Miracle-dz/UniFAR-FTUs huggingface dataset:
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among millions of people worldwide. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, Artificial Intelligence (AI) services in the medical field, including automated CSR detection, are now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers deliver accurate, fast, and reliable CSR detection. However, more research needs to be conducted on publicly available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease. INDEX TERMS Central Serous Retinopathy, Deep Learning, Fundus Images, Machine Learning, Optical Coherence Tomography Images
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons

With the growing demand for deploying Deep Learning models to the “edge”, it is paramount to develop techniques that allow to execute state-of-the-art models within very tight and limited resource constraints. In this work we propose a software-hardware optimization paradigm for obtaining a highly efficient execution engine of deep neural networks (DNNs) that are based on fully-connected layers. The work’s approach is centred around compression as a means for reducing the area as well as power requirements of, concretely, multilayer perceptrons (MLPs) with high predictive performances. Firstly, we design a novel hardware architecture named FantastIC4, which (1) supports the efficient on-chip execution of multiple compact representations of fully-connected layers and (2) minimizes the required number of multipliers for inference down to only 4 (thus the name). Moreover, in order to make the models amenable for efficient execution on FantastIC4, we introduce a novel entropy-constrained training method that renders them to be robust to 4bit quantization and highly compressible in size simultaneously. The experimental results show that we can achieve throughputs of 2.45 TOPS with a total power consumption of 3.6W on a Virtual Ultrascale FPGA XCVU440 device implementation, and achieve a total power efficiency of 20.17 TOPS/W on a 22nm process ASIC version. When compared to other state-of-the-art accelerators designed for the Google Speech Command (GSC) dataset, FantastIC4 is better by $51\times $ in terms of throughput and $145\times $ in terms of area efficiency (GOPS/mm2).
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal

In remote sensing images, the existence of the thin cloud is an inevitable and ubiquitous phenomenon that crucially reduces the quality of imageries and limits the scenarios of application. Therefore, thin cloud removal is an indispensable procedure to enhance the utilization of remote sensing images. Generally, even though contaminated by thin clouds, the pixels still retain more or less surface information. Hence, different from thick cloud removal, thin cloud removal algorithms normally concentrate on inhibiting the cloud influence rather than substituting the cloud-contaminated pixels. Meanwhile, considering the surface features obscured by the cloud are usually similar to adjacent areas, the dependency between each pixel of the input is useful to reconstruct contaminated areas. In this paper, to make full use of the dependencies between pixels of the image, we propose a Multi-Head Linear Attention Generative Adversarial Network (MLAGAN) for Thin Cloud Removal. The MLA-GAN is based on the encoding-decoding framework consisting of multiple attention-based layers and deconvolutional layers. Compared with six deep learning-based thin cloud removal benchmarks, the experimental results on the RICE1 and RICE2 datasets demonstrate that the proposed framework MLA-GAN has dominant advantages in thin cloud removal.
International Journal of Food Engineering Biochemical Changes during Alcoholic Fermentation in the Production of ` ` Tchapalo ' ' , a Traditional Sorghum Beer

The production of tchapalo remains more or less an empirical process. For the improvement of the product quality, there is a need of more knowledge on this traditional process and the beer characteristics. The alcoholic fermentation is one of the most steps which most determines the quality of the beer. In this study, the characteristics of the traditional starter and the evolution of temperature, ethanol, sugars and organic acids were assessed at small-scale production during alcoholic fermentation of tchapalo. Brewers used traditional starter at the rate of 0.76-0.88 % when it was dried or at 2.38 % when it was not. When the wort was at 36.6-39.3 °C, the starter was added to initiate the fermentation. Temperature decreased to 34.6-36.5 °C at the end of the fermentation which was concluded after 7.6 h to 8.4 h. Sugars were consumed during the fermentation but it remained 12.9 to 16.57 g/L of reducing sugars and 8.02 to 9.12 °Brix of TSS in the final product. Ethanol content increased from 0.02-0.35 % (v/v) in the wort to 3.5-4.59 % in the beer. The oxalic, citric, tartaric, malic, lactic, fumaric and propionic acids were found in the wort and in tchapalo and increased or decreased during fermentation differently from one site to another. But, the acetic acid was not always detected in both the wort and the beer.
Asymptotic behavior of a low-temperature non-cascading 2-GREM dynamics at extreme time scales

We derive the scaling limit for the Hierarchical Random Hopping dynamics for the non cascading 2-GREM at low temperatures and time scales where the dynamics is close to equilibrium. The fine tuning phenomenon plays a role (under certain choices of parameters of the model), yielding three dynamical regimes. In contrast to the cascading case, the pairs of first and second level energies have fluctuations which scale with the volume, and this leads to a family of time scales where we see the dynamics moving through only a part of the full low temperature energy landscape.
Grant-Free Random Access in Machine-Type Communication: Approaches and Challenges

Massive machine-type communication (MTC) is expected to play a key role in supporting Internet of Things (IoT) applications such as smart cities, smart factories, and connected vehicles through cellular networks. MTC is characterized by a large number of MTC devices and their sparse activities, which are difficult to be supported by conventional approaches and motivate the design of new access technologies. In particular, in the 5th generation (5G), grant-free (GF) or 2-step random access schemes are introduced for MTC to be more efficient by reducing signaling overhead. In this article, we first introduce GF random access and discuss how it can be modified with massive multiple-input multiple-output (MIMO) to exploit a high spatial multiplexing gain. We then explain preamble designs that can improve the performance and variations based on the notions of semi-GF random access and non-orthogonal multiple access (NOMA). Finally, design challenges of GF random access toward next generation cellular systems are presented.
Oxygenated Functional Group Density on Graphene Oxide: Its Effect on Cell Toxicity

The association of cellular toxicity with the physiochemical properties of graphene‐based materials is largely unexplored. A fundamental understanding of this relationship is essential to engineer graphene‐based nanomaterials for biomedical applications. Here, an in vitro toxicological assessment of graphene oxide (GO) and reduced graphene oxide (RGO) and in correlation with their physiochemical properties is reported. GO is found to be more toxic than RGO of same size. GO and RGO induce significant increases in both intercellular reactive oxygen species (ROS) levels and messenger RNA (mRNA) levels of heme oxygenase 1 (HO1) and thioredoxin reductase (TrxR). Moreover, a significant amount of DNA damage is observed in GO treated cells, but not in RGO treated cells. Such observations support the hypothesis that oxidative stress mediates the cellular toxicity of GO. Interestingly, oxidative stress induced cytotoxicity reduces with a decreasing extent of oxygen functional group density on the RGO surface. It is concluded that although size of the GO sheet plays a role, the functional group density on the GO sheet is one of the key components in mediating cellular cytotoxicity. By controlling the GO reduction and maintaining the solubility, it is possible to minimize the toxicity of GO and unravel its wide range of biomedical applications.
CuO/Cu(OH) 2 hierarchical nanostructures as bactericidal photocatalysts

Various morphologies of CuO/Cu(OH)2 nanostructures with different adsorbed –OH contents were synthesized on an acid-treated Cu foil through variation of NaOH concentration from 0 to 50 mM with an in situoxidation method. X-ray diffractometry and X-ray photoelectron spectroscopy (XPS) indicated formation of CuO on the Cu(OH)2 crystalline phase at a growth temperature of 60 °C for 20 h. Antibacterial activity of the nanostructures against Escherichia coli bacteria was studied in the dark and under light irradiation. The nanostructures grown at a NaOH concentration of 30 mM showed the highest surface area and the strongest antibacterial activity among the samples. After elimination of the contribution of the effective surface area of the nanostructures to the antibacterial activity, it was found that the surface morphology and chemical composition of the nanostructures were the other most important parameters in the antibacterial activity of the nanostructures. Using XPS analysis, the better photocatalytic activity per surface area of the nanostructures prepared at higher NaOH concentrations was substantially attributed to the amount of adsorbed OH bonds on the surface of the nanostructures.
Massive heartbeat stars from TESS

Heartbeat stars are eccentric binaries exhibiting characteristic shape of brightness changes during periastron passage caused by tidal distortion of the components. Variable tidal potential can drive tidally excited oscillations (TEOs), which are usually gravity modes. Studies of heartbeat stars and TEOs open a new possibility to probe interiors of massive stars. There are only a few massive (masses of components $\gtrsim 2 $M$_\odot$) systems of this type known. Using TESS data from the first 16 sectors, we searched for new massive heartbeat stars and TEOs using a sample of over 300 eccentric spectroscopic binaries. We analysed TESS 2-min and 30-min cadence data. Then, we fitted Kumar's analytical model to the light curves of stars showing heartbeats and performed times-series analysis of the residuals searching for TEOs and periodic intrinsic variability. We found 20 massive heartbeat systems, of which seven show TEOs. The TEOs occur at harmonics of orbital frequencies in the range between 3 and 36, with the median value equal to 9, lower than those in known Kepler systems with TEOs. The most massive system in this sample is the quadruple star HD 5980, a member of Small Magellanic Cloud. With the total mass of $\sim$150 M$_{\odot}$ it is the most massive system showing a heartbeat. Six stars in the sample of the new heartbeat stars are eclipsing. Comparison of the parameters derived from fitting Kumar's model and from light-curve modelling shows that Kumar's model does not provide reliable parameters. Finally, intrinsic pulsations of $\beta$ Cep, SPB, $\delta$ Sct, and $\gamma$ Dor-type were found in nine heartbeat systems. This opens an interesting possibility of studies of pulsation-binarity interaction and the co-existence of forced and self-excited oscillations.
Little Hankel Operators Between Vector-Valued Bergman Spaces on the Unit Ball

Abstract. In this paper, we study the boundedness and the compactness of the little Hankel operators hb with operator-valued symbols b between different weighted vector-valued Bergman spaces on the open unit ball Bn in C. More precisely, given two complex Banach spaces X, Y, and 0 < p, q ≤ 1, we characterize those operator-valued symbols b : Bn → L(X,Y ) for which the little Hankel operator hb : A p α(Bn, X) −→ A q α(Bn, Y ), is a bounded operator. Also, given two reflexive complex Banach spaces X,Y and 1 < p ≤ q < ∞, we characterize those operator-valued symbols b : Bn → L(X,Y ) for which the little Hankel operator hb : A p α(Bn,X) −→ A q α(Bn, Y ), is a compact operator.
The Probabilistic Serial and Random Priority Mechanisms with Minimum Quotas

Consider the problem of assigning indivisible objects to agents with strict ordinal preferences over objects, where each agent is interested in consuming at most one object, and objects have integer minimum and maximum quotas. We define an assignment to be feasible if it satisfies all quotas and assume such an assignment always exists. The Probabilistic Serial (PS) and Random Priority (RP) mechanisms are generalised based on the same intuitive idea: Allow agents to consume their most preferred available object until the total mass of agents yet to be allocated is exactly equal to the remaining amount of unfilled lower quotas; in this case, we restrict agents' menus to objects which are yet to fill their minimum quotas. We show the mechanisms satisfy the same criteria as their classical counterparts: PS is ordinally efficient, envy-free and weakly strategy-proof; RP is strategy-proof, weakly envy-free but not ordinally efficient.
A shear-compression damage model for the simulation of underground mining by block caving

Block caving is an ore extraction technique used in the copper mines of Chile. It uses gravity to ease the breaking of rocks, and to facilitate the extraction from the mine of the resulting mixture of ore and waste. To simulate this extraction process numerically and better understand its impact on the mine environment, we study 3 variational models for damage, based on the gradient damage model of Pham and Marigo (2010). In these models, the damage criterion may exhibit an anisotropic dependence on the spherical and deviatoric parts of the stress tensor. We report simulations that satisfactorily represent the expected evolution of the stress field in a block caving operation.
Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses

Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2D pose skeleton. We evaluate the generated videos quantitatively and qualitatively showing that the current models are not enough to generated adequate videos for Sign Language due to lack of detail in hands.
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation

Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a large volume of publicly available labeled radiographs. These pre-trained DL models' ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs. In chest radiographs, the heterogeneity in distributions arises from the diverse conditions in X-ray equipment and their configurations used for generating the images. In the machine learning community, the challenges posed by the heterogeneity in the data generation source is known as domain shift, which is a mode shift in the generative model. In this work, we introduce a domain-shift detection and removal method to overcome this problem. Our experimental results show the proposed method's effectiveness in deploying a pre-trained DL model for abnormality detection in chest radiographs in a clinical setting.
A PROOF of Weak Graph Positivity, for a Large Range of the Parameters

One deals with r -regular bipartite graphs with 2 n vertices. In a previous paper Butera, Pernici, and the author have introduced a quantity d ( i ), a function of the number of i matchings, and conjectured that as n goes to inﬁnity the fraction of graphs that satisfy ∆ k d ( i ) ≥ 0, for all k and i , approaches 1. Here ∆ is the ﬁnite diﬀerence operator. This conjecture we called the “graph positivity conjecture”. “Weak graph positivity” is the conjecture that for each i and k the probability that ∆ k d ( i ) ≥ 0 goes to 1 as n goes to inﬁnity. Here we prove this for the range of parameters where r ≤ 10 , i + k ≤ 100 , k ≤ 27. or i + k ≤ 29 all r . A formalism of Wanless as systematized by Pernici is central to this eﬀort.
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems

We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show that our method significantly outperforms the state-of-the-art deep learning based models.
Identifying Latent Groups in Spatial Panel Data Using a Markov Random Field Constrained Product Partition Model

Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in many applications such as economics and environmental science. In this paper, we focus on regression models for spatial panel data analysis, where repeated measurements are collected over time at various spatial locations. We propose a novel class of nonparametric priors that combines Markov random field (MRF) with the product partition model (PPM), and show that the resulting prior, called by MRF-PPM, is capable of identifying the latent group structure among the spatial locations while efficiently utilizing the spatial dependence information. We derive a closed-form conditional distribution for the proposed prior and introduce a new way to compute the marginal likelihood that renders efficient Bayesian inference. We further study the theoretical properties of the proposed MRF-PPM prior and show a clustering consistency result for the posterior distribution. We demonstrate the excellent empirical performance of our method via extensive simulation studies and applications to a US precipitation data and a California median household income data study.
Frequency Response of Metal-Oxide Memristors

Memristors have been at the forefront of nanoelectronics research for the last few decades, offering a valuable component to reconfigurable computing. Their attributes have been studied extensively along with applications that leverage their state-dependent programmability in a static fashion. However, practical applications of memristor-based alternating current (ac) circuits have been rather sparse, with only a few examples found in the literature where their use is emulated at higher frequencies. In this work, we study the behavior of metal-oxide memristors under a noninvasive ac perturbation in a range of frequencies, from 103 to 107 Hz. Metal-oxide memristors are found to behave as RC low-pass filters and they present a variable cut-off frequency when their state is switched, thus providing a window of reconfigurability when used as filters. We further study this behavior across distinct material systems, and we show that the usable reconfigurability window of the devices can be tailored to encompass specific frequency ranges by amending the devices’ capacitance. This study extends current knowledge on metal-oxide memristors by characterizing their frequency-dependent characteristics, providing useful insights for their use in reconfigurable ac circuits.
The Age of Information in a Discrete Time Queue: Stationary Distribution and Non-Linear Age Mean Analysis

In this work, we investigate information freshness in a status update communication system consisting of a source-destination link. Initially, we study the properties of a sample path of the age of information (AoI) process at the destination. We obtain a general formula of the stationary distribution of the AoI, under the assumption of ergodicity. We relate this result to a discrete time queueing system and provide a general expression of the generating function of AoI in relation with the system time and the peak age of information (PAoI) metric. Furthermore, we consider three different single-server system models and we obtain closed-form expressions of the generating functions and the stationary distributions of the AoI and the PAoI. The first model is a first-come-first-served (FCFS) queue, the second model is a preemptive last-come-first-served (LCFS) queue, and the last model is a bufferless system with packet dropping. We build upon these results to provide a methodology for analyzing general non-linear age functions for this type of systems, using representations of functions as power series.
Incremental Verification of Fixed-Point Implementations of Neural Networks

Implementations of artificial neural networks (ANNs) might lead to failures, which are hardly predicted in the design phase since ANNs are highly parallel and their parameters are barely interpretable. Here, we develop and evaluate a novel symbolic verification framework using incremental bounded model checking (BMC), satisfiability modulo theories (SMT), and invariant inference, to obtain adversarial cases and validate coverage methods in a multi-layer perceptron (MLP). We exploit incremental BMC based on interval analysis to compute boundaries from a neuron's input. Then, the latter are propagated to effectively find a neuron's output since it is the input of the next one. This paper describes the first bit-precise symbolic verification framework to reason over actual implementations of ANNs in CUDA, based on invariant inference, therefore providing further guarantees about finite-precision arithmetic and its rounding errors, which are routinely ignored in the existing literature. We have implemented the proposed approach on top of the efficient SMT-based bounded model checker (ESBMC), and its experimental results show that it can successfully verify safety properties, in actual implementations of ANNs, and generate real adversarial cases in MLPs. Our approach was able to verify and produce adversarial examples for 85.8% of 21 test cases considering different input images, and 100% of the properties related to covering methods. Although our verification time is higher than existing approaches, our methodology can consider fixed-point implementation aspects that are disregarded by the state-of-the-art verification methodologies.