Miracle-dz/UniFAR-FTUs huggingface dataset:
Modification of transition radiation by three-dimensional topological insulators

We study how transition radiation is modiﬁed by the presence of a generic magnetoelectric medium with a special focus on topological insulators. To this end, we use the Green’s function for the electromagnetic ﬁeld in presence of a plane interface between two topological insulators with diﬀerent topological parameters, permittivities, and permeabilities. We employ the far-ﬁeld approximation together with the steepest descent method to obtain approximate analytical expressions for the electromagnetic ﬁeld. Through this method, we ﬁnd that the electric ﬁeld is a superposition of spherical waves and lateral waves. Contributions of both kinds can be attributed to a purely topological origin. After computing the angular distribution of the radiation, we ﬁnd that in a region far from the interface the main contribution to the radiation comes from the spherical waves. We present typical radiation patterns for the topological insulator TlBiSe 2 and the magnetoelectric TbPO 4 . In the ultra-relativistic case, the additional contributions from the magnetoelectric coupling appreciably enhance the global maximum of the angular distribution. We also present an analytic expression for the frequency distribution of the radiation for this case. We ﬁnd that in the limit where the permittivities are equal, there still exists transition radiation of the order of the square of the topological parameter with a pure topological origin.
Improving Dither Modulation based Robust Steganography by Overflow Suppression

Nowadays, people are sharing their pictures on online social networks (OSNs), so OSN is a good platform for Steganography. But OSNs usually perform JPEG compression on the uploaded image, which will invalidate most of the existing steganography algorithms. Recently, some works try to design robust steganography which can resist JPEG compression, such as Dither Modulation-based robust Adaptive Steganography (DMAS) and Generalized dither Modulation-based robust Adaptive Steganography (GMAS). They relieve the problem that the receivers cannot extract the message correctly when the quality factor of channel JPEG compression is larger than that of cover images. However, they only can realize limited resistance to detection and compression due to robust domain selection. To overcome this problem, we meticulously explore three lossy operations in the JPEG recompression and discover that the key problem is spatial overflow. Then two preprocessing methods Overall Scaling (OS) and Specific Truncation (ST) are presented to remove overflow before message embedding as well as generate a reference image. The reference image is employed as the guidance to build asymmetric distortion for removing overflow during embedding. Experimental results show that the proposed methods significantly surpass GMAS in terms of security and achieve comparable robustness.
Spectral Efficiency of OTFS Based Orthogonal Multiple Access With Rectangular Pulses

In this paper, we consider Orthogonal Time Frequency Space (OTFS) modulation based multiple-access (MA). We specifically consider Orthogonal MA methods (OMA) where the user terminals (UTs) are allocated non-overlapping physical resource in the delay-Doppler (DD) and/or time-frequency (TF) domain. Well known OMA methods include, i) Guard Band (GB) based MA (GBMA), ii) Interleaved Delay Doppler MA (IDDMA), iii) Interleaved Time Frequency MA (ITFMA). The spectral efficiency (SE) performance of these OMA methods has only been studied for ideal transmit and receive pulses. With ideal pulses, IDDMA and ITFMA are free from multi-user interference (MUI) and unlike GBMA they do not use guard bands in the DD/TF domain. Since ideal pulses are not realizable, in this paper we study the SE performance of these OMA methods with practical rectangular pulses. For these OMA methods, we derive the expression for the received DD/TF domain symbols at the base station (BS) receiver and the effective DD domain channel matrix when rectangular pulses are used. Study of these expressions reveals the presence of MUI when rectangular pulses are used, although the amount of MUI in IDDMA is observed to be significantly smaller than that in ITFMA. We also derive the expression for the achievable sum SE. Through simulations, for practical values of the received signal-to-noise ratio, we observe that with rectangular pulses the sum SE achieved by the IDDMA method is significantly higher than that achieved by the ITFMA and GBMA methods.
Quillen homology of spectral Lie algebras with application to mod $p$ homology of labeled configuration spaces

A BSTRACT . We provide a general method computing the mod p Quillen homology of algebras over a monad that parametrizes the structure of mod p homology of spectral Lie algebras. This is the E 2 -page of the bar spectral sequence converging to the mod p topological Quillen homology of spectral Lie algebras. The computation of the Quillen homology of the trivial algebra allows us to deduce that the F p -linear spectral Lie operad is not formal. As an application, we study the mod p homology of the labeled conﬁguration space B k ( M ; X ) in a manifold M with labels in a spectrum X , which is the mod p topological Quillen homology of a certain spectral Lie algebra by a result of Knudsen. We obtain general upper bounds for the mod p homology of B k ( M ; X ) , as well as explicit computations for small k . When p is odd, we observe that the mod p homology of B k ( M n ; S r ) for small k depends on and only on the cohomology ring of the one-point compactiﬁcation of M when n + r is even. This supplements and contrasts with the result of B¨odigheimer-Cohen-Taylor when n + r is odd.
Neuro-Symbolic Forward Reasoning

Reasoning is an essential part of human intelligence and thus has been a long-standing goal in artificial intelligence research. With the recent success of deep learning, incorporating reasoning with deep learning systems, i.e., neuro-symbolic AI has become a major field of interest. We propose the Neuro-Symbolic Forward Reasoner (NSFR), a new approach for reasoning tasks taking advantage of differentiable forward-chaining using first-order logic. The key idea is to combine differentiable forward-chaining reasoning with object-centric (deep) learning. Differentiable forward-chaining reasoning computes logical entailments smoothly, i.e., it deduces new facts from given facts and rules in a differentiable manner. The object-centric learning approach factorizes raw inputs into representations in terms of objects. Thus, it allows us to provide a consistent framework to perform the forward-chaining inference from raw inputs. NSFR factorizes the raw inputs into the object-centric representations, converts them into probabilistic ground atoms, and finally performs differentiable forward-chaining inference using weighted rules for inference. Our comprehensive experimental evaluations on object-centric reasoning data sets, 2D Kandinsky patterns and 3D CLEVR-Hans, and a variety of tasks show the effectiveness and advantage of our approach.
PAGnol: An Extra-Large French Generative Model

Access to large pre-trained models of varied architectures, in many different languages, is central to the democratization of NLP. We introduce PAGnol, a collection of French GPT models. Using scaling laws, we efficiently train PAGnol-XL (1.5B parameters) with the same computational budget as CamemBERT, a model 13 times smaller. PAGnol-XL is the largest model trained from scratch for the French language. We plan to train increasingly large and performing versions of PAGnol, exploring the capabilities of French extreme-scale models. For this first release, we focus on the pre-training and scaling calculations underlining PAGnol. We fit a scaling law for compute for the French language, and compare it with its English counterpart. We find the pre-training dataset significantly conditions the quality of the outputs, with common datasets such as OSCAR leading to low-quality offensive text. We evaluate our models on discriminative and generative tasks in French, comparing to other state-of-the-art French and multilingual models, and reaching the state of the art in the abstract summarization task. Our research was conducted on the public GENCI Jean Zay supercomputer, and our models up to the Large are made publicly available.
Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.
Spark Deficient Gabor Frames for Inverse Problems

In this paper, we apply star-Digital Gabor Transform in analysis Compressed Sensing and speech denoising. Based on assumptions on the ambient dimension, we produce a window vector that generates a spark deficient Gabor frame with many linear dependencies among its elements. We conduct computational experiments on both synthetic and real-world signals, using as baseline three Gabor transforms generated by state-of-the-art window vectors and compare their performance to star-Gabor transform. Results show that the proposed star-Gabor transform outperforms all others in all signal cases.
Endoscopic en-face optical coherence tomography and fluorescence imaging using correlation-based probe tracking

Forward-viewing endoscopic optical coherence tomography (OCT) provides 3D imaging in vivo, and can be combined with widefield fluorescence imaging by use of a double-clad fiber. However, it is technically challenging to build a high-performance miniaturized 2D scanning system with a large field-of-view. In this paper we demonstrate how a 1D scanning probe, which produces cross-sectional OCT images (B-scans) and 1D fluorescence T-scans, can be transformed into a 2D scanning probe by manual scanning along the second axis. OCT volumes are assembled from the B-scans using speckle decorrelation measurements to estimate the out-of-plane motion along the manual scan direction. Motion within the plane of the B-scans is corrected using image registration by normalized cross correlation. En-face OCT slices and fluorescence images, corrected for probe motion in 3D, can be displayed in real-time during the scan. For a B-scan frame rate of 250 Hz, and an OCT lateral resolution of approximately 20μm , the approach can handle out-of-plane motion at speeds of up to 4 mm/s.
Self-supervised U-Net for segmenting flat and sessile polyps

Colorectal Cancer(CRC) poses a great risk to public health. It is the third most common cause of cancer in the US. Development of colorectal polyps is one of the earliest signs of cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Manual inspection can cause misdetections because polyps vary in color, shape, size and appearance. To this end, Computer-Aided Diagnosis systems(CADx) has been proposed that detect polyps by processing the colonoscopic videos. The system acts a secondary check to help clinicians reduce misdetections so that polyps may be resected before they transform to cancer. Polyps vary in color, shape, size, texture and appearance. As a result, the miss rate of polyps is between 6% and 27% despite the prominence of CADx solutions. Furthermore, sessile and flat polyps which have diameter less than 10 mm are more likely to be undetected. Convolutional Neural Networks(CNN) have shown promising results in polyp segmentation. However, all of these works have a supervised approach and are limited by the size of the dataset. It was observed that smaller datasets reduce the segmentation accuracy of ResUNet++. Self-supervision is a stronger alternative to fully supervised learning especially in medical image analysis since it redresses the limitations posed by small annotated datasets. From the self-supervised approach proposed by Jamaludin et al., it is evident that pretraining a network with a proxy task helps in extracting meaningful representations from the underlying data which can then be used to improve the performance of the final downstream supervised task. In summary, we train a U-Net to inpaint randomly dropped out pixels in the image as a proxy task. The dataset we use for pretraining is Kvasir-SEG dataset. This is followed by a supervised training on the limited Kvasir-Sessile dataset. Our experimental results demonstrate that with limited annotated dataset and a larger unlabeled dataset, self-supervised approach is a better alternative than fully supervised approach. Specifically, our self-supervised U-Net performs better than five segmentation models which were trained in supervised manner on the Kvasir-Sessile dataset.
Enhanced Isotropy Maximization Loss: Seamless and High-Performance Out-of-Distribution Detection Simply Replacing the SoftMax Loss

—Current out-of-distribution detection approaches usually present special requirements (e.g., collecting outlier data and hyperparameter validation) and produce side effects (e.g., classiﬁcation accuracy drop and slow/inefﬁcient inferences). Recently, entropic out-of-distribution detection has been proposed as a seamless approach (i.e., a solution that avoids all previously mentioned drawbacks). The entropic out-of-distribution detection solution uses the IsoMax loss for training and the entropic score for out-of-distribution detection. The IsoMax loss works as a drop-in replacement of the SoftMax loss (i.e., the combination of the output linear layer, the SoftMax activation, and the cross-entropy loss) because swapping the SoftMax loss with the IsoMax loss requires no changes in the model’s architecture or training procedures/hyperparameters. In this paper, we perform what we call an isometrization of the distances used in the IsoMax loss. Additionally, we propose replacing the entropic score with the minimum distance score. Experiments showed that these modiﬁcations signiﬁcantly increase out-of-distribution detection performance while keeping the solution seamless. Besides being competitive with or outperforming all major current approaches, the proposed solution avoids all their current limitations, in addition to being much easier to use because only a simple loss replacement for training the neural network is required. The code to replace the SoftMax loss with the IsoMax+ loss and reproduce the results is available at https://github.com/dlmacedo/entropic-out-of-distribution-detection.
Transposon insertion mutation of Antarctic psychrotrophic fungus for red pigment production adaptive to normal temperature

Abstract Polar regions are rich in microbial and product resources. Geomyces sp. WNF-15A is an Antarctic psy chrotrophic filamentous fungus producing high quality red pigment with potential for industrial use. However, efficient biosynthesis of red pigment can only realize at low temperature, which brings difficult control and high cost for the large-scale fermentation. This study aims to develop transposon insertion mutation method to improve cell growth and red pigment production adaptive to normal temperature. Genetic manipulation system of this fungus was firstly developed by antibiotic marker screening, protoplast preparation and transformation optimization, by which transformation efficiency of ∼50% was finally achieved. Then transposable insertion systems were established using Helitron, Fot1, and Impala transposons. The transposition efficiency reached 11.9%, 9.4%, and 4.6%, respectively. Mutant MP1 achieved the highest red pigment production (OD520 of 39) at 14°C, which was 40% higher than the wild-type strain. Mutant MP14 reached a maximum red pigment production (OD520 of 14.8) at 20°C, which was about twofold of the wild-type strain. Mutants MP2 and MP10 broke the repression mechanism of red pigment biosynthesis in the wild-type and allowed production at 25°C. For cell growth, eight mutants grew remarkably better (12%∼30% biomass higher) than the wild-type at 25°C. This study established an efficient genetic manipulation and transposon insertion mutation platform for polar filamentous fungus. It provides reference for genetic breeding of psychrotrophic fungi from polar and other regions.
Sensitivity of clinician‐assessed efficacy outcome measurement instruments in trials of topical therapies for atopic dermatitis: a systematic review and meta‐analysis

The rising prevalence of atopic dermatitis (AD) in developing countries and its substantial socioeconomic impact have furthered research over the last two decades giving way to advances in its aetiopathogenesis and treatment. Topical therapies targeting newly identified AD signalling pathways are being developed. Numerous clinician‐assessed disease severity outcome measurement instruments (OMIs) are available to evaluate the efficacy of investigational treatments in proof‐of‐concept (POC) trials for AD. However, little is known about the comparative sensitivity of these efficacy OMIs. We performed a systematic review and meta‐analysis to compare the sensitivity of different OMIs in controlled trials of topical therapies for AD published between January 1, 2000 and April 7, 2020. Treatment effect size of OMIs reported at Week 4 was calculated with 95% Confidence Interval (CI). The sensitivity of OMIs was compared by pooling the standardized difference between means (Cohen's d and Cohen's h) for any two OMI‐parameter combinations that were reported in ≥3 studies identified in our systematic review. Assessed parameters were difference between active and vehicle at Week 4 and change from baseline [CFB] and percentage change from baseline [%CFB] at Week 4. We identified a total of 15 studies with 3313 subjects examining 14 different OMIs were included in this quantitative meta‐analysis. Continuous OMIs had a significantly higher treatment effect size vs. dichotomous OMIs (P = 0.006). Comparisons of Eczema Area and Severity Index (EASI), Investigator's Global Assessment (IGA), body surface area (BSA) and SCORing Atopic Dermatitis (SCORAD) for available parameters were performed and generally had a similar sensitivity, with BSA showing smaller overall effect size estimates. In conclusion, continuous OMIs used in topical clinical trials for AD had significantly higher treatment effect sizes when compared to dichotomous OMIs. Continuous OMIs could provide more power for POC trials with a small sample size in atopic dermatitis with topical therapies.
Vitamin D and green tea extracts for the treatment of uterine fibroids in late reproductive life: a pilot, prospective, daily-diary based study

Abstract Objective The beneficial effects of Vitamin D (VD) and Epigallocatechin gallate (EGCG), a polyphenol of green tea, on the growth of uterine fibroids (UF) were previously described in vitro and in vivo. We have decided to investigate their simultaneous administration in women with UFs in late reproductive life. Methods >40 years old n = 16 premenopausal women with intramural (IM) or subserosal (SS) UF of ≥3 cm or several UFs of different sizes, even smaller but with a total diameter ≥3 cm but <10 cm, without further concomitant organic causes of abnormal uterine bleeding, treated with EGCG 300 mg, Vitamin B6 10 mg and VD 50 µg/day for 90 days. Women completed a diary on a daily basis to obtain information about bleeding and pelvic pain. Results We have observed a significant reduction in UF’s mean size both at patient’s (−17.8%, p = .03) and at single UF’s level (−37.3%, p = .015). The effect was more evident in women with predominant IM (p = .016) in comparison to SS UFs. No significant changes were observed for uterine and ovarian volume and endometrial thickness during treatment. We reported a significant decrease in menstrual flow length of 0.9 day (p = .04) with no modification in cycle length, menstrual flow intensity and menstrual pain intensity. The satisfaction with treatment was in general very high, with no adverse effects reported. Conclusion The concomitant administration of VD and EGCG represents a promising treatment of UF in women of late reproductive life for which hormonal manipulation is not foreseen.
Tofacitinib improved peripheral endothelial dysfunction and brain‐derived neurotrophic factor levels in the rat adjuvant‐induced arthritis model

This study aimed to explore the effect of Tofacitinib on endothelial dysfunction and cerebral levels of brain‐derived neurotrophic factor (BDNF) in the adjuvant‐induced arthritis (AIA) rat model. Tofacitinib (10 mg/kg twice a day) or vehicle was administered from the first signs of inflammation. Arthritis scores were daily monitored while other parameters including endothelial function assessed from aortic rings, radiographic scores, blood pressure, heart rate, circulating levels of triglycerides, cholesterol, and interleukin (IL)‐1β, tumor necrosis factor‐α (TNF‐α), IL‐17A, and cerebral BDNF levels were determined after 3 weeks of treatment. A group of non‐AIA rats served as controls. In AIA rats, as compared with vehicle, Tofacitinib significantly reduced arthritis and radiographic scores, decreased total cholesterol and low‐density lipoprotein cholesterol (LDL‐C), but changed neither blood pressure nor heart rate and proinflammatory cytokines levels. It also fully restored acetylcholine (Ach)‐induced relaxation (p < 0.05) through increased nitric oxide (NO) synthase activity, reduced BH4 deficiency and O2−° production, decreased cyclo‐oxygenase‐2 (COX‐2)/arginase activities, and enhanced endothelium‐derived hyperpolarizing factor (EDHF) production. These effects translated into a decrease in atherogenic index and an elevation of BDNF levels in the prefrontal cortex (p < 0.05) and hippocampus (p < 0.001). The present study identified Tofacitinib as an efficient therapeutic option to reduce cardiovascular risk and improve BDNF‐dependent cognition in arthritis.