Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranging and positioning experiments for aircraft using Engineering Test Satellite V

In an aeronautical satellite system for air traffic control in an oceanic area, it is important to know the position of aircraft at the ground earth station. This paper describes a ranging and positioning system using two geostationary satellites, one real and one pseudo on the ground, and gives the results of experiments. This system is designed to allow ranging and positioning of each aircraft every 0.6 s with a ranging and positioning accuracy of 100 m and 1 km, respectively. Experiments to estimate this system were performed using two aircraft: a Boeing 747 on the north Pacific route with a satellite evelation angle (EL) of about 20°, and a Beechcraft B99 in the domestic area of Japan with an EL of about 47°. The random error of the range was about 50 m in the experiment on the north Pacific route. The difference between the position of the B99 obtained with our system and that obtained with the global positioning system (GPS) was about 500 m in the experiment in the domestic area of Japan.
Can CNNs Accurately Classify Human Emotions? A Deep-Learning Facial Expression Recognition Study

Emotional Artificial Intelligences are currently one of the most anticipated developments of AI. If successful, these AIs will be classified as one of the most complex, intelligent nonhuman entities as they will possess sentience, the primary factor that distinguishes living humans and mechanical machines. For AIs to be classified as"emotional,"they should be able to empathize with others and classify their emotions because without such abilities they cannot normally interact with humans. This study investigates the CNN model's ability to recognize and classify human facial expressions (positive, neutral, negative). The CNN model made for this study is programmed in Python and trained with preprocessed data from the Chicago Face Database. The model is intentionally designed with less complexity to further investigate its ability. We hypothesized that the model will perform better than chance (33.3%) in classifying each emotion class of input data. The model accuracy was tested with novel images. Accuracy was summarized in a percentage report, comparative plot, and confusion matrix. Results of this study supported the hypothesis as the model had 75% accuracy over 10,000 images (data), highlighting the possibility of AIs that accurately analyze human emotions and the prospect of viable Emotional AIs.
The free boundary of steady axisymmetric inviscid flow with vorticity II: near the non-degenerate point

This is the sequel of the recent work (Du, Huang, Pu, Commun. Math. Phys, 2023, doi: 10.1007/s00220-023-04651-7) on axially symmetric gravity water waves with general vorticities, which has investigated the singular wave profile of the free boundary near the degenerate points. In this companion paper, we are interested in the regularity of the free surface of the water wave near the non-degenerate point. Precisely, we showed that the free boundary is $C^{1,\gamma}$ smooth for some $\gamma\in(0,1)$ near all non-degenerate points. The problem is intrinsically intertwined with the regularity theory of the semilinear Bernoulli-type free boundary problem. Our approach is closely related to the monotonicity formula developed by Weiss in his celebrated work (Weiss, J. Geom. Anal. 9: 317-326, 1999), and to a partial boundary Harnack inequality for the one-phase free boundary problem, which is dedicated to De Silva (De Silva, Interfaces Free Bound. 13, 223-238, 2011). Mathematically, we associate the existence of viscosity solutions with the Weiss boundary-adjusted energy. Compared to the classical approach of Caffarelli (Caffarelli, Ann. Scuola Norm. Sup. Pisa, 15, 583-602, 1988), we provide an alternative proof of the existence of viscosity solutions for a large class of semilinear free boundary problems.
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues. In contrast, error-backpropagation has demonstrated its effectiveness for training deep networks in practice. In this study, we propose a local objective for neurons that, when pursued by neurons individually, align them to exhibit similarities to error-backpropagation in terms of efficiency and scalability during training. For this purpose, we examine a neural network comprising decentralized, self-interested neurons seeking to maximize their local objective -- attention from subsequent layer neurons -- and identify the optimal strategy for neurons. We also analyze the relationship between this strategy and backpropagation, establishing conditions under which the derived strategy is equivalent to error-backpropagation. Lastly, we demonstrate the learning capacity of these multi-agent neural networks through experiments on three datasets and showcase their superior performance relative to error-backpropagation in a catastrophic forgetting benchmark.
VizAbility: Multimodal Accessible Data Visualization with Keyboard Navigation and Conversational Interaction

Data visualization serves as a crucial tool for communicating important information in our society. Yet, as visualizations grow more complex, they become less accessible to individuals with visual impairments. Traditional accessibility approaches like alternative text and data tables often fall short of capturing the full potential of data visualization. To bridge this gap, we introduce VizAbility, a novel multimodal accessible system that combines keyboard navigation with conventional interaction, enabling individuals with visual impairments to actively engage with and explore data visualizations. We built an LLM-based pipeline that classifies user queries and synthesizes underlying data, chart structure, user locality, and web-based information to answer the queries. Our preliminary evaluation using real-world questions from blind individuals demonstrates the significant potential of VizAbility.
Let's reward step by step: Step-Level reward model as the Navigators for Reasoning

Recent years have seen considerable advancements in multi-step reasoning with Large Language Models (LLMs). The previous studies have elucidated the merits of integrating feedback or search mechanisms during model inference to improve the reasoning accuracy. The Process-Supervised Reward Model (PRM), typically furnishes LLMs with step-by-step feedback during the training phase, akin to Proximal Policy Optimization (PPO) or reject sampling. Our objective is to examine the efficacy of PRM in the inference phase to help discern the optimal solution paths for multi-step tasks such as mathematical reasoning and code generation. To this end, we propose a heuristic greedy search algorithm that employs the step-level feedback from PRM to optimize the reasoning pathways explored by LLMs. This tailored PRM demonstrated enhanced results compared to the Chain of Thought (CoT) on mathematical benchmarks like GSM8K and MATH. Additionally, to explore the versatility of our approach, we develop a novel method to automatically generate step-level reward dataset for coding tasks and observed similar improved performance in the code generation tasks. Thus highlighting the robust nature of our reward-model-based approach to inference for reasoning tasks.
Land surface thermal environment during heat wave event measured by satellite observation

In summer 2013, mainly from July to August, most parts of China continued to experience an unusually severe heat wave with exceptionally high air temperatures, based on the records measured at meteorological stations. As a supplement to the weather station networks, remotely sensed observation can quantify detailed variation of surface temperature at relatively high spatial resolution, owing to its ability to provide a complete and homogeneous data sources. In addition to the GHCN CAMS gridded land air surface temperature, land surface temperature products of MODIS including MOD11C3/MYD11C3 and MOD11A2/MYD11A2 were used to evaluate the anomaly of summertime thermal environment over the South China in 2013. To investigate the impacts of heat wave event on built environment, the MODIS Land Cover Type yearly product (MCD12Q1) was collected. Regional thermal anomaly was observed in both air and surface temperature measurements, especially for August. Statistics based on MOD11A2/MYD11A2 shows the spatio-temporal variation of land surface temperature at regional scale, and the heterogeneous characteristics in diurnal cycle are also shown. Compared with other types, the urban and built-up generally presents larger surface temperature at daytime. Detailed analyses were further conducted for three selected regions roughly covering the Yangtze River Delta, the Pearl River Delta, and the areas around Wuhan City respectively. Findings indicate that urban and built-up exhibits more distinct thermal contrast to its surroundings at daytime, in contrast to the situation at nighttime. This thermal contrast was defined as surface urban heat island intensity (UHII) calculated using a newly proposed procedure, in this paper. The UHII shows both time- and geography-dependent variations. Meanwhile, the UHII over medium and small cities was even more obvious and larger than that over megalopolitan areas. These preliminary findings suggest that land use and land cover changes as a consequence of rapid urbanization possibly gives positive feedback to warming anomaly during heat wave event. The exacerbated warming of built-up environment, not only over megalopolitan areas but also over medium and small cities, deserves our attention in urban management.
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring

Automatic Speech Recognition (ASR) has witnessed a profound research interest. Recent breakthroughs have given ASR systems different prospects such as faithfully transcribing spoken language, which is a pivotal advancement in building conversational agents. However, there is still an imminent challenge of accurately discerning context-dependent words and phrases. In this work, we propose a novel approach for enhancing contextual recognition within ASR systems via semantic lattice processing leveraging the power of deep learning models in accurately delivering spot-on transcriptions across a wide variety of vocabularies and speaking styles. Our solution consists of using Hidden Markov Models and Gaussian Mixture Models (HMM-GMM) along with Deep Neural Networks (DNN) models integrating both language and acoustic modeling for better accuracy. We infused our network with the use of a transformer-based model to properly rescore the word lattice achieving remarkable capabilities with a palpable reduction in Word Error Rate (WER). We demonstrate the effectiveness of our proposed framework on the LibriSpeech dataset with empirical analyses.
The Zones of the Blackdown Beds, and their Correlation with those at Haldon, with a List of the Fossils

Living, as I do, almost at the foot of the Blackdown Hills, it has naturally been my aim and ambition to add something to our knowlenge of their much discussed and highly fossiliferous strata. For some time I almost despaired of achieving any thing. The absence of good sections, the great difficulty (not to say, very often, the impossibility) of seeing the fossils in situ, the fact that all the pits but a very few are now closed, and the confused and unsatisfactory way in which the fossils have often been collected in past times, make the subject at the present time a very tangled skein indeed. Collectors of Blackdown fossils have almost without exception, and perhaps unavoidably so, bought their specimens from quarrymen. And this means, generally speaking, that a quarryman who has “happened on some shells,” has brought them home, and put them into a drawer or bucket or some such receptacle. Another find from another zone is the next day thrown in with them, and so on, until a sufficient quantity has accumulated, when the whole are thrown promiscuously into a hamper and sent to the address of the buyer.
Ultra-Diffuse Galaxies $-$ A Distinct Population? Dwarf Galaxies in the Coma Cluster and A262 from Deep $u'-g'-r'$ Wendelstein Imaging Data

In this study, we compare the structural parameters of ultra-diffuse galaxies (UDGs) to those of other dwarf galaxies and investigate whether UDGs form a distinct population. We observed deep $u'$-, $g'$-, and $r'$-band images (maximum limiting surface brightness [3$\sigma$, $10"\times10"$] $u'$ and $g'$: $\mathrm{\approx 30\,mag\,arcsec^{-2}}$; $r'$: $\mathrm{\approx 29\,mag\,arcsec^{-2}}$) of A1656 (Coma cluster) and A262 with the Wendelstein Wide Field Imager at the 2.1 m-Fraunhofer Wendelstein Telescope on the Wendelstein Observatory. We measure $u'-g'$ and $g'-r'$ colors and structural parameters using parametric fitting of tens of thousands of potential UDGs and other dwarf galaxies. Cluster members are identified and separated from diffuse background galaxies based on red sequence membership and location in the $u'-g'$ vs. $g'-r'$ color-color diagram. We find 11 UDGs in A262 and 48 UDGs in A1656. The latter is six times more than van Dokkum et al. found in the overlapping region. By comparing the structural parameters of UDGs to non-UDGs in our sample and to spheroidals from the literature, we do not find any separation in all tested parameter spaces. Instead, UDGs form the diffuse end of the already well-known spheroidal population and slightly extend it. Furthermore, we find that the UDG definition used by Koda et al. and Yagi et al. mainly extends the definition by van Dokkum et al. toward ordinary spheroidals.
Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network

This paper investigates the zero-shot object goal visual navigation problem. In the object goal visual navigation task, the agent needs to locate navigation targets from its egocentric visual input. "Zero-shot"means that the target the agent needs to find is not trained during the training phase. To address the issue of coupling navigation ability with target features during training, we propose the Class-Independent Relationship Network (CIRN). This method combines target detection information with the relative semantic similarity between the target and the navigation target, and constructs a brand new state representation based on similarity ranking, this state representation does not include target feature or environment feature, effectively decoupling the agent's navigation ability from target features. And a Graph Convolutional Network (GCN) is employed to learn the relationships between different objects based on their similarities. During testing, our approach demonstrates strong generalization capabilities, including zero-shot navigation tasks with different targets and environments. Through extensive experiments in the AI2-THOR virtual environment, our method outperforms the current state-of-the-art approaches in the zero-shot object goal visual navigation task. Furthermore, we conducted experiments in more challenging cross-target and cross-scene settings, which further validate the robustness and generalization ability of our method. Our code is available at: https://github.com/SmartAndCleverRobot/ICRA-CIRN.
ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion

Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured from different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited. Central to our method is a multi-view consistency block which enables information exchange across multiple single-view diffusion processes based on the underlying multi-view geometry principles. ConsistNet is an extension to the standard latent diffusion model, and consists of two sub-modules: (a) a view aggregation module that unprojects multi-view features into global 3D volumes and infer consistency, and (b) a ray aggregation module that samples and aggregate 3D consistent features back to each view to enforce consistency. Our approach departs from previous methods in multi-view image generation, in that it can be easily dropped-in pre-trained LDMs without requiring explicit pixel correspondences or depth prediction. Experiments show that our method effectively learns 3D consistency over a frozen Zero123 backbone and can generate 16 surrounding views of the object within 40 seconds on a single A100 GPU. Our code will be made available on https://github.com/JiayuYANG/ConsistNet
Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis

Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public. In this study, we aim to assess the performance of OpenAI's newest model, GPT-4V(ision), specifically in the realm of multimodal medical diagnosis. Our evaluation encompasses 17 human body systems, including Central Nervous System, Head and Neck, Cardiac, Chest, Hematology, Hepatobiliary, Gastrointestinal, Urogenital, Gynecology, Obstetrics, Breast, Musculoskeletal, Spine, Vascular, Oncology, Trauma, Pediatrics, with images taken from 8 modalities used in daily clinic routine, e.g., X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Digital Subtraction Angiography (DSA), Mammography, Ultrasound, and Pathology. We probe the GPT-4V's ability on multiple clinical tasks with or without patent history provided, including imaging modality and anatomy recognition, disease diagnosis, report generation, disease localisation. Our observation shows that, while GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy, it faces significant challenges in disease diagnosis and generating comprehensive reports. These findings underscore that while large multimodal models have made significant advancements in computer vision and natural language processing, it remains far from being used to effectively support real-world medical applications and clinical decision-making. All images used in this report can be found in https://github.com/chaoyi-wu/GPT-4V_Medical_Evaluation.
Soft Demodulator for Symbol-Level Precoding in Coded Multiuser MISO Systems

In this paper, we consider symbol-level precoding (SLP) in channel-coded multiuser multi-input single-output (MISO) systems. It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems. It, therefore, calls for novel soft demodulator designs for non-Gaussian distributed SLP signals with accurate log-likelihood ratio (LLR) calculation. To this end, we first investigate the non-Gaussian characteristics of both phase-shift keying (PSK) and quadrature amplitude modulation (QAM) received signals with existing SLP schemes and categorize the signals into two distinct types. The first type exhibits an approximate-Gaussian distribution with the outliers extending along the constructive interference region (CIR). In contrast, the second type follows some distribution that significantly deviates from the Gaussian distribution. To obtain accurate LLR, we propose the modified Gaussian soft demodulator and Gaussian mixture model (GMM) soft demodulators to deal with two types of signals respectively. Subsequently, to further reduce the computational complexity and pilot overhead, we put forward a novel neural soft demodulator, named pilot feature extraction network (PFEN), leveraging the transformer mechanism in deep learning. Simulation results show that the proposed soft demodulators dramatically improve the throughput of existing SLPs for both PSK and QAM transmission in coded systems.
Mass independent oxygen isotopic composition of atmospheric sulfate: Origin and implications for the present and past atmosphere of Earth and Mars

It is reported that atmospheric (aerosol and rainwater) sulfate possesses a mass independent isotopic composition, with excess 17O. Laboratory experiments indicate that the oxidants, H2O2 and O3, are the source of anomalous 17O enrichments in atmospheric sulfate via aqueous phase S(IV) oxidation. The discovery of the oxygen anomaly in atmospheric sulfate, with definition of its origin, provides a new way to investigate atmospheric and general planetary processes. The observations provide new insight into atmospheric chemistry with application in studies of the evolution of the atmosphere, and surface‐atmosphere interactions, both on Earth and Mars.
On the convergence of discrete dynamic unbalanced transport models

A generalized unbalanced optimal transport distance ${\rm WB}_{\Lambda}$ on matrix-valued measures $\mathcal{M}(\Omega,\mathbb{S}_+^n)$ was defined in [arXiv:2011.05845] \`{a} la Benamou-Brenier, which extends the Kantorovich-Bures and the Wasserstein-Fisher-Rao distances. In this work, we investigate the convergence properties of the discrete transport problems associated with ${\rm WB}_{\Lambda}$. We first present a convergence framework for abstract discretization. Then, we propose a specific discretization scheme that aligns with this framework, under the assumption that the initial and final distributions are absolutely continuous with respect to the Lebesgue measure. Moreover, thanks to the static formulation, we show that such an assumption can be removed for the Wasserstein-Fisher-Rao distance.
Sectorial covers over fanifolds

For the stopped Weinstein sector associated with any fanifold recently introduced by Gammage--Shende, we construct a Weinstein sectorial cover which allows us to describe homological mirror symmetry over the fanifold as an isomorphism of cosheaves of categories. In a special case, our Weinstein sectorial cover gives a lift of the open cover for the global skeleton of a very affine hypersurface computed in their previous work.
Airborne laser profiling of swell in an open ice field

Airborne laser profiles of swell entering the drift ice off the east coast of Newfoundland were made using a Geodolite profiler mounted in a DC-4 ice patrol aircraft. Concurrent infrared imagery along the track enabled the floe size distribution to be recorded. A linear decay of wave energy with ‘effective penetration’ (distance penetrated multiplied by fractional ice cover) was found, the decay rate increasing with wave frequency. A simple theoretical model based on progressive reflection from rows of floes gave good agreement with the observations.