Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent stereoselective interactions and self-recognition in polar chiral active ellipsoids

Experiments on 3D-printed chiral active ellipsoids now uncover the dynamical analogs of stereoselectivity and self-recognition. In many active matter systems, particle trajectories have a well-defined handedness or chirality. Whether such chiral activity can introduce stereoselective interactions between particles is not known. Here, we developed a strategy to tune the nature of chiral activity of three-dimensionally printed granular ellipsoids without altering their shape or size. In vertically agitated monolayers of these particles, we observed two types of dimers form depending on the chirality of the pairing monomers. Heterochiral dimers moved collectively as a single achiral active unit, while homochiral ones formed a translationally immobile spinner. In active racemic mixtures, the former was more abundant than the latter, indicating that interactions were stereoselective. Through dimer lifetime measurements, we further provide evidence for chiral self-recognition in mixtures of particles with different chiral activities. We lastly show that, at fixed particle number density, changing the net chirality of a dense active liquid fundamentally alters the nature of collective relaxation.
Identification of Two Major Genes Plus Polygenes Mixed Inheritance Model of Quantitative Traits in B_1 and B_2, and F_2

Both QTL mapping and the segregation analysis indicated that major gene plus polygene mixed model needs to be extended from one major gene to two or more major genes. Fortunately, the iterated ECM (IECM) algorithm makes it possible. In the present paper, Akaike's Information Criterion are employed to identify the exlstence of major genes affecting quantitative traits. Meanwhile, IECM algorithm is used to obtain the maximum likelihood estimates of parameters of component distributions, as well as the first order or second order genetic parameters. From P_1, P_2, F_1, B_1 and B_2 or P_1, P_2, F_1 and F_2, the likelihood ratio test is used to test the exlstence of polygenes. An example of the inheritance of rice plant height is provided.
SURF-SVM Based Identification and Classification of Gastrointestinal Diseases in Wireless Capsule Endoscopy

Endoscopy provides a major contribution to the diagnosis of the Gastrointestinal Tract (GIT) diseases. With Colon Endoscopy having its certain limitations, Wireless Capsule Endoscopy is gradually taking over it in the terms of ease and efficiency. WCE is performed with a miniature optical endoscope which is swallowed by the patient and transmits colour images wirelessly during its journey through the GIT, inside the body of the patient. These images are used to implement an effective and computationally efficient approach which aims to detect the abnormal and normal tissues in the GIT automatically, and thus helps in reducing the manual work of the reviewers. The algorithm further aims to classify the diseased tissues into various GIT diseases that are commonly known to be affecting the tract. In this manuscript, the descriptor used for the detection of the interest points is Speeded Up Robust Features (SURF), which uses the colour information contained in the images which is converted to CIELAB space colours for better identification. The features extracted at the interest points are then used to train and test a Support Vector Machine (SVM), so that it automatically classifies the images into normal or abnormal and further detects the specific abnormalities. SVM, along with a few parameters, gives a very high accuracy of 94.58% while classifying normal and abnormal images and an accuracy of 82.91% while classifying into multi-class. The present work is an improvement on the previously reported analyses which were only limited to the bi-class classification using this approach.
Discriminating qubit amplitude damping channels

We address the issue of the discrimination between two-qubit amplitude damping channels by exploring several strategies. For the single-shot, we show that the excited state does not always give the optimal input, and that side entanglement assistance has limited benefit. On the contrary, feedback assistance from the environment is more beneficial. For the two-shot, we prove the in-utility of entangled inputs. Then focusing on individual (local) measurements, we find the optimal adaptive strategy.
Flow-based Detection and Proxy-based Evasion of Encrypted Malware C2 Traffic

State of the art deep learning techniques are known to be vulnerable to evasion attacks where an adversarial sample is generated from a malign sample and misclassified as benign. Detection of encrypted malware command and control traffic based on TCP/IP flow features can be framed as a learning task and is thus vulnerable to evasion attacks. However, unlike e.g. in image processing where generated adversarial samples can be directly mapped to images, going from flow features to actual TCP/IP packets requires crafting the sequence of packets, with no established approach for such crafting and a limitation on the set of modifiable features that such crafting allows.In this paper we discuss learning and evasion consequences of the gap between generated and crafted adversarial samples. We exemplify with a deep neural network detector trained on a public C2 traffic dataset, white-box adversarial learning, and a proxy-based approach for crafting longer flows. Our results show 1) the high evasion rate obtained by using generated adversarial samples on the detector can be significantly reduced when using crafted adversarial samples; 2) robustness against adversarial samples by model hardening varies according to the crafting approach and corresponding set of modifiable features that the attack allows for; 3) incrementally training hardened models with adversarial samples can produce a level playing field where no detector is best against all attacks and no attack is best against all detectors, in a given set of attacks and detectors. To the best of our knowledge this is the first time that level playing field feature set- and iteration-hardening are analyzed in encrypted C2 malware traffic detection.
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation

Today's scene graph generation (SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling the class imbalance trouble of SGG is critical and challenging. In this paper, we first discover that when predicate labels have strong correlation with each other, prevalent re-balancing strategies (e.g., re-sampling and re-weighting) will give rise to either over-fitting the tail data (e.g., bench sitting on sidewalk rather than on), or still suffering the adverse effect from the original uneven distribution (e.g., aggregating varied parked on/standing on/sitting on into on). We argue the principal reason is that re-balancing strategies are sensitive to the frequencies of predicates yet blind to their relatedness, which may play a more important role to promote the learning of predicate features. Therefore, we propose a novel Predicate-Correlation Perception Learning (PCPL for short) scheme to adaptively seek out appropriate loss weights by directly perceiving and utilizing the correlation among predicate classes. Moreover, our PCPL framework is further equipped with a graph encoder module to better extract context features. Extensive experiments on the benchmark VG150 dataset show that the proposed PCPL performs markedly better on tail classes while well-preserving the performance on head ones, which significantly outperforms previous state-of-the-art methods.
Deep Generative Model for Image Inpainting With Local Binary Pattern Learning and Spatial Attention

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based image inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially in the boundary and highly textured regions. To tackle this challenge, in this work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model through combining a local binary pattern (LBP) learning network with an actual inpainting network. Specifically, the first LBP learning network using U-Net architecture is designed to accurately predict the structural information of the missing region, which subsequently guides the second image inpainting network for better filling the missing pixels. Furthermore, an improved spatial attention mechanism is integrated into the image inpainting network, by considering the consistency not only between the known region with the generated one, but also within the generated region itself. Extensive experiments on public datasets including CelebA-HQ, Places and Paris StreetView demonstrate that our model generates better inpainting results than the state-of-the-art competing algorithms, both quantitatively and qualitatively. The source code and trained models are available at https://github.com/HighwayWu/ImageInpainting.
Transform Quantization for CNN Compression

In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization. Previous CNN quantization techniques tend to ignore the joint statistics of weights and activations, producing sub-optimal CNN performance at a given quantization bit-rate, or consider their joint statistics during training only and do not facilitate efficient compression of already trained CNN models. We optimally transform (decorrelate) and quantize the weights post-training using a rate–distortion framework to improve compression at any given quantization bit-rate. Transform quantization unifies quantization and dimensionality reduction (decorrelation) techniques in a single framework to facilitate low bit-rate compression of CNNs and efficient inference in the transform domain. We first introduce a theory of rate and distortion for CNN quantization and pose optimum quantization as a rate–distortion optimization problem. We then show that this problem can be solved using optimal bit-depth allocation following decorrelation by the optimal End-to-end Learned Transform (ELT) we derive in this paper. Experiments demonstrate that transform quantization advances the state of the art in CNN compression in both retrained and non-retrained quantization scenarios. In particular, we find that transform quantization with retraining is able to compress CNN models such as AlexNet, ResNet and DenseNet to very low bit-rates (1–2 bits).
Spectator dark matter in nonstandard cosmologies

It has been shown that the observed dark matter (DM) abundance can be produced by amplification of quantum fluctuations of an energetically subdominant scalar field during inflation. In this paper, we study the robustness of this "spectator dark matter" scenario to changes in the expansion rate of the early Universe. Compared to the standard radiation-dominated (RD) scenario, two aspects will change: the DM energy density evolves differently as a function of time, and also the DM isocurvature perturbation spectrum will be different from the result in the RD case. These can impose sizeable changes to the values of model parameters which allow the field to constitute all DM while simultaneously satisfying all observational constraints. We study both free and self-interacting DM in scenarios with non-standard expansion and quantify the changes to the cases with a standard cosmological history. We also discuss testability of the scenario through primordial DM isocurvature and non-Gaussianity.
Overcoming Negative Transfer: A Survey

Transfer learning (TL) tries to utilize data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., the source domain data/knowledge cause reduced learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches to overcome NT have been proposed in the literature. However, there has not been a systematic survey on overcoming NT. This paper fills the gap, by categorizing and reviewing near 100 approaches for combating NT, from four perspectives: source data quality, target data quality, domain divergence, and integrated algorithms. NT in related fields, e.g., multi-task learning, multilingual models, and lifelong learning, is also discussed.
Phosphorus Starvation-induced Expression of Leaf Acid Phosphatase Isoforms in Soybean

Leaf acid phosphatase (APase) activities of 274 soybean genotypes were surveyed under fieldconditions with two levels of P supplies, and a nutrient solution culture experiment with eight selectedgenotypes was subsequently conducted under greenhouse conditions to further characterize APase ac-tivity and its isoform expression induced by P starvation. Results from the field experiment showed thatthere was a great genotypic variation for leaf APase activity among the tested soybean genotypes fromdifferent origins, and APase activity in many of the tested genotypes (about 60%) was generally increasedin the treatment without P fertilizer addition. Results from the nutrient solution culture experimentshowed that APase activity in all the eight tested genotypes was generally enhanced by P starvation. Sixisoforms of APases were detected in isoelectric focusing gels with samples from both young and oldleaves. The activity of all the six isoforms was increased by P starvation, but no new APase isoform wasinduced. Our results suggest that leaf APase activity could serve as an enzymatic indicator of P starvationfor soybean; the increase in leaf APase activity under low P stress was mainly caused by the increase inthe activity of existing isoforms but not by the induction of new isoforms.
Enhancing Associative Memory Recall and Storage Capacity Using Confocal Cavity QED

We introduce a near-term experimental platform for realizing an associative memory. It can simultaneously store many memories by using spinful bosons coupled to a degenerate multimode optical cavity. The associative memory is realized by a confocal cavity QED neural network, with the cavity modes serving as the synapses, connecting a network of superradiant atomic spin ensembles, which serve as the neurons. Memories are encoded in the connectivity matrix between the spins, and can be accessed through the input and output of patterns of light. Each aspect of the scheme is based on recently demonstrated technology using a confocal cavity and Bose-condensed atoms. Our scheme has two conceptually novel elements. First, it introduces a new form of random spin system that interpolates between a ferromagnetic and a spin-glass regime as a physical parameter is tuned---the positions of ensembles within the cavity. Second, and more importantly, the spins relax via deterministic steepest-descent dynamics, rather than Glauber dynamics. We show that this nonequilibrium quantum-optical scheme has significant advantages for associative memory over Glauber dynamics: These dynamics can enhance the network's ability to store and recall memories beyond that of the standard Hopfield model. Surprisingly, the cavity QED dynamics can retrieve memories even when the system is in the spin glass phase. Thus, the experimental platform provides a novel physical instantiation of associative memories and spin glasses as well as provides an unusual form of relaxational dynamics that is conducive to memory recall even in regimes where it was thought to be impossible.
The Strategy of Body Size in Mammalian Carnivores

Techniques of statistical analysis are utilized to begin to unravel the causes involved in the evolution of body size clines in mammalian carnivores. Variables included are productivity, temperature, latitude, competitive pressure and size of potential prey. Temperature and latitude are shown to be good predictors of body size in several species. Indeed, they seem to be measures of the same cause, as has long been maintained by those attempting to explain Bergmann's rule. But temperature does no better than latitude at predicting size. Thus temperature is probably not a direct cause of Bergmann's rule in these animals. The measure of total primary productivity used (actual annual evapotranspiration, AE) proved to be one of the better corre- lates, especially at lower productivities. Apparently relatively meager food supplies set upper limits to, body size. Except in a very few cases, the biotic variables of competitive pressure and prey size were only second-rate predictors. Discussion of the patterns that have been shown by this analysis is included.
Non-Asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectories

This letter considers the problem of linear time-invariant (LTI) system identification using input/output data. Recent work has provided non-asymptotic results on partially observed LTI system identification using a single trajectory but is only suitable for stable systems. We provide finite-time analysis for learning Markov parameters based on the ordinary least-squares (OLS) estimator using multiple trajectories, which covers both stable and unstable systems. For unstable systems, our results suggest that the Markov parameters are harder to estimate in the presence of process noise. Without process noise, our upper bound on the estimation error is independent of the spectral radius of system dynamics with high probability. These two features are different from fully observed LTI systems for which recent work has shown that unstable systems with a bigger spectral radius are easier to estimate. Extensive numerical experiments demonstrate the performance of our OLS estimator.
INVESTIGATION ON SEED MYCOFLORA OF GREENGRAM [Vigna radiata (L.) Wilczek] AND THEIR MANAGEMENT

“INVESTIGATION ON SEED MYCOFLORA OF GREENGRAM [Vigna radiata (L.) Wilczek] AND THEIR MANAGEMENT” Name of Student Major Advisor Shri Deshmukh A. J. Dr. A. N. Sabalpara DEPARTMENT OF PLANT PATHOLOGY N.M. COLLEGE OF AGRICULTURE NAVSARI AGRICULTURAL UNIVERSITY NAVSARI-396 450
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models

We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To ensure solvability of simpler tasks, TMNs learn the textual input-output behavior (i.e., language) of existing models through their datasets. This differs from prior decomposition-based approaches which, besides being designed specifically for each complex task, produce decompositions independent of existing sub-models. Specifically, we focus on Question Answering (QA) and show how to train a next-question generator to sequentially produce sub-questions targeting appropriate sub-models, without additional human annotation. These sub-questions and answers provide a faithful natural language explanation of the model’s reasoning. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator. Our experiments show that ModularQA is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work.
On extra-zeros of p-adic Rankin-Selberg L-functions

We prove a version of the Extra-zero conjecture formulated by the first named author for p-adic L-functions associated to Rankin-Selberg convolutions of modular forms of the same weight. The novelty of this result is to provide strong evidence in support of this conjecture in the non-critical case, which remained essentially unstudied.
Memory systems of the brain

Humans have long been fascinated by how memories are formed, how they can be damaged or lost, or still seem vibrant after many years. Thus the search for the locus and organization of memory has had a long history, in which the notion that is is composed of distinct systems developed during the second half of the 20th century.A fundamental dichotomy between conscious and unconscious memory processes was first drawn based on evidences from the study of amnesiac subjects and the systematic experimental work with animals. The use of behavioral and neural measures together with imaging techniques have progressively led researchers to agree in the existence of a variety of neural architectures that support multiple memory systems.This article presents a historical lens with which to contextualize these idea on memory systems, and provides a current account for the multiple memory systems model.
Regulation of Non-Photochemical Quenching of Chlorophyll Fluorescence in Plants

Non-photochemical quenching of chlorophyll fluorescence indicates the de-excitation of light-generated excited states in the chlorophyll associated with photosystem II (PSII). The principle process contributing to this quenching is dependent on the formation of the thylakoid proton gradient and is an important mechanism for protecting PSII from photodamage. Evidence points to the importance of the light-harvesting chlorophyll proteins as the site of dissipation of energy, and suggests that the structure and function of these proteins are regulated by protonation and the ratio of zeaxanthin to violaxanthin. The minor light-harvesting proteins may have a particularly important role as the primary sites of proton binding and because of their enrichment in xanthophyll cycle carotenoids. The dynamic nature of the light-harvesting system is an important part of the process by which plants are able to adapt to different light environments.
Assessing Patterns of Morphological Variation of Quercus spp. in Mixed-oak Communities

Cluster analyses and principal components analyses were used to evaluate phenetic relationships among Quercus coccinea, Q. falcata, Q marilandica and Q velutina in three mixed-oak communities in the Land Between the Lakes (Kentucky and Tennessee). Two sets of quantitative characters, taken from (1) leaves and (2) fruits and buds, were analyzed for each community. In addition, all OTUs were combined for analyses of the two data sets as well as for an analysis based on all characters. The results support the hypothesis that hybridization is occurring between Q. velutina and Q. coccinea and/or Q. marilandica. Although the leaf and fruit/bud data sets tend to yield the same patterns of overall similarity, there are distinct differences between complementary phenograms in each community. While all analyses indicate that Q. coccinea and Q. velutina are more similar to each other than to either of the other two taxa, the leaf and fruit/bud data sets do not agree on the phenetic similarities between Q falcata, Q. marilandica, and the Q. coccinea-Q. velutina cluster. The patterns illustrated by these analyses provide insight into the characters emphasized in earlier hypotheses of taxonomic and/or evolutionary relationships among these species.