Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Nonlinear Model Order Reduction of the Quasigeostrophic Equations through a Physics-Informed Convolutional Autoencoder

Reduced order modeling (ROM) is a field of techniques that approximates complex physics-based models of real-world processes by inexpensive surrogates that capture important dynamical characteristics with a smaller number of degrees of freedom. Traditional ROM techniques such as proper orthogonal decomposition (POD) focus on linear projections of the dynamics onto a set of spectral features. In this paper we explore the construction of ROM using autoencoders (AE) that perform nonlinear projections of the system dynamics onto a low dimensional manifold learned from data. The approach uses convolutional neural networks (CNN) to learn spatial features as opposed to spectral, and utilize a physics informed (PI) cost function in order to capture temporal features as well. Our investigation using the quasi-geostrophic equations reveals that while the PI cost function helps with spatial reconstruction, spatial features are less powerful than spectral features, and that construction of ROMs through machine learning-based methods requires significant investigation into novel non-standard methodologies.
A Different Perspective On The Stochastic Convex Feasibility Problem

We investigate the stochastic convex feasibility problem, the problem of finding x satisfying a system of constraints fω(x) ≤ 0 (ω ∈ Ω), where the functions fω are convex, and where Ω is the sample space in a probability triple (Ω,F , P ). Our aim is to solve the problem approximately, in the sense that given values > 0 and 0 < Γ < 1, a point x is found for which the probability measure P satisfies P{ω ∈ Ω | fω(x) ≤ } ≥ 1 − Γ. Our primary vehicle for obtaining such a point x is a subgradient method taking “Polyak steps”, for which the only input is the size of minibatches to be employed in the effort. We prove stochastic iteration bounds for the general setting, and improved bounds when a notion of “Hölderian growth” is satisfied, showing, for example, linear convergence when the growth is linear (as happens, say, for a finite system of linear inequalities). We also derive deterministic iteration bounds for knowing “with high confidence” that an iterate xk is in hand which fulfills the desired inequality, P{ω ∈ Ω | fω(xk) ≤ } ≥ 1− Γ.
An Introduction to Hamiltonian Monte Carlo Method for Sampling

The goal of this article is to introduce the Hamiltonian Monte Carlo (HMC) method -- a Hamiltonian dynamics-inspired algorithm for sampling from a Gibbs density $\pi(x) \propto e^{-f(x)}$. We focus on the"idealized"case, where one can compute continuous trajectories exactly. We show that idealized HMC preserves $\pi$ and we establish its convergence when $f$ is strongly convex and smooth.
Asymptotically optimal strategies in a diffusion approximation of a repeated betting game

We construct a diffusion approximation of a repeated game in which agents make bets on outcomes of i.i. d. random vectors and their strategies are close to an asymptotically optimal strategy. This model can be interpreted as trading in an asset market with short-lived assets. We obtain sufficient conditions for a strategy to maintain a strictly positive share of total wealth over the infinite time horizon. For the game with two players, we find necessary and sufficient conditions for the wealth share process to be transient or recurrent in this model, and also in its generalization with Markovian regime switching.
A trustless decentralized protocol for distributed consensus of public quantum random numbers

Quantum random number (QRNG) beacons distinguish themselves from classical counterparts by providing intrinsic unpredictability originating from the fundamental laws of quantum mechanics. Most demonstrations have focused on certifiable randomness generators to guarantee the public that their genuineness is independent from imperfect implementations. These efforts however do not benefit applications where multiple distrusted users need a common set of random numbers, as they must rely on the honesty of beacon owners. In this paper, we formally introduce a design and proof-of-principle experiment of the first consensus protocol producing QRNs in a decentralized environment (dQRNG). Such protocol allows N number of participants contribute in the generation process and publicly verify numbers they collect. Security of the protocol is guaranteed given(N-1) dishonest participants. Our method is thus suited for distribute systems that requires a bias-resistant, highly secure, and public-verifiable random beacon.
In vivo functional and structural retina imaging using multimodal photoacoustic remote sensing microscopy and optical coherence tomography

We have developed a multimodal photoacoustic remote sensing (PARS) microscope combined with swept source optical coherence tomography for in vivo, non-contact retinal imaging. Building on the proven strength of multiwavelength PARS imaging, the system is applied for estimating retinal oxygen saturation in the rat retina. The capability of the technology is demonstrated by imaging both microanatomy and the microvasculature of the retina in vivo. To our knowledge this is the first time a non-contact photoacoustic imaging technique is employed for in vivo oxygen saturation measurement in the retina.
GLocal-K: Global and Local Kernels for Recommender Systems

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a G lobal-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.
A Radiatively Driven Wind from the η Tel Debris Disk

We present far- and near-ultraviolet absorption spectroscopy of the ∼23 Myr edge-on debris disk surrounding the A0V star η Telescopii, obtained with the Hubble Space Telescope Space Telescope Imaging Spectrograph. We detect absorption lines from C i, C ii, O i, Mg ii, Al ii, Si ii, S ii, Mn ii, Fe ii, and marginally N i. The lines show two clear absorption components at −22.7 ± 0.5 km s−1 and −17.8 ± 0.7 km s−1, which we attribute to circumstellar (CS) and interstellar gas, respectively. CO absorption is not detected, and we find no evidence for star-grazing exocomets. The CS absorption components are blueshifted by −16.9 ± 2.6 km s−1 in the star’s reference frame, indicating that they are outflowing in a radiatively driven disk wind. We find that the C/Fe ratio in the η Tel CS gas is significantly higher than the solar ratio, as is the case in the β Pic and 49 Cet debris disks. Unlike those disks, however, the measured C/O ratio in the η Tel CS gas is consistent with the solar value. Our analysis shows that because η Tel is an earlier type star than β Pic and 49 Cet, with more substantial radiation pressure at the dominant C ii transitions, this species cannot bind the CS gas disk to the star as it does for β Pic and 49 Cet, resulting in the disk wind.
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences

A growing number of commercially available mobile phones come with integrated high-resolution digital cameras. That enables a new class of dedicated applications to image analysis such as mobile visual search, image cropping, object detection, content-based image retrieval, image classification. In this paper, a new mobile application for image content retrieval and classification for mobile device display is proposed to enrich the visual experience of users. The mobile application can extract a certain number of images based on the content of an image with visual saliency methods aiming at detecting the most critical regions in a given image from a perceptual viewpoint. First, the most critical areas from a perceptual perspective are extracted using the local maxima of a 2D saliency function. Next, a salient region is cropped using the bounding box centred on the local maxima of the thresholded Saliency Map of the image. Then, each image crop feds into an Image Classification system based on SVM and SIFT descriptors to detect the class of object present in the image. ImageNet repository was used as the reference for semantic category classification. Android platform was used to implement the mobile application on a client-server architecture. A mobile client sends the photo taken by the camera to the server, which processes the image and returns the results (image contents such as image crops and related target classes) to the mobile client. The application was run on thousands of pictures and showed encouraging results towards a better user visual experience with mobile displays.
Automated Generation of Accurate & Fluent Medical X-ray Reports

Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Existing medical report generation efforts emphasize producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. Our generated medical reports, on the other hand, are fluent and, more importantly, clinically accurate. This is achieved by our fully differentiable and end-to-end paradigm that contains three complementary modules: taking the chest X-ray images and clinical history document of patients as inputs, our classification module produces an internal checklist of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, to produce the medical report; meanwhile, our generator also creates a weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to disease-related topics. Empirical evaluations demonstrate very promising results achieved by our approach on commonly-used metrics concerning language fluency and clinical accuracy. Moreover, noticeable performance gains are consistently observed when additional input information is available, such as the clinical document and extra scans from different views.
On the Representation of Correlated Exponential Distributions by Phase Type Distributions

In this paper we present results for bivariate exponential distributions which are represented by phase type distributions. The paper extends results from previous publications [3, 11] on this topic by introducing new representations that require a smaller number of phases to reach some correlation coefficient and introduces different ways to describe correlation between exponentially distributed random variables.
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System

Chinese modern poetry generation has been a challenging task. One issue is the Chinese word segmentation (CWS) which is critical to comprehend the Chinese language but was not always considered in common tokenization methods. Another is the decoding (sampling) method which may induce repetition and boredom and severely lower the diversity of the generated poetry. To address these issues, we present Lingxi, a diversity-aware Chinese modern poetry generation system. For the CWS issue, we propose a novel framework that incorporates CWS in the tokenization process. The proposed method can achieve a high vocabulary coverage rate with a reasonable vocabulary size. For the decoding method and the diversity issue, we propose a novel sampling algorithm that flattens the high likelihood part of the predicted distribution of the language model to emphasize the comparatively low-likelihood words and increase the diversity of generated poetry. Empirical results show that even when the top 60% of cumulative probability mass of the predicted distribution is flattened, our method achieves comparable or even better performance than baseline sampling methods. Our system is available at http://lingxi.website.
4-bit Quantization of LSTM-based Speech Recognition Models

We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition (ASR): hybrid Deep Bidirectional LSTM - Hidden Markov Models (DBLSTM-HMMs) and Recurrent Neural Network - Transducers (RNN-Ts). Using a 4-bit integer representation, a na\"ive quantization approach applied to the LSTM portion of these models results in significant Word Error Rate (WER) degradation. On the other hand, we show that minimal accuracy loss is achievable with an appropriate choice of quantizers and initializations. In particular, we customize quantization schemes depending on the local properties of the network, improving recognition performance while limiting computational time. We demonstrate our solution on the Switchboard (SWB) and CallHome (CH) test sets of the NIST Hub5-2000 evaluation. DBLSTM-HMMs trained with 300 or 2000 hours of SWB data achieves $<$0.5% and $<$1% average WER degradation, respectively. On the more challenging RNN-T models, our quantization strategy limits degradation in 4-bit inference to 1.3%.
Blind Reconstruction of Multilayered Tissue Profiles with UWB Radar Under Bayesian Setting

In this paper, we investigate the problem of inverse electromagnetic scattering to recover multilayer human tissue profiles using ultrawideband radar systems in Bayesian setting. We study the recovery problem in blind setting, in which we simultaneously estimate both the dielectric/geometric properties of the one-dimensional target tissue profile and the transmitted radar waveform. To perform Bayesian parameter estimation, we propose a hybrid and adaptive Markov Chain Monte Carlo method, which combines the Slice sampling and Hamiltonian Monte Carlo approaches. The introduced sampling mechanism also incorporates the Parallel Tempering approach to escape from the local optimal regions of the complex posterior distribution. We provide empirical support through various numerical simulations for the achieved enhanced sampling efficiency compared to conventional sampling schemes. To investigate the recovery performance, we work on synthetic measurements simulating actual radar returns from multilayer tissue profiles. We derive theoretical bounds for the best achievable estimation performance in terms of normalized root mean square error and provide a comparison with the performance of our estimator.
Optoelectronic Fingerprints of Interference between Different Charge Carriers in Graphene Superlattices and Analogies to Twisted Graphene Bilayers

Motivated by recent experimental findings on the low-energy spectrum of Kekulé-patterned graphene, the optoelectronic signatures of graphene superlattices with a spatial modulation that triples the size of the unit cell and folds the valleys to the center of the Brillouin zone are studied. For superlattices like those visualized in recent experiments, the optoelectronic response reveals multiple species of carriers distinguished by their effective masses or Fermi velocities. Their signatures are similar to those of systems hosting multifold fermions in which different frequency intervals are dominated by different types of quasiparticles. Remarkably, the response of these systems exhibits a characteristic peak in the optical conductivity suggesting a kind of interference between the different species of carriers. We also discuss a related superlattice that exhibits merging Dirac cones and band flattening, with a Hamiltonian that resembles a version of the chiral model for twisted bilayer graphene where the long-range moiré modulation has been substituted by a two-parameter bias.
The ICSCREAM Methodology: Identification of Penalizing Configurations in Computer Experiments Using Screening and Metamodel—Applications in Thermal Hydraulics

Abstract In the framework of risk assessment in nuclear accident analysis, best-estimate computer codes associated with probabilistic modeling of uncertain input variables are used to estimate safety margins. Often, a first step in such uncertainty quantification studies is to identify the critical configurations (or penalizing, in the sense of a prescribed safety margin) of several input parameters (called scenario inputs) under the uncertainty of the other input parameters. However, the large CPU-time cost of most of the computer codes used in nuclear engineering, as the ones related to thermal-hydraulic accident scenario simulations, involves developing highly efficient strategies. This work focuses on machine learning algorithms by way of a metamodel-based approach (i.e., a mathematical model that is fitted on a small sample of simulations). To achieve it with a very large number of inputs, a specific and original methodology called Identification of penalizing Configurations using SCREening And Metamodel (ICSCREAM) is proposed. The screening of influential inputs is based on an advanced global sensitivity analysis tool (Hilbert-Schmidt Independence Criterion importance measures). A Gaussian process metamodel is then sequentially built and used to estimate within a Bayesian framework the conditional probabilities of exceeding a high-level threshold according to the scenario inputs. The efficiency of this methodology is illustrated with two high-dimensional (around a hundred inputs) thermal-hydraulic industrial cases simulating an accident of primary coolant loss in a pressurized water reactor. For both use cases, the study focuses on the peak cladding temperature (PCT), and critical configurations are defined by exceeding the 90%-quantile of the PCT. In both cases, using only around one thousand code simulations, the ICSCREAM methodology allows one to estimate the impact of the scenario inputs and their critical areas of values.
SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

Surface reconstruction from point clouds is a fundamental problem in the computer vision and graphics community. Recent state-of-the-arts solve this problem by individually optimizing each local implicit field during inference. Without considering the geometric relationships between local fields, they typically require accurate normals to avoid the sign conflict problem in overlapped regions of local fields, which severely limits their applicability to raw scans where surface normals could be unavailable. Although SAL breaks this limitation via sign-agnostic learning, further works still need to explore how to extend this technique for local shape modeling. To this end, we propose to learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks, to simultaneously achieve advanced scalability to large-scale scenes, generality to novel shapes, and applicability to raw scans in a unified framework. Concretely, we achieve this goal by a simple yet effective design, which further optimizes the pre-trained occupancy prediction networks with an unsigned cross-entropy loss during inference. The learning of occupancy fields is conditioned on convolutional features from an hourglass network architecture. Extensive experimental comparisons with previous state-of-the-arts on both object-level and scene-level datasets demonstrate the superior accuracy of our approach for surface reconstruction from un-orientated point clouds. The code is available at https://github.com/tangjiapeng/SA-ConvONet.
Effect of Gap Size on Seasonal Variation of Soil Chemical Properties in Subtropical Forest, Southern China

Gap area has an effect on the soil chemical properties. To find out whatever the effect of gap area on the soil chemical properties, gap area was divided into small, medium, and large gap sizes and under the canopy as control. To determine the soil chemical properties such as pH, organic matter (OM) (g/kg), total nitrogen (TN) %, total carbon (TC) %, C/N ratio, total phosphorus (TP) (g/kg), total potassium (TK) (g/kg), available phosphorus (AP) (mg/kg), available potassium (AK) (mg/kg), and hydrolysable nitrogen (HN) (mg/kg), the grid system (3×3 m 2 ) was applied for collecting the soil samples over the entire gap area at 20 cm depth. The results showed that most of the soil chemical properties varied significantly with respect to the gap sizes and the seasons. The summer season had greatly influenced all the chemical properties when compared with the winter season. However, the soil chemical properties exhibited irregular variation in different gap sizes. Moreover, a strong significant positive correlation among TN, TC, C/N, and OM was found. The highest soil OM in the large gap size indicated the high potential of nutrient supply for the plant growth. The results of this study provide useful information for the regeneration and conservation of C. kawakamii .
Approaches for Hyperspectral Image Classification Detailed Review

Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.