Miracle-dz/UniFAR-FTUs huggingface dataset:
Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays

Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection.
A Deep Transfer Learning Approach to an Effective Classification of Water Pollutants From Voltammetric Characterizations

This paper proposes a deep transfer learning approach based on convolutional neural networks to the detection of organic pollutants in water, starting from the results of electrochemical characterizations based on voltammetry. The approach is based on a deep transfer learning based on convolutional neural network. The results shown here refer to the detection of Hydroquinone and Benzoquinone by using voltammetry characterization results obtained from different screen printed platforms. The proposed approach provides a significant robustness against the input data heterogeneity and a highly reliable detection performance.
A Method Based on Wavelets for Band Structure Analysis of Phononic Crystals

In this paper, a numerical method based on the wavelet theory is developed for calculating band structures of 2D phononic crystals consisting of general anisotropic materials. After systematical consideration of the appropriate choice of wavelets, two types of wavelets, the Haar wavelet and Biorthogonal wavelet, are selected. Combined with the supercell technique, the developed method can be then applied to compute the band structures of phononic crystals with point or line defects. We illustrate the advantages of the method both mathematically and numerically. Particularly some representative numerical examples are presented for various material combinations (solid-solid, solid-fluid and fluid-fluid) with complex lattice structures to show the accuracy, fast convergence and wide applicability of the method.
Efficient Representation of Large-Alphabet Probability Distributions via Arcsinh-Compander

A number of engineering and scientific problems require representing and manipulating probability distributions over large alphabets, which we may think of as long vectors of reals summing to 1. In some cases it is required to represent such a vector with only b bits per entry. A natural choice is to partition the interval [0,1] into 2b uniform bins and quantize entries to each bin independently. We show that a minor modification of this procedure – applying an entrywise non-linear function (compander) f(x) prior to quantization – yields an extremely effective quantization method. For example, for b = 8(16) and 105-sized alphabets, the quality of representation improves from a loss (under KL divergence) of 0.5(0.1) bits/entry to 10−4(10−9) bits/entry. Compared to floating point representations, our compander method improves the loss from 10−1(10−6) to 10−4(10−9) bits/entry. These numbers hold for both real-world data (word frequencies in books and DNA k-mer counts) and for synthetic randomly generated distributions. Theoretically, we set up a minimax optimality criterion and show that the compander $f(x) \propto \operatorname{ArcSinh} (\sqrt {(1/2)(K\log K)x} )$ achieves near-optimal performance, attaining a KL-quantization loss of ≍ 2−2b log2 K for a K-letter alphabet and b →∞. Interestingly, a similar minimax criterion for the quadratic loss on the hypercube shows optimality of the standard uniform quantizer. This suggests that the ArcSinh quantizer is as fundamental for KL-distortion as the uniform quantizer for quadratic distortion.
The NOAO Deep Wide-Field Survey

The NOAO Deep Wide-Field Survey is a deep optical and IR (B W RIJHK) imaging survey of 18 t u of the sky with the primary goal of studying the evolution of large-scale structure from z 1;4. The images should also enable investigation of the formation and evolution of the red-envelope galaxy population and the detection of luminous, very distant (z > 4), star-forming galaxies and quasars. We are mapping an unprecedentedly large area to faint ux limits (B W RI 26 AB mag. and J H= 2 1 K = 2 1 :4 A B m a g. 5-detection limits in a 2 00 diameter aperture). This survey will also be valuable in addressing many other astrophysical problems, and we h a ve designed its execution in a manner that attempts to maximize the scientiic return when it becomes a publicly available database. 1. Survey Goals We h a ve just completed the rst two y ears of observations for a deep wide-eld optical and IR imaging survey designed to investigate the extent, nature, and evolution of large scale structure at redshifts z 1 ; 44 to identify luminous z > 4 star-forming galaxiess and to nd the cosmologically important z > 1 r e d-envelope galaxy population (e.g., old ellipticals and dusty protogalaxies). While there are tantalizing hints of small-scale clustering in the z > 2:5 starburst population (e.g., Steidel et al. 1996), the detection of large-scale structures at z > 1 will provide unique constraints on hierarchical clustering theories and galaxy formation models. In addition, there is growing evidence that massive elliptical galaxies form rst at z > 4 (in apparent c o n tradiction to hierarchical scenarios) and are already old (>4 Gyr) at z > 1:5, providing a strong lower bound to the age of the Universe (e.g., Dunlop et al. 1996, Spinrad et al. 1997, Francis et al. 1997). Our survey will probe the evolution of the large scale clustering properties of the z > 1 galaxy population, is designed (with an IR component) to identify the z 1 luminous red galaxy population, and will trace the evolution of luminous star-forming galaxies to z 5. The survey occupies a unique region of parameter space (Figure 1), providing both optical and IR coverage over a large area and to great depth in the optical bands. In addition to meeting our primary scientiic goals, the calibrated …
Random Walk: A Modern Introduction

Random walks are stochastic processes formed by successive summation of independent, identically distributed random variables and are one of the most studied topics in probability theory. This contemporary introduction evolved from courses taught at Cornell University and the University of Chicago by the first author, who is one of the most highly regarded researchers in the field of stochastic processes. This text meets the need for a modern reference to the detailed properties of an important class of random walks on the integer lattice. It is suitable for probabilists, mathematicians working in related fields, and for researchers in other disciplines who use random walks in modeling.
An affine property of the reciprocal Asian option process

This note describes a property of the Asian option process, which neatly links the process atμ with the one at−μ, whereμ is the drift of the geometric Brownian motion. The proof is based on (i) a known result due to Yor, on the law of the Asian option process taken at an exponential time, and (ii) a recent result on beta and gamma distributions. Suppose is one-dimensional standard Brownian motion starting at the origin, and define what this author calls the Asian option process , for want of a better name:
Evaluating the Gilbert-Varshamov Bound for Constrained Systems

We revisit the well-known Gilbert-Varshamov (GV) bound for constrained systems. In 1991, Kolesnik and Krachkovsky showed that GV bound can be determined via the solution of some optimization problem. Later, Marcus and Roth (1992) modified the optimization problem and improved the GV bound in many instances. In this work, we provide explicit numerical procedures to solve these two optimization problems and hence, compute the bounds. We then show the procedures can be further simplified when we plot the respective curves.
Edinburgh Research Explorer Clonal and atypical Toxoplasma strain differences in virulence varies with mouse sub-species

The severe virulence of Toxoplasma gondii in classical laboratory inbred mouse strains contradicts the hypothesis that house mice ( Mus musculus ) are the most important intermediate hosts for its transmission and evolution because death of the mouse before parasite transmission equals death of the parasite. However, the classical laboratory inbred mouse strains ( Mus musculus domesticus ), commonly used to test Toxoplasma strain differences in virulence, do not capture the genetic diversity within Mus musculus . Thus, it is possible that Toxoplasma strains that are severely virulent in laboratory inbred mice are avirulent in some other mouse sub-species. Here, we present insight into the responses of individual mouse strains, representing strains of the genetically divergent M us musculus musculus , Mus musculus castaneus and Mus musculus domesticus , to infection with individual clonal and atypical Toxoplasma strains. We observed that, unlike M. m. domesticus , M. m. musculus and M. m. castaneus are resistant to the clonal Toxoplasma strains. For M. m. musculus , we show that this is due to a locus on chromosome 11 that includes the genes that encode the interferon gamma (IFNG)-inducible immunity-related GTPases (Irgs) that can kill the parasite by localizing and subsequently vesiculating the parasitophorous vacuole membrane (PVM). However, despite the localization of known effector Irgs to the Toxoplasma PVM, we observed that some atypical Toxoplasma strains are virulent in all the mouse strains tested. The virulence of these atypical strains in M. m. musculus could not be attributed to individual rhoptry protein 5 (ROP5) alleles, a secreted parasite pseudokinase that antagonizes the canonical effector Irgs and is indispensable for parasite virulence in laboratory inbred mice ( M. m. domesticus ). We conclude that murine resistance to Toxoplasma is modulated by complex interactions between host and parasite genotypes and may be independent of known effector Irgs on murine chromosome 11.
Characterizing the role of SLC3A2 in the molecular landscape and immune microenvironment across human tumors

Background: Inducing ferroptosis in human tumors has become a potential strategy to improve the prognosis of patients, even in those with chemotherapeutic resistance. The xCT complex is a major target for ferroptosis induction, constituted by SLC7A11 and SLC3A2. The role of SLC7A11 in cancer has been widely studied in recent years. However, related research studies for its partner SLC3A2 are still rare. Methods: Bulk transcriptome, single-cell sequencing, and immunohistochemical staining were analyzed to explore the expression distribution of SLC3A2. Clinical outcomes were referred to uncover the relationship between SLC3A2 expression and patients’ prognosis. Immune cell infiltration was estimated by multiple deconvolution algorithms. The effect of SLC3A2 on the proliferation and drug resistance of cancer cell lines was evaluated by DEPMAP. Results: Upregulated SLC3A2 may have an adverse effect on the survival of multiple cancers such as lower-grade glioma and acute myeloid leukemia. SLC3A2 expression is indispensable for multiple cell lines’ proliferation, especially for ESO51 (a cell line for esophageal cancer). In addition, SLC3A2 expression level was related to the remodeling of the immune microenvironment in cancers and some immune checkpoints such as PD-1 and PD-L1, which were potential therapeutic targets in many distinct cancers. Conclusion: Our study systematically elucidated the role of SLC3A2 in the survival of cancer patients and the potential immunotherapeutic response. Few molecular mechanisms by which SLC3A2 regulates anti-tumor immunity have been clarified in the present study, which is the main limitation. Future research into the biological mechanism could further help with targeted treatment for cancer patients.
Peak frequency of the sensorimotor mu rhythm varies with autism-spectrum traits

Autism spectrum disorder (ASD) is a neurodevelopmental syndrome characterized by impairments in social perception and communication. Growing evidence suggests that the relationship between deficits in social perception and ASD may extend into the neurotypical population. In electroencephalography (EEG), high autism-spectrum traits in both ASD and neurotypical samples are associated with changes to the mu rhythm, an alpha-band (8–12 Hz) oscillation measured over sensorimotor cortex which typically shows reductions in spectral power during both one’s own movements and observation of others’ actions. This mu suppression is thought to reflect integration of perceptual and motor representations for understanding of others’ mental states, which may be disrupted in individuals with autism-spectrum traits. However, because spectral power is usually quantified at the group level, it has limited usefulness for characterizing individual variation in the mu rhythm, particularly with respect to autism-spectrum traits. Instead, individual peak frequency may provide a better measure of mu rhythm variability across participants. Previous developmental studies have linked ASD to slowing of individual peak frequency in the alpha band, or peak alpha frequency (PAF), predominantly associated with selective attention. Yet individual variability in the peak mu frequency (PMF) remains largely unexplored, particularly with respect to autism-spectrum traits. Here we quantified peak frequency of occipitoparietal alpha and sensorimotor mu rhythms across neurotypical individuals as a function of autism-spectrum traits. High-density 128-channel EEG data were collected from 60 participants while they completed two tasks previously reported to reliably index the sensorimotor mu rhythm: motor execution (bimanual finger tapping) and action observation (viewing of whole-body human movements). We found that individual measurement in the peak oscillatory frequency of the mu rhythm was highly reliable within participants, was not driven by resting vs. task states, and showed good correlation across action execution and observation tasks. Within our neurotypical sample, higher autism-spectrum traits were associated with slowing of the PMF, as predicted. This effect was not likely explained by volume conduction of the occipitoparietal PAF associated with attention. Together, these data support individual peak oscillatory alpha-band frequency as a correlate of autism-spectrum traits, warranting further research with larger samples and clinical populations.
Regulation of cocaine seeking behavior by locus coeruleus noradrenergic activity in the ventral tegmental area is time- and contingency-dependent

Substance use disorder is linked to impairments in the ventral tegmental area (VTA) dopamine (DA) reward system. Noradrenergic (NA) inputs from locus coeruleus (LC) into VTA have been shown to modulate VTA neuronal activity, and are implicated in psychostimulant effects. Phasic LC activity controls time- and context-sensitive processes: decision making, cognitive flexibility, motivation and attention. However, it is not yet known how such temporally-distinct LC activity contributes to cocaine seeking. In a previous study we demonstrated that pharmacological inhibition of NA signaling in VTA specifically attenuates cocaine-seeking. Here, we used virally-delivered opsins to target LC neurons for inhibition or excitation, delivered onto afferents in VTA of male rats seeking cocaine under extinction conditions. Optogenetic stimulation or inhibition was delivered in distinct conditions: upon active lever press, contingently with discreet cues; or non-contingently, i.e., throughout the cocaine seeking session. Non-contingent inhibition of LC noradrenergic terminals in VTA attenuated cocaine seeking under extinction conditions. In contrast, contingent inhibition increased, while contingent stimulation reduced cocaine seeking. These findings were specific for cocaine, but not natural reward (food) seeking. Our results show that NA release in VTA drives behavior depending on timing and contingency between stimuli – context, discreet conditioned cues and reinforcer availability. We show that, depending on those factors, noradrenergic signaling in VTA has opposing roles, either driving CS-induced drug seeking, or contributing to behavioral flexibility and thus extinction.
TomatoDet: Anchor-free detector for tomato detection

The accurate and robust detection of fruits in the greenhouse is a critical step of automatic robot harvesting. However, the complicated environmental conditions such as uneven illumination, leaves or branches occlusion, and overlap between fruits make it difficult to develop a robust fruit detection system and hinders the step of commercial application of harvesting robots. In this study, we propose an improved anchor-free detector called TomatoDet to deal with the above challenges. First, an attention mechanism is incorporated into the CenterNet backbone to improve the feature expression ability. Then, a circle representation is introduced to optimize the detector to make it more suitable for our specific detection task. This new representation can not only reduce the degree of freedom for shape fitting, but also simplifies the regression process from detected keypoints. The experimental results showed that the proposed TomatoDet outperformed other state-of-the-art detectors in respect of tomato detection. The F1 score and average precision of TomatoDet reaches 95.03 and 98.16%. In addition, the proposed detector performs robustly under the condition of illumination variation and occlusion, which shows great promise in tomato detection in the greenhouse.
Overexpression of an NF-YC2 gene confers alkali tolerance to transgenic alfalfa (Medicago sativa L.)

Alkaline stress severely limits plant growth and yield worldwide. NF-YC transcription factors (TFs) respond to abiotic stress by activating gene expression. However, the biological function of NF-YC TFs in alfalfa (Medicago sativa L.) is not clear. In our study, an NF-YC2 gene was identified and transgenic plants were obtained by constructing overexpression vector and cotyledon node transformation system in alfalfa. The open reading frame of MsNF-YC2 is 879 bp with 32.4 kDa molecular mass. MsNF-YC2 showed tissue expression specificity and was induced by a variety of abiotic stresses including drought, salt, and alkali stress in alfalfa. Under alkali stress treatment, transgenic plants exhibited higher levels of antioxidant enzyme activities and proline (Pro), correlating with a lower levels of hydrogen peroxide (H2O2), superoxide anion (O2–) compared with wild-type (WT) plants. Transcriptomic results showed that overexpression of MsNF-YC2 regulated the expression of phytohormone signal transduction and photosynthesis-related genes under normal and alkaline stress treatments. These results suggest that the MsNF-YC2 gene plays crucial role enhance alkali adaptation abilities in alfalfa.
Future projections of extreme temperature events in Southwest China using nine models in CMIP6

Southwest China, which is close to the Qinghai-Tibet Plateau, presents complex topography. As a result of the combined influence of the South Asian monsoon, East Asian monsoon, and plateau monsoon, climate in this region is unique. Since Southwest China is one of the areas where extreme weather events occur more frequently, this region is sensitive and vulnerable to climate change. In the present research, daily temperature from 1969 to 2020 recorded at 93 weather stations in Southwest China, and data from nine models for the period 1995 to 2040 were used in CMIP6 (Coupled Model Intercomparison Project Phase 6) to calculate 17 ETIs (Extreme Temperature Indices). Furthermore, we analyzed and compared the annual change rate, temporal and spatial change trend, and mean change of extreme temperature events in Southwest China and four subzones during historical period and under SSP2-4.5 scenario for the next 20 years. The results showed: 1) The 8 ECTIs (Extreme Cold Temperature Indices) and the 8 EWTIs (Extreme Warm Temperature Indices) in 1969–2020 were corroborated. These results indicated a warming trend. Also, DTR (Diurnal temperature range) showed a decreasing trend, and different degrees of warming were observed in the four subzones. 2) From 2021 to 2040 and under the SSP2-4.5 scenario, the annual rates of change for 17 ETIs in Southwest China showed that extreme cold events will continue to decrease. On the other hand, extreme warm events will continue to increase. 3) Under the SSP2-4.5 scenario in the next 20 years, CSDI (cold spell duration indicator) will decrease, while WSDI (warm spell duration indicator), TMINmean (average daily minimum temperature), and TMAXmean (average daily maximum temperature) will increase. Moreover, the decrease in amplitude of CSDI was smaller than the increase in amplitude of WSDI. Also, the increase in amplitude of TMINmean was slightly smaller than that of TMAXmean. The projected WSDI, TMINmean, and TMAXmean obtained with the preferred three models and MEM-9 (nine-Model Ensemble Mean) showed an overall growing trend with respect to space; however, the increased range fluctuated in different regions. 4) In 2021–2040, mean values of 4 ETIs in different subzones indicated that the lowest TMINmean and TMAXmean were observed in the ZP (Zoigê Plateau), the highest in the YGP (Yunnan-Guizhou Plateau), and intermediate higher in the SB (Sichuan Basin). Compared with the 4 ETI mean values corresponding to 1969–2020, the persistence and average state of extreme cold and warm events in different subzones showed that future change trends depend on altitude.