphical methods and reciprocal constructions in structural engineering. The elementary mechanics and statics of trusses are derived from the linear algebra of sheaves and cosheaves. Further, the homological algebra of these mathematical constructions cleanly and concisely describes the formation of 2D reciprocal diagrams and 3D polyhedral lifts. Additional relationships between geometric quantities of these dual diagrams are developed, including systems of impossible edge rotations. These constructions generalize to non-planar graphs. When a truss embedded in a torus or higher genus surface has a sufficient degree of axial self stress, we show non-trivial reciprocal figures and non-simply connected polyhedral lifts are guaranteed to exist.
Fast Adaptive Optics for High-Dimensional Quantum Communications in Turbulent Channels

Quantum Key Distribution (QKD) promises a provably secure method to transmit information from one party to another. Free-space QKD allows for this information to be sent over great distances and in places where fibre-based communications cannot be implemented, such as ground-satellite. The primary limiting factor for free-space links is the effect of atmospheric turbulence, which can result in significant error rates and increased losses in QKD channels. Here, we employ the use of a high-speed Adaptive Optics (AO) system to make real-time corrections to the wavefront distortions on spatial modes that are used for high-dimensional QKD in our turbulent channel. First, we demonstrate the effectiveness of the AO system in improving the coupling efficiency of a Gaussian mode that has propagated through turbulence. Through process tomography, we show that our system is capable of significantly reducing the crosstalk of spatial modes in the channel. Finally, we show that employing AO reduces the quantum dit error rate for a high-dimensional orbital angular momentum-based QKD protocol, allowing for secure communication in a channel where it would otherwise be impossible. These results are promising for establishing long-distance free-space QKD systems.
MAGNETOSTRATIGRAPHY OF A LOESS-SOIL SEQUENCE IN THE GARZE AREA,WESTERN SICHUAN

The western Sichuan Plateau is located on the southeastern margin of the Qinghai-Tibet Plateau,and its climate is mainly controlled by the Indian monsoon and Plateau monsoon.The loess-paleosol sequence distributed widely on the western Sichuan Plateau is among the best continuous records of the Quaternary climate and environment on the margins of the plateau.It records the evolutionary process of the above-mentioned circulation system and its relation to the phased evolution of the Qinghai-Tibet Plateau and is crucial for understanding the regional environmental response to the uplift of the plateau.A systematic magnetostratigraphic study was conducted on the Garze-A section in the Garze area,western Sichuan.The results indicate that the B/M boundary appears at the top of L_7 and that the Jaramillo normal polarity subzone of the Matuyama negative polarity zone appears in the lower part of the section.Extrapolation from the paleomagnetic boundary point yields a basal age of ～1.16 Ma for typical eolian deposits of the Garze-A section.
Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos

The compact cameras recording high-speed scenes with high resolution are highly demanded, but the required high bandwidth often leads to bulky, heavy systems, which limits their applications on low-capacity platforms. Adopting a coded exposure setup to encode a frame sequence into a blurry snapshot and retrieve the latent sharp video afterward can serve as a lightweight solution. However, restoring motion from blur is quite challenging due to the high ill-posedness of motion blur decomposition, intrinsic ambiguity in motion direction, and diverse motions in natural videos. In this work, by leveraging classical coded exposure imaging technique and emerging implicit neural representation for videos, we tactfully embed the motion direction cues into the blurry image during the imaging process and develop a novel self-recursive neural network to sequentially retrieve the latent video sequence from the blurry image utilizing the embedded motion direction cues. To validate the effectiveness and efficiency of the proposed framework, we conduct extensive experiments on benchmark datasets and real-captured blurry images. The results demonstrate that our proposed framework significantly outperforms existing methods in quality and flexibility. The code for our work is available at https://github.com/zhihongz/BDINR
A Model-Free Approach to Fingertip Slip and Disturbance Detection for Grasp Stability Inference

Robotic capacities in object manipulation are incomparable to those of humans. Besides years of learning, humans rely heavily on the richness of information from physical interaction with the environment. In particular, tactile sensing is crucial in providing such rich feedback. Despite its potential contributions to robotic manipulation, tactile sensing is less exploited; mainly due to the complexity of the time series provided by tactile sensors. In this work, we propose a method for assessing grasp stability using tactile sensing. More specifically, we propose a methodology to extract task-relevant features and design efficient classifiers to detect object slippage with respect to individual fingertips. We compare two classification models: support vector machine and logistic regression. We use highly sensitive Uskin tactile sensors mounted on an Allegro hand to test and validate our method. Our results demonstrate that the proposed method is effective in slippage detection in an online fashion.
Terminal Phase Navigation for AUV Docking: An Innovative Electromagnetic Approach

This study introduces a groundbreaking approach for real-time 3D localization, specifically focusing on achieving seamless and precise localization during the terminal guidance phase of an autonomous underwater vehicle (AUV) as it approaches an omnidirectional docking component in an automated deployable launch and recovery system (LARS). Using the AUV’s magnetometer, an economical electromagnetic beacon embedded in the docking component, and an advanced signal processing algorithm, this novel approach ensures the accurate localization of the docking component in three dimensions without the need for direct line-of-sight contact. The method’s real-time capabilities were rigorously evaluated via simulations, prototype experiments in a controlled lab setting, and extensive full-scale pool experiments. These assessments consistently demonstrated an exceptional average positioning accuracy of under 3 cm, marking a significant advancement in AUV guidance systems.
Speak Like a Native: Prompting Large Language Models in a Native Style

In-context learning (ICL) with large language models (LLMs) has become the modern tool of choice for many natural language processing tasks. However, how the text style of in-context examples influences the performance of LLMs still remains under-explored. This paper presents a novel and effective approach, named \textbf{AlignedCoT}, to improve the reasoning capability of LLMs by aligning the in-context examples with the native style of LLMs. ``Native'' refers to the inherent characteristic of LLMs which can be probed by zero-shot scenarios. We conduct extensive and comprehensive experiments on several benchmarks on mathematical question-answering and common-sense reasoning. The empirical results demonstrate that our AlignedCoT significantly improves performance over the carefully handcrafted demonstrations. Specifically, with AlignedCoT, we observe an average +3.2\% improvement for \texttt{gpt-3.5-turbo} compared to the carefully handcrafted CoT on multi-step reasoning benchmarks. Furthermore, we use AlignedCoT to rewrite the CoT text style in the training set, which improves the performance of Retrieval Augmented Generation by 3.6\%. Our source code and dataset are available at https://github.com/yangzhch6/AlignedCoT.
The photospheres of the hottest fastest stars in the Galaxy

We perform nonlocal thermodynamic equilibrium (NLTE) model atmosphere analyses of the three hottest hypervelocity stars (space velocities between ≈1500–2800 km s−1) known to date, which were recently discovered spectroscopically and identified as runaways from Type Ia supernovae. The hottest of the three (J0546+0836, effective temperature Teff = 95 000 ± 15 000 K, surface gravity log 𝑔 = 5.5 ± 0.5) has an oxygen-dominated atmosphere with a significant amount of carbon (C = 0.10 ± 0.05, O = 0.90 ± 0.05, mass fractions). Its mixed absorption+emission line spectrum exhibits photospheric absorption lines from O v and O vi as well as O iii and O iv emission lines that are formed in a radiation-driven wind with a mass-loss rate of the order of Ṁ= 10−8 M⊙ yr−1. Spectroscopically, J0546+0836 is a [WC]–PG1159 transition-type pre-white dwarf. The second object (J0927–6335) is a PG1159-type white dwarf with a pure absorption-line spectrum dominated by C III/C IV and O III/O IV. We find Teff = 60 000 ± 5000 K, log 𝑔 = 7.0 ± 0.5, and a carbon-and oxygen-dominated atmosphere with C = 0.47 ± 0.25, O = 0.48 ± 0.25, and possibly a minute amount of helium (He = 0.05 ± 0.05). Comparison with post-AGB evolutionary tracks suggests a mass of M ≈ 0.5 M⊙ for both objects, if such tracks can safely be applied to these stars. We find the third object (J1332−3541) to be a relatively massive (M = 0.89 M⊙) hydrogen-rich (DAO) white dwarf with Teff = 65 657 ± 2390 K, log 𝑔 = 8.38 ± 0.08, and abundances H = 0.65 ± 0.04 and He = 0.35 ± 0.04. We discuss our results in the context of the “dynamically driven double-degenerate double-detonation” (D6) scenario proposed for the origin of these stars.
Impact of moment-based, energy integrated neutrino transport on microphysics and ejecta in binary neutron star mergers

We present an extensive study of the effects of neutrino transport in 3-dimensional general relativistic radiation hydrodynamics (GRHD) simulations of binary neutron star (BNS) mergers using our moment-based, energy-integrated neutrino radiation transport (M1) scheme. We consider a total of 8 BNS configurations, while varying equation of state models, mass ratios and grid resolutions, for a total of 16 simulations. We find that M1 neutrino transport is crucial in modeling the local absorption of neutrinos and the deposition of lepton number throughout the medium. We provide an in-depth look at the effects of neutrinos on the fluid dynamics and luminosity during the late inspiral and post-merger phases, the properties of ejecta and outflow, and the post-merger nucleosynthesis. The simulations presented in this work comprise an extensive study of the combined effect of the equation of state and M1 neutrino transport in GRHD simulations of BNS mergers, and establish that the solution provided by our M1 scheme is robust across system properties and provides insight into the effects of neutrino trapping in BNS mergers.
Belief identification by proxy

It is well known that individual beliefs cannot be identified using traditional choice data, unless we exogenously assume state-independent utilities. In this paper, I propose a novel methodology that solves this long-standing identification problem in a simple way. This method relies on the extending the state space by introducing a proxy, for which the agent has no stakes conditional on the original state space. The latter allows us to identify the agent's conditional beliefs about the proxy given each state realization, which in turn suffices for indirectly identifying her beliefs about the original state space. This approach is analogous to the one of instrumental variables in econometrics. Similarly to instrumental variables, the appeal of this method comes from the flexibility in selecting a proxy.
Modular Blended Attention Network for Video Question Answering

In multimodal machine learning tasks, it is due to the complexity of the assignments that the network structure, in most cases, is assembled in a sophisticated way. The holistic architecture can be separated into several logical parts according to the respective ends that the modules are devised to achieve. As the number of modalities of information representation increases, constructing ad hoc subnetworks for processing the data from divergent modalities while mediating the fusion of different information types has become a cumbersome and expensive problem. In this paper, we present an approach to facilitate the question with a reusable and composable neural unit; by connecting the units in series or parallel, the arduous network constructing of multimodal machine learning tasks will be accomplished in a much straightforward way. Additionally, through parameter sharing (weights replication) among the units, the space complexity will be significantly reduced. We have conducted experiments on three commonly used datasets; our method achieves impressive performance compared to several video QA baselines.
Beet taproot plasma membrane sugar transport revisited

Sugar beet (Beta vulgaris) is the major sugar-producing crop in Europe and Northern America, as the taproot stores sucrose at a concentration of around 20%. Genome sequence analysis together with biochemical and electrophysiological approaches led to the identification and characterization of the TST sucrose transporter driving vacuolar sugar accumulation in the taproot. However, the sugar transporters mediating sucrose uptake across the plasma membrane of taproot parenchyma cells remained unknown. As with glucose, sucrose stimulation of taproot parenchyma cells caused inward proton fluxes and plasma membrane depolarization, indicating a sugar/proton symport mechanism. To decipher the nature of the corresponding proton-driven sugar transporters, we performed transcriptomic taproot profiling and identified the cold-induced PMT5a and STP13 transporters. When expressed in Xenopus laevis oocytes, BvPMT5a was characterized as a voltage- and H+-driven low-affinity glucose transporter, which does not transport sucrose. In contrast, BvSTP13 operated as a high-affinity H+/sugar symporter, transporting glucose better than sucrose, and being more cold-tolerant than BvPMT5a. Modeling of the BvSTP13 structure with bound mono- and disaccharides suggests plasticity of the binding cleft to accommodate the different saccharides. The identification of BvPMT5a and BvSTP13 as taproot sugar transporters could improve breeding of sugar beet to provide a sustainable energy crop. Significance Statement In vivo electrophysiological studies with sugar beet taproots provide clear evidence for proton-coupled glucose and sucrose uptake into taproot parenchyma cells. In search for the molecular entities, the taproot-expressed BvPMT5a and BvSTP13 carriers were studied in detail, because they mediate proton-driven import of glucose and sucrose and thus provide proper candidates for sugar beet plasma membrane sugar-proton cotransporters.
SmCCNet 2.0: an Upgraded R package for Multi-omics Network Inference

Summary Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multiomics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. Availability This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available in https://smccnet.shinyapps. io/smccnetnetwork/.
Protein Stability Prediction by Fine-tuning a Protein Language Model on a Mega-scale Dataset

The stability of a protein is crucial to its utility in industrial applications. While engineering campaigns can now be routinely used to enhance protein thermal stability to the level needed in an industrial setting, there is a significant desire to fast-track these efforts through predictive tools allowing one to jump in a minimal number of design iterations to a highly stabilized protein. In this work, we explore utilizing a mega-scale dataset for development of a protein language model tuned for stability. This model is trained on the folding stability of 528k sequences derived from 461 small protein domains and designs, and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. The fine-tuned protein language model, named ESMtherm, performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss its limitations when compared to other state-of-the-art methods in generalizing to larger protein scaffolds and highlight the need of large-scale stability measurement on a diverse dataset that represents the distribution of sequence lengths commonly observed in nature.
An integrated transcriptomic cell atlas of human endoderm-derived organoids

Human stem cells can generate complex, multicellular epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. These tissues, also called organoids, can be derived from pluripotent stem cells or tissue-resident fetal and adult stem cells. However, it has remained difficult to understand the precision and accuracy of organoid cell states through comparison with primary counterparts, and to comprehensively assess the similarity and differences between organoid protocols. Advances in computational single-cell biology now allow the integration of datasets with high technical variability. Here, we integrate single-cell transcriptomes from 218 samples covering organoids of diverse endoderm-derived tissues including lung, pancreas, intestine, liver, biliary system, stomach, and prostate to establish an initial version of a human endoderm organoid cell atlas (HEOCA). The integration includes nearly one million cells across diverse conditions, data sources and protocols. We align and compare cell types and states between organoid models, and harmonize cell type annotations by mapping the atlas to primary tissue counterparts. To demonstrate utility of the atlas, we focus on intestine and lung, and clarify ontogenic cell states that can be modeled in vitro. We further provide examples of mapping novel data from new organoid protocols to expand the atlas, and showcase how integrating organoid models of disease into the HEOCA identifies altered cell proportions and states between healthy and disease conditions. The atlas makes diverse datasets centrally available, and will be valuable to assess organoid fidelity, characterize perturbed and diseased states, and streamline protocol development.