Miracle-dz/UniFAR-FTUs huggingface dataset:
Residual effect of amoxicillin in broiler

This study was designed with the aim to determine the impact of residual antibiotics on poultry muscle (breast and thigh), kidney, spleen, fat & liver and effect of discriminate and indiscriminate use of antibiotic on haematological constituents in broiler chickens. The chickens were properly reared and on the day of 16, chicks were randomly divided into three groups namely control group (group A), discriminate antibiotic group (group B), indiscriminate antibiotic group (group c). Each group consists six chicks. In case of discriminate group, the chicks were treated with amoxicillin antibiotic followed by maintaining withdrawal period one week before the sacrifice of the birds. On the other hand indiscriminate group of chicks was continued without maintaining any withdrawal period and birds were sacrificed for sampling. TLC method was used for screening detection of amoxicillin residues in poultry tissues and hematological parameters were investigated with appropriate methods such as hemoglobin determination, packed cell volume and total erythrocyte count. In case of control group (group A): No positive sample found. In case of discriminate group (group B): the residues of amoxicillin were 100% in livers, 66.67% in kidneys, 0% in thigh muscles, and 0% in breast muscles, 33.34% in fat and 33.34% in spleen. In case of indiscriminate group (group c): the residues of amoxicillin were 100% in livers, 100% in kidneys, 16.67% in thigh muscles, and 16.67% in breast muscles, 50% in fat and 50% in spleen. Among the poultry tissues, liver and kidney had the highest level of antibiotic residues in comparison to other samples. It was observed that indiscriminate use of antibiotic represent the antibiotic residues in different edible tissues. Evidence suggests that more judicious use of antimicrobials in food animals will reduce the selection of resistant bacteria and help to preserve these valuable drugs for both human and veterinary medicine. National authorities should adopt a proactive approach that promotes programs aimed at reducing the need for antimicrobials in food animals and ensuring their prudent use. Further investigation is required for the quantitative determination of antibiotic residues in food product.
Performance analysis of machine learning algorithms in breast cancer diagnosis

— Breast cancer is recognized as one of the foremost causes of death among women in worldwide with more than one million of cases and nearly 600,000 deaths each year. It is extremely important to identify it at the early stage in considerably to increase the chances of survival. The breast cancer can be classified into two types of tumors which are benign and malignant. Benign tumors are undangerous tumors where they develop slowly in organ while malignant are dangerous tumors where they would spread to the other organ of body. In this paper, five machine learning algorithms are used to predict if the tumor is benign or malignant based on the Wisconsin Prognostic Breast Cancer dataset, while one of the algorithms is modified to achieve a better performance. The five algorithms used are Gaussian Naïve Bayes Classifier, Random Forest Classifier, Decision Tree Classifier, Kernel Support Vector Machine Classifier, and K-Nearest Neighbors Classifier while the modified algorithm is Kernel Support Vector Machine Classifier. The aim is to use Machine Learning algorithms to make prediction of breast cancer and improved the accuracy of the algorithm. 10-fold Cross Validation is implemented after compared it with Bootstrapping as a resampling method as it is more efficient. At the end, the comparison in results shown that the modified Kernel Support Vector Machine Classifier predicted the highest accuracy among these five machine learning models.
Magnetic field sustained by the elastic force in neutron star crusts

We investigate the magneto–elastic equilibrium of a neutron star crust and magnetic energy stored by the elastic force. The solenoidal motion driven by the Lorentz force can be controlled by the magnetic elastic force, so that conditions for the magnetic field strength and geometry are less restrictive. For equilibrium models, the minor solenoidal part of the magnetic force is balanced by a weak elastic force because the irrotational part is balanced by the dominant gravity and pressure forces. Therefore, a strong magnetic field may be confined in the interior, regardless of poloidal or toroidal components. We numerically calculated axially symmetric models with the maximum shear–strain, and found that a magnetic energy > 10 erg can be stored in the crust, even for a normal surface dipole-field-strength (< 10 G). The magnetic energy much exceeds the elastic energy (10 − 10 erg). The shear–stress spatial distribution revealed that the elastic structure is likely to break down near the surface. In particular, the critical position is highly localized at a depth less than 100 m from the surface.
Incremental Object Grounding Using Scene Graphs

Object grounding tasks aim to locate the target object in an image through verbal communications. Understanding human command is an important process needed for effective human-robot communication. However, this is challenging because human commands can be ambiguous and erroneous. This paper aims to disambiguate the human’s referring expressions by allowing the agent to ask relevant questions based on semantic data obtained from scene graphs. We test if our agent can use relations between objects from a scene graph to ask semantically relevant questions that can disambiguate the original user command. In this paper, we present Incremental Grounding using Scene Graphs (IGSG), a disambiguation model that uses semantic data from an image scene graph and linguistic structures from a language scene graph to ground objects based on human command. Compared to the baseline, IGSG shows promising results in complex real-world scenes where there are multiple identical target objects. IGSG can effectively disambiguate ambiguous or wrong referring expressions by asking disambiguating questions back to the user.
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.
Nonclassicality criteria for 
N
-dimensional optical fields detected by quadratic detectors

Non-classicality criteria for general N-dimensional optical fields are derived. They involve intensity moments, the probabilities of photon-number distributions or combinations of both. The Hillery criteria for the sums of the probabilities of even or odd photon numbers are generalized to N-dimensional fields. As an example, the derived non-classicality criteria are applied to an experimental 3-mode optical field containing two types of photon-pair contributions. The accompanying non-classicality depths are used to mutually compare their performance.
ConTrip: Consensus Sentiment review Analysis and Platform ratings in a single score

People unequivocally employ reviews to decide on purchasing an item or an experience on the internet. In that regard, the growing significance and number of opinions have led to the development of methods to assess their sentiment content automatically. However, it is not straightforward for the models to create a consensus value that embodies the agreement of the different reviews and differentiates across equal ratings for an item. Based on the approach proposed by Nguyen et al. [8], we derive a novel consensus value named ConTrip that merges their consensus score and the overall rating of a platform for an item. ConTrip lies in the rating range values, which makes it more interpretable while maintaining the ability to differentiate across equally rated experiences. ConTrip is implemented and freely available under MIT license at github.com/pepebonet/contripscore
Generating asymmetric aberration laser beams with controlled intensity distribution

We present generation of asymmetric aberration laser beams (aALBs) with controlled intensity distribution, using a diffractive optical element (DOE) involving phase asymmetry. The asymmetry in the phase distribution is introduced by shifting the coordinates in a complex plane. The results show that autofocusing properties of aALBs remain invariant with respect to the asymmetry parameters. However, a controlled variation in the phase asymmetry allows to control the spatial intensity distribution of aALBs. In an ideal ALB containing equal intensity bright lobes, by introducing asymmetry most of the intensity can be transferred to any one of single bright lobe, and forms a high-power density lobe. For a given beam parameter m, the precise spatial position of high-power density lobe can be controlled by the asymmetry parameter β, and we have determined the empirical relations for them. We have found that for the specific values of β, the intensity in the high-power density lobe can be enhanced by several times the intensity in other lobes. The experimental results show a good agreement with the numerical simulations. The findings can be suitable for applications such as in optical trapping and manipulation as well as material processing.
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets

Social Media usage has increased to an all-time high level in today's digital world. The majority of the population uses social media tools (like Twitter, Facebook, YouTube, etc.) to share their thoughts and experiences with the community. Analysing the sentiments and opinions of the common public is very important for both the government and the business people. This is the reason behind the activeness of many media agencies during the election time for performing various kinds of opinion polls. In this paper, we have worked towards analysing the sentiments of the people of India during the Lok Sabha election of 2019 using the Twitter data of that duration. We have built an automatic tweet analyser using the Transfer Learning technique to handle the unsupervised nature of this problem. We have used the Linear Support Vector Classifiers method in our Machine Learning model, also, the Term Frequency Inverse Document Frequency (TF-IDF) methodology for handling the textual data of tweets. Further, we have increased the capability of the model to address the sarcastic tweets posted by some of the users, which has not been yet considered by the researchers in this domain.
An overview of the QCD phase diagram at finite $T$ and $\mu$

In recent years there has been much progress on the investigation of the QCD phase diagram with lattice QCD. This talk will focus on the developments in the last few years. Especially the addition of external influences and extended ranges of $T$ and $\mu$ yield an increasing number of interesting results, a subset of which will be discussed. Many of these conditions are important for the understanding of both the QCD transition in the early universe and heavy ion collision experiments which are conducted for example at the LHC and RHIC. This offers many exciting opportunities for comparisons between theory and experiment.
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions

Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootstrapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. Alternatively, the value function can be updated toward a learning target constructed by separately predicting successor features (SF)—a policy-dependent model—and linearly combining them with instantaneous rewards. We focus on bootstrapping targets used when estimating value functions, and propose a new backup target, the ? -return mixture, which implicitly combines value-predictive knowledge (used by TD methods) with (successor) feature-predictive knowledge—with a parameter ? capturing how much to rely on each. We illustrate that incorporating predictive knowledge through an ? -discounted SF model makes more efficient use of sampled experience, compared to either extreme, i.e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models. We empirically show this approach leads to faster policy evaluation and better control performance, for tabular and nonlinear function approximations, indicating scalability and generality.
Inferring the Intermediate-mass Black Hole Number Density from Gravitational-wave Lensing Statistics

The population properties of intermediate-mass black holes remain largely unknown, and understanding their distribution could provide a missing link in the formation of supermassive black holes and galaxies. Gravitational-wave observations can help fill in the gap from stellar mass black holes to supermassive black holes with masses between ∼100–104 M ⊙. In our work, we propose a new method for examining lens populations through lensing statistics of gravitational waves, here focusing on inferring the number density of intermediate-mass black holes through hierarchical Bayesian inference. Simulating ∼200 lensed gravitational-wave signals, we find that existing gravitational-wave observatories at their design sensitivity could either constrain the number density of 106 Mpc−3 within a factor of 10, or place an upper bound of ≲104 Mpc−3 if the true number density is 103 Mpc−3. More broadly, our method leaves room for incorporation of additional lens populations, providing a general framework for probing the population properties of lenses in the universe.
Mean-Field Limits in Statistical Dynamics

These lectures notes are aimed at introducing the reader to some recent mathematical tools and results for the mean-field limit in statistical dynamics. As a warm-up, lecture 1 reviews the approach to the mean-field limit in classical mechanics following the ideas of W. Braun, K. Hepp and R.L. Dobrushin, based on the notions of phase space empirical measures, Klimontovich solutions and Monge-Kantorovich-Wasserstein distances between probability measures. Lecture 2 discusses an analogue of the notion of Klimontovich solution in quantum dynamics, and explains how this notion appears in Pickl’s method to handle the case of interaction potentials with a Coulomb type singularity at the origin. Finally, lecture 3 explains how the mean-field and the classical limits can be taken jointly on quantum N-particle dynamics, leading to the Vlasov equation. These lectures are based on a series of joint works with C. Mouhot and T. Paul. Introduction: What is a Mean-Field Dynamics? Consider a system of N perfectly identical point particles, subject to pairwise interactions. We shall be concerned with the case where N ≫ 1. For instance, in the case of an ideal gas, a volume of 22.4 ⋅10−3m3 contains NA ≃ 6.02 ⋅10 (Avogrado’s constant) gas molecules at the atmospheric pressure 101.3kPa at the temperature of 0○C = 273.1K. The evolution of such a system can be described either (a) by the system of motion equations (Newton’s second law of motion) satisfied by each particle, or (b) by the motion equation for the “typical particle” driven by its collective interaction with all the other particles. The description of such a large particle system following the procedure outlined in (b) is usually referred to as the “mean-field approximation” for the N -particle dynamics. The advantages and drawbacks of each description can be summarized as follows: (a) is perfect in theory, but unfeasible in practice, since it involves observing initial data in a phase space of very high dimension (6N components for the positions and momenta in the case N point particles in the three-dimensional Euclidean space), not to mention the resolution of a coupled system of 6N differential equations; (b) is only an approximation, but is set on a phase space of relatively low (at least fixed and independent of N) dimension, specifically 6 instead of 6N . This obviously suggests the mathematical problem of justifying rigorously the mean-field approximation (b) starting from (a) as a first principle. Obviously, it would be desirable to obtain a convergence rate as the particle number N →∞, in order to have an idea of the precision of the mean-field description. There are numerous examples of mean-field equations in physics, such as
Efficiently Disentangle Causal Representations

This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional probabilities in original and new distributions. We approximate the difference with models' generalization abilities so that it fits in the standard machine learning framework and can be efficiently computed. In contrast to the state-of-the-art approach, which relies on the learner's adaptation speed to new distribution, the proposed approach only requires evaluating the model's generalization ability. We provide a theoretical explanation for the advantage of the proposed method, and our experiments show that the proposed technique is 1.9--11.0$\times$ more sample efficient and 9.4--32.4 times quicker than the previous method on various tasks. The source code is available at \url{https://github.com/yuanpeng16/EDCR}.
Multi-Label Classification on Remote-Sensing Images

Acquiring information on large areas on the earth's surface through satellite cameras allows us to see much more than we can see while standing on the ground. This assists us in detecting and monitoring the physical characteristics of an area like land-use patterns, atmospheric conditions, forest cover, and many unlisted aspects. The obtained images not only keep track of continuous natural phenomena but are also crucial in tackling the global challenge of severe deforestation. Among which Amazon basin accounts for the largest share every year. Proper data analysis would help limit detrimental effects on the ecosystem and biodiversity with a sustainable healthy atmosphere. This report aims to label the satellite image chips of the Amazon rainforest with atmospheric and various classes of land cover or land use through different machine learning and superior deep learning models. Evaluation is done based on the F2 metric, while for loss function, we have both sigmoid cross-entropy as well as softmax cross-entropy. Images are fed indirectly to the machine learning classifiers after only features are extracted using pre-trained ImageNet architectures. Whereas for deep learning models, ensembles of fine-tuned ImageNet pre-trained models are used via transfer learning. Our best score was achieved so far with the F2 metric is 0.927.
Liouville domains from Okounkov bodies

Given a strictly concave rational PL function $\phi$ on a complete $n$-dimensional fan $\Sigma$, we construct an exact symplectic structure of finite volume on $(\mathbb{C}^\times)^n$ and a family of functions $H_{\phi,\epsilon}$ called polyhedral Hamiltonians. We prove that for each $\epsilon$ the one-periodic orbits of $H_{\phi,\epsilon}$ come in families corresponding to finitely many primitive lattice points of $\Sigma$ and determine their topology. When $\phi$ is negative on the rays of $\Sigma$, we show that the level sets of polyhedral Hamiltonians are hypersurfaces of contact type. As a byproduct, this construction provides a dynamical model for the singularities of toric varieties obtained as degenerations of Fano manifolds in any dimension via Okounkov bodies.
Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?

Statistical language models on source code have successfully assisted software engineering tasks. However, developers can create or pick arbitrary identifiers when writing source code. Freely chosen identifiers lead to the notorious out-of-vocabulary (OOV) problem that negatively affects model performance. Recently, Karampatsis et al. showed that using the Byte Pair Encoding (BPE) algorithm to address the OOV problem can improve the language models' predictive performance on source code. However, a drawback of BPE is that it cannot split the identifiers in a way that preserves the meaningful semantics. Prior researchers also show that splitting compound identifiers into sub-words that reflect the semantics can benefit software development tools. These two facts motivate us to explore whether identifier splitting techniques can be utilized to augment the BPE algorithm and boost the performance of open-vocabulary language models considered in Karampatsis et al.'s work. This paper proposes to split identifiers in both constructing vocabulary and processing model inputs procedures, thus exploiting three different settings of applying identifier splitting to language models for the code completion task. We contrast models' performance under these settings and find that simply inserting identifier splitting into the pipeline hurts the model performance, while a hybrid strategy combining identifier splitting and the BPE algorithm can outperform the original open-vocabulary models on predicting identifiers by 3.68% of recall and 6.32% of Mean Reciprocal Rank. The results also show that the hybrid strategy can improve the entropy of language models by 2.02%.
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling

Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content, e.g., by assigning a semantic label to every pixel of the image. With the popularization of data-driven methods, the past decades have witnessed promising progress on ASP by approaching the problem with the schemes of tile-level scene classification or segmentation-based image analysis, when using high-resolution aerial images. However, the former scheme often produces results with tile-wise boundaries, while the latter one needs to handle the complex modeling process from pixels to semantics, which often requires large-scale and well-annotated image samples with pixel-wise semantic labels. In this paper, we address these issues in ASP, with perspectives from tile-level scene classification to pixel-wise semantic labeling. Specifically, we first revisit aerial image interpretation by a literature review. We then present a large-scale scene classification dataset that contains one million aerial images termed Million-AID. With the presented dataset, we also report benchmarking experiments using classical convolutional neural networks (CNNs). Finally, we perform ASP by unifying the tile-level scene classification and object-based image analysis to achieve pixel-wise semantic labeling. Intensive experiments show that Million-AID is a challenging yet useful dataset, which can serve as a benchmark for evaluating newly developed algorithms. When transferring knowledge from Million-AID, fine-tuning CNN models pretrained on Million-AID perform consistently better than those pretrained ImageNet for aerial scene classification. Moreover, our designed hierarchical multi-task learning method achieves the state-of-the-art pixel-wise classification on the challenging GID, bridging the tile-level scene classification toward pixel-wise semantic labeling for aerial image interpretation.