Miracle-dz/UniFAR-FTUs huggingface dataset:
The NOAO Variable-Sky Project

Abstract Modern time-domain surveys have demonstrated that finding variable objects is relatively straightforward. The problem now is one of selecting and following up discoveries. With even larger-scale surveys on the horizon, the magnitude of the problem will inevitably increase. One way to prepare for the coming deluge is to have realistic estimates of the numbers of potential detections so that resources can be developed to meet that need. To that end, astronomers at the National Optical Astronomy Observatory (NOAO) have begun a project to characterize the variable sky in terms of type of objects, distribution on the sky and range of variation.
Multiuser Detection Based on Reduced Complexity

We consider a multiuser detector based on modelling the multiple-access interference (MAI) as a vector of Gaussian random variables. This approach leads to the probabilistic data association (PDA) multiuser detector, which outputs good ap- proximations to marginal posterior-mode optimal de- cisions. A simplification to the PDA detector is suggested, leading to a soft interference cancellation scheme. In the large system limit, the bit error rate performance of this detector can be accurately pre- dicted, and shown to be identical to the optimal de- tector.
Shape Description Using Arc-Height Radius Complex Function

A novel contour function,Arc-Height Radius Complex function(AHRC),is proposed for shape description.AHRC captures the global feature and local detail information of the shape through extracting the centroid distance and signed arc-height features from the shape contour.The Fourier coefficients of AHRC are utilized to construct the feature vector for shape description.The performance of the proposed method is evaluated on the standard MPEG-7 shape database and the leaf shape database which is captured from the real world.The experimental results of image retrieval show that the proposed method outperforms the other Fourier descriptors including centroid distance,triangle area,farther-point distance,angle radius and wavelet Fourier descriptors.
A method for calculating asphericity of high-order optical aspheric surface

In view of methods for calculating asphericity of optical aspheric surface are nonuniform, a least maximum residual error method for calculating asphericity of high-order optical aspheric surface is proposed on the basis of analyzing common calculating methods. Through finding a best fitting spherical surface, making the maximum deviation between aspheric surface and the best fitting spherical surface minimum, the maximum deviation at this time is just the maximum asphericity of aspheric surface. The method is suitable for computer programming and its calculation results are accurate. It fully unifies the asphericity calculation for conicoid and high-order aspheric surface.
An MSA-Net Algorithm for Direct Estimation of Gas Holdup Based on Electrical Impedance Tomography System

Electrical impedance tomography (EIT) is a nondestructive testing technique, which has great potential to be used for the detection of the cross-sectional gas-holdup ratio (CGR) of gas–liquid two-phase flow. Due to the nonlinear and ill-posed characteristics of the EIT image reconstruction, the accuracy of image-based CGR estimation methods is poor. To improve the detection accuracy, a learning-based direct CGR estimation method is proposed in this study, which applies a novel multiscale attention network (MSA-Net) to directly estimate the CGR from the voltage measurements. Multiscale feature extraction and residual structure are introduced into MSA-Net to fully extract the features, and attention unit (AU) is also used to capture high-frequency features. As a result, accurate and robust CGR estimation can be realized by MSA-Net. ResNet18 and single-scale attention network (SSA-Net) are selected for comparison. The simulation results indicate that the relative error (RE $_{\text {CG}}{)}$ of CGR estimated by the learning-based direct CGR estimation method is far lower than that of the image-based CGR estimation methods. Compared with ResNet18 and SSA-Net, the RECG of the CGR estimated by MSA-Net is lower, and the range is 0.07%–0.36%. Moreover, MSA-Net shows good noise robustness simultaneously. The experiment is also set to test MSA-Net. The range of RECG is 0.1%–1.12%, which further verifies the practicability of the proposed learning-based direct CGR estimation method.
Immunogenicity of COVID-eVax Delivered by Electroporation Is Moderately Impacted by Temperature and Molecular Isoforms

DNA integrity is a key issue in gene therapy and genetic vaccine approaches based on plasmid DNA. In contrast to messenger RNA that requires a controlled cold chain for efficacy, DNA molecules are considered to be more stable. In this study, we challenged this concept by characterizing the immunological response induced by a plasmid DNA vaccine delivered using electroporation. As a model, we used COVID-eVax, a plasmid DNA-based vaccine that targets the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Increased nicked DNA was produced by using either an accelerated stability protocol or a lyophilization protocol. Surprisingly, the immune response induced in vivo was only minimally affected by the percentage of open circular DNA. This result suggests that plasmid DNA vaccines, such as COVID-eVax that have recently completed a phase I clinical trial, retain their efficacy upon storage at higher temperatures, and this feature may facilitate their use in low-/middle-income countries.
Impact of geo-political risk on stocks, oil, and gold returns during GFC, COVID-19, and Russian – Ukraine War

Abstract The study uses wavelet power spectrum and wavelet coherence transformation methodologies to examine how geopolitical risk affected the returns on stocks, oil, and gold during the GFC, COVID-19, and Russia-Ukraine war-three disruptive events that affected the world’s financial markets. For better diversification benefits during the turbulent times, we further investigate the degree of co-movement in frequency and time domains. We observe that GPR has high variations during Russia-Ukraine war period compared to COVID-19 period and is shown to have least variation during the GFC period. WTI crude oil and DJGI indexes are observed to have high variations during GFC, and COVID-19 periods followed by Russia-Ukraine war. We further observe that GOLD offers better diversification opportunity as well as leading movement against WTI and DJGI during disruptive events in financial markets. The results provide new understanding of how geopolitical risk affects financial assets for international investors, fund managers, and regulators, which would further aid to find risky and safer haven possibilities during the turmoil periods.
The one-dimensional monoenergetic time-dependent transport equation in infinite media (revisited)

Abstract Classical integral transform methods are used to solve the monoenergetic time-dependent transport equation for the angular distribution and density in an infinite plane geometry with isotropic scattering for both a mono-directional and an isotropic, infinite, planar pulsed source (in other words, the initial-value Green's functions). In contrast to earlier work on the same problem, the methods presented here are relatively simple, and the results are—at least for the isotropic source—intuitively appealing, exact closed form solutions. For the isotropic, pulsed source problem, the expression for the density is shown to agree with both the closed form solution obtained by Ganapol and Grossman using the multiple collision method and the solution found by Case using the singular eigenfunction method. I also point out how exact closed form solutions for the density due to localized, isotropic pulsed sources in both spherical and cylindrical geometries can be obtained from the plane geometry result. Th.
Blind identification of linear degradation operators in the Fourier domain

Captured real images are often affected by environmental and technical interferences such as linear homogeneous distortion, nonuniform illumination, sensors’ noise, geometrical scene distortion, etc. Among these issues, the former is particularly interesting because various physical problems can be modeled by such degradations. In this work we propose a blind algorithm for identification of the linear distortion operator based on the analysis of zero-crossings and phase distribution of the distorted image spectrum. The algorithm is tested with common real linear distortion operators and its identification rate is discussed.
Pyromagnetic domain walls connecting antiferromagnetic non-ferroelastic magnetoelectric domains

Abstract We describe a group-theoretical procedure that enables one to find necessary conditions for the appearance of spontaneous magnetization in domain walls. We illustrate the derivation of wall symmetries on example of Cr2O3 and present a brief summary of a systematic analysis of domain walls in antiferromagnetic non-ferroelastic and magnetoelectric phases which shows that in more than 50% of possible domain walls a spontaneous magnetization may appear.
Eye-tracking in map use, map user and map usability research: what are we looking for?

ABSTRACT This overview paper summarises the ‘state of the art and of the science’ of eye-tracking, and its applications in map use research. Cartographic research is introduced, and its contemporary direction, which indicates that the main areas of such research are now focussed on human beings and their interaction with maps and geospatial displays, is stressed. A brief outline of several different methodologies for map use research is presented: observation, thinking loud, keyboard analysis, eye-tracking, and questionnaires. The role of eye-tracking as a major methodology for use, user, and usability investigation is explored; along with the possible choices for the researcher in the important areas of participant selection, eye-tracking equipment, set-up and use of the testing environment, and analysis of output data. Typical outcomes from eye tracking research are considered, with an assessment of its value in cartographic research in general. Future directions are suggested, along with the need for cartography to promote the valuable work done by researchers using eye-tracking for map use studies to the wider human-computer interaction community, expanding the scope of the geospatial-based stimuli in such experiments beyond maps, making use of the significant expertise and enthusiasm of cartographic researchers.
BanglaCoNER: Towards Robust Bangla Complex Named Entity Recognition

Named Entity Recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying named entities in text. But much work hasn't been done for complex named entity recognition in Bangla, despite being the seventh most spoken language globally. CNER is a more challenging task than traditional NER as it involves identifying and classifying complex and compound entities, which are not common in Bangla language. In this paper, we present the winning solution of Bangla Complex Named Entity Recognition Challenge - addressing the CNER task on BanglaCoNER dataset using two different approaches, namely Conditional Random Fields (CRF) and finetuning transformer based Deep Learning models such as BanglaBERT. The dataset consisted of 15300 sentences for training and 800 sentences for validation, in the . conll format. Exploratory Data Analysis (EDA) on the dataset revealed that the dataset had 7 different NER tags, with notable presence of English words, suggesting that the dataset is synthetic and likely a product of translation. We experimented with a variety of feature combinations including Part of Speech (POS) tags, word suffixes, Gazetteers, and cluster information from embeddings, while also finetuning the BanglaBERT (large) model for NER. We found that not all linguistic patterns are immediately apparent or even intuitive to humans, which is why Deep Learning based models has proved to be the more effective model in NLP, including CNER task. Our fine tuned BanglaBERT (large) model achieves an F1 Score of 0.79 on the validation set. Overall, our study highlights the importance of Bangla Complex Named Entity Recognition, particularly in the context of synthetic datasets. Our findings also demonstrate the efficacy of Deep Learning models such as BanglaBERT for NER in Bangla language.
Logarithmic divergence of the density of states at balanced hyperbolic critical energies

At a hyperbolic critical energy of a one-dimensional random discrete Schr¨odinger operator, by deﬁnition all transfer matrices commute and have their spectrum oﬀ the unit circle. If, moreover, the Lyapunov exponent vanishes, then the hyperbolic critical energy is called balanced. Such balanced hyperbolic critical energies exist in a random hopping Hamiltonian and at topological phase transitions of a generalized Su-Schrieﬀer-Heeger model. The main result shows that the density of states has a logarithmic divergence at these critical energies. The proof is based on renewal theory for the Pr¨ufer phase dynamics and the optional stopping theorem for suitably constructed comparison martingales.
Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention

Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS). However, lapses currently exist in the timely treatment of unsafe blood glucose (BG) levels, especially in the case of rebound hyperglycemia. We propose a machine learning (ML) method for predictive BG scenario categorization that outputs messages alerting the patient to upcoming BG trends to allow for earlier, educated treatment. In addition to standard notifications of predicted hypoglycemia and hyperglycemia, we introduce BG scenario-specific alert messages and the preliminary steps toward precise basal suggestions for the prevention of rebound hyperglycemia. Experimental evaluation on the DCLP3 clinical dataset achieves >98% accuracy and > 79% precision for predicting rebound high events for patient alerts.
MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation

Audio-Driven Face Animation is an eagerly anticipated technique for applications such as VR/AR, games, and movie making. With the rapid development of 3D engines, there is an increasing demand for driving 3D faces with audio. However, currently available 3D face animation datasets are either scale-limited or quality-unsatisfied, which hampers further developments of audio-driven 3D face animation. To address this challenge, we propose MMFace4D, a large-scale multi-modal 4D (3D sequence) face dataset consisting of 431 identities, 35,904 sequences, and 3.9 million frames. MMFace4D exhibits two compelling characteristics: 1) a remarkably diverse set of subjects and corpus, encompassing actors spanning ages 15 to 68, and recorded sentences with durations ranging from 0.7 to 11.4 seconds. 2) It features synchronized audio and 3D mesh sequences with high-resolution face details. To capture the subtle nuances of 3D facial expressions, we leverage three synchronized RGBD cameras during the recording process. Upon MMFace4D, we construct a non-autoregressive framework for audio-driven 3D face animation. Our framework considers the regional and composite natures of facial animations, and surpasses contemporary state-of-the-art approaches both qualitatively and quantitatively. The code, model, and dataset will be publicly available.
Single-view Neural Radiance Fields with Depth Teacher

Neural Radiance Fields (NeRF) have been proposed for photorealistic novel view rendering. However, it requires many different views of one scene for training. Moreover, it has poor generalizations to new scenes and requires retraining or fine-tuning on each scene. In this paper, we develop a new NeRF model for novel view synthesis using only a single image as input. We propose to combine the (coarse) planar rendering and the (fine) volume rendering to achieve higher rendering quality and better generalizations. We also design a depth teacher net that predicts dense pseudo depth maps to supervise the joint rendering mechanism and boost the learning of consistent 3D geometry. We evaluate our method on three challenging datasets. It outperforms state-of-the-art single-view NeRFs by achieving 5$\sim$20\% improvements in PSNR and reducing 20$\sim$50\% of the errors in the depth rendering. It also shows excellent generalization abilities to unseen data without the need to fine-tune on each new scene.
Microfluidic Filling and Spectroscopy of Colloidal CdSe/CdS Nanoplatelets in Liquid Core Fibers

Colloidal 2D semiconductor nanoplatelets are highly efficient light emitters, which exhibit large absorption and emission cross sections, and constitute promising laser gain media. However, if dispersed in solutions, such nanoplatelets lack a suitable optical platform for scalable and application-oriented integration into optical setups such as lasers. Here, we demonstrate the first successful integration of solution-processed 2D CdSe/CdS Core/Crown nanoplatelets in m-scale liquid core optical fibers. We compare the nanoplatelets' spectroscopic properties before and after filling them into the fibers and find that spontaneous emission is shifted and broadened. We even observe a first evidence of stimulated emission at high excitation energies. In conclusion, liquid core fibers constitute a novel and scalable platform for optical integration of nanoplatelets for applications as novel, highly reconfigurable laser gain medium.
Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression

Genetic algorithms are a well-known example of bio-inspired heuristic methods. They mimic natural selection by modeling several operators such as mutation, crossover, and selection. Recent discoveries about Epigenetics regulation processes that occur"on top of"or"in addition to"the genetic basis for inheritance involve changes that affect and improve gene expression. They raise the question of improving genetic algorithms (GAs) by modeling epigenetics operators. This paper proposes a new epigenetics algorithm that mimics the epigenetics phenomenon known as DNA methylation. The novelty of our epigenetics algorithms lies primarily in taking advantage of attention mechanisms and deep learning, which fits well with the genes enhancing/silencing concept. The paper develops theoretical arguments and presents empirical studies to exhibit the capability of the proposed epigenetics algorithms to solve more complex problems efficiently than has been possible with simple GAs; for example, facing two Non-convex (multi-peaks) optimization problems as presented in this paper, the proposed epigenetics algorithm provides good performances and shows an excellent ability to overcome the lack of local optimum and thus find the global optimum.
Stochastic Submodular Maximization via Polynomial Estimators

In this paper, we study stochastic submodular maximization problems with general matroid constraints, that naturally arise in online learning, team formation, facility location, influence maximization, active learning and sensing objective functions. In other words, we focus on maximizing submodular functions that are defined as expectations over a class of submodular functions with an unknown distribution. We show that for monotone functions of this form, the stochastic continuous greedy algorithm attains an approximation ratio (in expectation) arbitrarily close to $(1-1/e) \approx 63\%$ using a polynomial estimation of the gradient. We argue that using this polynomial estimator instead of the prior art that uses sampling eliminates a source of randomness and experimentally reduces execution time.
Alternate Loss Functions Can Improve the Performance of Artificial Neural Networks

All machine learning algorithms use a loss, cost, utility or reward function to encode the learning objective and oversee the learning process. This function that supervises learning is a frequently unrecognized hyperparameter that determines how incorrect outputs are penalized and can be tuned to improve performance. This paper shows that training speed and final accuracy of neural networks can significantly depend on the loss function used to train neural networks. In particular derivative values can be significantly different with different loss functions leading to significantly different performance after gradient descent based Backpropagation (BP) training. This paper explores the effect on performance of new loss functions that are more liberal or strict compared to the popular Cross-entropy loss in penalizing incorrect outputs. Eight new loss functions are proposed and a comparison of performance with different loss functions is presented. The new loss functions presented in this paper are shown to outperform Cross-entropy loss on computer vision and NLP benchmarks.
Neural Refinement for Absolute Pose Regression with Feature Synthesis

Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model, which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of-the-art single-image APR accuracy on indoor and outdoor datasets.
DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations

Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep re-inforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been paid to hand-object interaction representations for high-level generalization. In this paper, we propose a novel geometric and spatial hand-object interaction representation, named DexRep, to capture object surface features and the spatial relations between hands and objects during grasping. DexRep comprises Occupancy Feature for rough shapes within sensing range by moving hands, Surface Feature for changing hand-object surface distances, and LocalGeo Feature for local geometric surface features most related to potential contacts. Based on the new representation, we propose a dexterous deep reinforcement learning method DexRepNet to learn a generalizable grasping policy. Experimental results show that our method outperforms baselines using existing representations for robotic grasping dramatically both in grasp success rate and convergence speed. It achieves a 93% grasping success rate on seen objects and higher than 80% grasping success rates on diverse objects of unseen categories in both simulation and real-world experiments.
Steady nonlinear capillary waves on curved sheets

This paper presents a new class of solutions for steady nonlinear capillary waves on a curved sheet of fluid in the plane. The solutions are exact in that the free surfaces of the sheet and the associated flow field can be found in closed form. The solutions are generalizations of the classic solutions for finite amplitude waves on fluid sheets [5] to the case where the fluid sheets are curved.