Miracle-dz/UniFAR-FTUs huggingface dataset:
LGBTQ Persons' Use of Online Spaces to Navigate Conception, Pregnancy, and Pregnancy Loss: An Intersectional Approach

Navigating conception, pregnancy, and loss is challenging for lesbian, gay, bisexual, transgender, and queer (LGBTQ) people, who experience stigma due to LGBTQ identity, other identities (e.g., loss), and intersections thereof. We conducted interviews with 17 LGBTQ people with recent pregnancy loss experiences. Taking LGBTQ identity and loss as a starting point, we used an intracategorical intersectional lens to uncover the benefits and challenges of LGBTQ-specific and non-LGBTQ-specific pregnancy and loss-related online spaces. Participants used LGBTQ-specific online spaces to enact individual, interpersonal, and collective resilience. However, those with multiple marginalized identities (e.g., people of color and non-partnered individuals), faced barriers in finding support within LGBTQ-specific spaces compared to those holding privileged identities (e.g., White and married). Non-LGBTQ spaces were beneficial for some informational needs, but not community and emotional needs due to pervasive heteronormativity, cisnormativity, and a perceived need to educate. We conceptualize experiences of exclusion as symbolic annihilation and intersectional invisibility, and discuss clinical implications and design directions.
A DEEP LEARNING APPROACH TO AUTONOMOUS DRIVING IN URBAN ENVIRONMENT

This paper is dedicated to road object detection in urban environment using Deep Learning Neural Networks (DLNN). We have chosen the advanced architecture YOLOv5 trained on COCO 2017 dataset (containing 80 classes of objects in 1.5 million images) and we have specialized YOLOv5 to recognize objects from BDD100K dataset (Berkeley Diverse Driving Video Database, including 100,000 images). We have kept eight classes from BDD100K: car, truck, person, traffic sign, traffic light, bus, rider and bike. The object detection performance (average precision) obtained in this paper for BDD100K dataset is the highest, compared with previous published approaches for the same dataset, and the highest for the mapping of YOLOv5 and BDD100K dataset. The research results prove that one can apply state of the art cutting edge deep learning technologies to open new perspectives for automotive industry.
Filter-Based Feature Selection Using Information Theory and Binary Cuckoo Optimisation Algorithm

Dimensionality reduction is among the data mining process that is used to reduce the noise and complexity of features in various datasets. Feature selection (FS) is one of the most commonly used dimensionalities that reduces the unwanted features from the datasets. FS can be either wrapper or filter. Wrappers select subsets of the feature with better classification performance but are computationally expensive. On the other hand, filters are computationally fast but lack feature interaction among selected subsets of features which in turn affect the classification performance of the chosen subsets of features. This study proposes two concepts of information theory mutual information (MI). As well as entropy (E). Both were used together with binary cuckoo optimization algorithm BCOA (BCOA-MI and BCOA-EI). The target is to improve classification performance (reduce the error rate and computational complexity) on eight datasets with varying degrees of complexity. A support vector machine classifier was used to measure and computes the error rates of each of the datasets for both BCOA-MI and BCOA-E. The analysis of the results showed that BCOA-E selects a fewer number of features and performed better in terms of error rate. In contrast, BCOA-MI is computationally faster but chooses a larger number of features. Comparison with other methods found in the literature shows that the proposed BCOA-MI and BCOA-E performed better in terms of accuracy, the number of selected features, and execution time in most of the datasets .
Performance of medicinal plants grown under multilayered agroforestry system

The experiment was conducted at the Germplasm Center (GPC), Bangladesh Agricultural University, Mymensingh during December 2008 to November 2009 to investigate the growth and development of some medicinal plants (Aloevera, Asparagus and Misridana) under multistoried agroforestry systems having three light levels i.e. open condition (received 100% of light), guava + lemon based agroforestry system (received 55-60% light) and sissoo + lemon based agroforestry system (received 45-55% light). The experiment was laid out in a randomized complete block design with three replications. Results revealed that the performance of three medicinal plants, Aloevera, Asparagus and Misridana in terms of growth, yield contributing characters and yield was significantly influenced by the multilayered agroforestry systems. Growth and development, yield contributing characters and yield of Aloevera, Asparagus and Misridana were greater in all the agroforestry systems than open condition. However, between two agroforestry systems (guava + lemon and sissoo + lemon based systems), Aloevera and Misridana performed better under guava with lemon agroforestry system than sissoo with lemon agroforestry system in terms of growth, yield contributing characters and yield except leaf dry matter percentage for Aloevera and corms dry matter percentage for Misridana. On the other hand, Asparagus performed better in respect of growth, yield contributing characters and yield under sissoo + lemon based agroforestry system than guava + lemon based agroforestry system.
Assessment of Deformation Properties of Coal Measure Sandstones Through Regression Analyses and Artificial Neural Networks

The deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. in this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus ( E ti , GPa) and tangential Poisson’s Ratio ( v ti ) of coal measure sandstones located in Zonguldak Hardcoal Basin ( ZHB ), nW Turkey. Predictive models were established based on various regression and artificial neural network ( ANN ) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz ( Q , %) and lithic fragment ( LF , %) and the textural features (i.e., average grain size, d 50 , and sorting coefficient, S c ) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the E ti and v ti . The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. it was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones.
Selenium and Silicon Fertilization in Soilless Grown Eggplant

In the study, 10μM Se from sodium selenate (Na2SeO4) and 2.8 mM Si from potassium silicate (K2SiO3), each one alone or both together, were used for nutr ition of greenhouse-soilless grown eggplant. The eggplant yi eld was increased by 16.4% in Se alone, 12.5% in Si alone a nd decreased by 8.2 % in Se+Si together. Silicon effec ts on plant growth were not significant somewhat reducing effec ts observed, however better effects of Se on leaf area , shoot fresh and dry weight were recorded. Selenium and S i were not significantly affected to the eggplant fruit physical properties, except firmness. These two elements esp cially Si increased eggplant fruit firmness. Selenium and Si contents of the fruit were increased by the treatments. This can be important especially for Se nutrition of the human. Leaf nutrients contents, for N, P, K, Ca, Mg, Fe, Mn, Zn, Cu, were adequate; there were no any deficiencies or toxicit ies under the Se and Si doses in this experiment.
Neural Piecewise-Constant Delay Differential Equations

Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neural network, called the Neural Piecewise-Constant Delay Differential Equations (PCDDEs). Here, unlike the recently proposed framework of the Neural Delay Differential Equations (DDEs), we transform the single delay into the piecewise-constant delay(s). The Neural PCDDEs with such a transformation, on one hand, inherit the strength of universal approximating capability in Neural DDEs. On the other hand, the Neural PCDDEs, leveraging the contributions of the information from the multiple previous time steps, further promote the modeling capability without augmenting the network dimension. With such a promotion, we show that the Neural PCDDEs do outperform the several existing continuous-depth neural frameworks on the one-dimensional piecewise-constant delay population dynamics and real-world datasets, including MNIST, CIFAR10, and SVHN.
Adaptive Model Predictive Control of Wheeled Mobile Robots

In this paper, a control algorithm for guiding a two wheeled mobile robot with unknown inertia to a desired point and orientation using an Adaptive Model Predictive Control (AMPC) framework is presented. The two wheeled mobile robot is modeled as a knife edge or a skate with nonholonomic kinematic constraints and the dynamical equations are derived using the Lagrangian approach. The inputs at every time instant are obtained from Model Predictive Control (MPC) with a set of nominal parameters which are updated using a recursive least squares algorithm. The efficacy of the algorithm is demonstrated through numerical simulations at the end of the paper.
S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks

Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra- and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.
Submix: Practical Private Prediction for Large-Scale Language Models

Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim. This is a vulnerability that can compromise the privacy of the model's training data. In this work, we introduce SubMix: a practical protocol for private next-token prediction designed to prevent privacy violations by language models that were fine-tuned on a private corpus after pre-training on a public corpus. We show that SubMix limits the leakage of information that is unique to any individual user in the private corpus via a relaxation of group differentially private prediction. Importantly, SubMix admits a tight, data-dependent privacy accounting mechanism, which allows it to thwart existing data-extraction attacks while maintaining the utility of the language model. SubMix is the first protocol that maintains privacy even when publicly releasing tens of thousands of next-token predictions made by large transformer-based models such as GPT-2.
Neural Myerson Auction for Truthful and Energy-Efficient Autonomous Aerial Data Delivery

A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction-based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of these SPA-based operations, a deep learning-based framework is additionally designed for delivery performance improvements.
Designing for Robustness in Electric Grids via a General Effective Resistance Measure

—We propose a mathematical framework for designing robust networks of coupled phase-oscillators by leveraging a vulnerability measure proposed by Tyloo et. al that quantiﬁes how much a small perturbation to a phase-oscillator’s natural frequency impacts the system’s global synchronized frequencies. Given a ﬁxed complex network topology with speciﬁc governing dynamics, the proposed framework ﬁnds an optimal allocation of edge weights that minimizes such vulnerability measure(s) at the node(s) for which we expect perturbations to occur by solving a tractable semideﬁnite programming problem. We specify the mathematical model to high voltage electric grids where each node corresponds to a voltage phase angle associated with a bus and edges correspond to transmission lines. Edge weights are determined by the susceptance values along the transmission lines. In this application, frequency synchronization is increasingly challenged by the integration of renewable energy, yet is imperative to the grid’s health and functionality. Our framework helps to alleviate this challenge by optimizing the placement of renewable generation and the susceptance values along the transmission lines.
Multiphase critical Higgs boson at colliders

The recently proposed multi-phase criticality principle in Coleman-Weinberg models can provide a new explanation for the hierarchy between the electroweak and new physics scales. When applied to the Standard Model, a Higgs boson as light as the pseudo-Goldstone boson of broken scale invariance occurs. The suppressed mixing between the two light fields still carries information about the large scale of symmetry breaking, albeit up to logarithmic corrections. In this work we probe this scenario with the present LHC data and assess the impact of future lepton and hadron colliders. Our results show that the multi-phase criticality can easily explain the apparent absence of new physics at the energy scales tested in current experiments.
CEMENT: Incomplete Multi-View Weak-Label Learning with Long-Tailed Labels

A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views. In recent years, several methods have been proposed to cope with it and achieved much success, but still suffer from two key problems: 1) lack the ability to deal with the incomplete multi-view weak-label data, in which only a subset of features and labels are provided for each sample; 2) ignore the presence of noisy views and tail labels usually occurring in real-world problems. In this paper, we pro-pose a novel method, named CEMENT, to overcome the limitations. For 1), CEMENT jointly embeds incomplete views and weak labels into distinct low-dimensional subspaces, and then correlates them via Hilbert-Schmidt Independence Criterion (HSIC). For 2), CEMEMT adaptively learns the weights of embeddings to capture noisy views, and explores an additional sparse component to model tail labels, making the low-rankness available in the multi-label setting. We develop an alternating algorithm to solve the proposed optimization problem. Experimental results on seven real-world datasets demonstrate the effectiveness of the proposed method.
Fractal dimension and the counting rule of the Goldstone modes

It is argued that highly degenerate ground states arising from spontaneous symmetry breaking with a type-B Goldstone mode are scale-invariant, with a salient feature that the entanglement entropy $S(n)$ scales logarithmically with the block size $n$ in the thermodynamic limit. As it turns out, the prefactor is half the number of type-B Goldstone modes $N_B$. This is achieved by performing an exact singular value decomposition of the degenerate ground states, thus unveiling their self-similarities - the essence of a fractal. Combining with a field-theoretic prediction [O. A. Castro-Alvaredo and B. Doyon, Phys. Rev. Lett. \textbf{108}, 120401 (2012)], we are led to the identification of the fractal dimension $d_f$ with the number of type-B Goldstone modes $N_B$ for quantum many-body systems undergoing spontaneous symmetry breaking.
Field-induced boson insulating states in a 2D superconducting electron gas with strong spin–orbit scatterings

The phenomenon of field-induced superconductor-to-insulator transitions observed experimentally in electron-doped SrTiO3/LaAlO3 interfaces, analyzed recently by menas of 2D superconducting fluctuations theory, is revisited with new insights associating it with the appearnace at low temperatures of field-induced boson insulating states. Within the framework of the time-dependent Ginzburg–Landau functional approach, we pinpoint the origin of these states in field-induced extreme softening of fluctuation modes over a large region in momentum space, upon diminishing temperature, which drives Cooper-pair fluctuations to condense into mesoscopic puddles in real space. Dynamical quantum tunneling of Cooper-pair fluctuations out of these puddles, introduced within a phenomenological approach, which break into mobile single-electron states, contains the high-field resistance onset predicted by the exclusive boson theory.
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing

In this article, we propose to build up a collaboration between a deep neural network and a human in the loop to swiftly obtain accurate segmentation maps of remote sensing images. In a nutshell, the agent iteratively interacts with the network to correct its initially flawed predictions. Concretely, these interactions are annotations representing the semantic labels. Our methodological contribution is twofold. First, we propose two interactive learning schemes to integrate user inputs into deep neural networks. The first one concatenates the annotations with the other network’s inputs. The second one uses the annotations as a sparse ground truth to retrain the network. Second, we propose an active learning (AL) strategy to guide the user toward the most relevant areas to annotate. To this purpose, we compare different state-of-the-art acquisition functions to evaluate the neural network uncertainty such as ConfidNet, entropy, or ODIN. Through experiments on three remote sensing datasets, we show the effectiveness of the proposed methods. Notably, we show that AL based on uncertainty estimation enables to quickly lead the user toward mistakes and that it is thus relevant to guide the user interventions. Code will be open-source and released in this repository.1
Sparse Non-Convex Optimization For Higher Moment Portfolio Management

One of the reasons that higher order moment portfolio optimization methods are not fully used by practitioners in investment decisions is the complexity that these higher moments create by making the optimization problem nonconvex. Many few methods and theoretical results exists in the literature, but the present paper uses the method of successive convex approximation for the mean-variance-skewness problem.
J an 2 02 2 Continuous-time mean-variance hedging under different loan and deposit rates

This paper investigates a continuous-time mean-variance hedging problem under different loan and deposit rates. The value function is shown to satisfy a fully nonlinear PDE and be of C3,2 smooth by PDE method and verification theorem. We show that there are a borrowing and a saving boundary that divide the whole trading space into three regions: borrowing money region, no-trading region and saving money region. The optimal strategy is a mixture of the continuously trading strategy (as suggested by most continuous-time models) and discontinuously trading strategy (as suggested by models with transaction costs): one should put all her wealth in the stock in the middle no-trading region, and continuously trade the risky asset in the borrowing and saving money regions. Also one should never short sale the stock.
Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

Deep neural networks (DNNs) are threatened by adversarial examples. Adversarial detection, which distinguishes adversarial images from benign images, is fundamental for robust DNN-based services. Image transformation is one of the most effective approaches to detect adversarial examples. During the last few years, a variety of image transformations have been studied and discussed to design reliable adversarial detectors. In this paper, we systematically synthesize the recent progress on adversarial detection via image transformations with a novel classification method. Then, we conduct extensive experiments to test the detection performance of image transformations against state-of-the-art adversarial attacks. Furthermore, we reveal that each individual transformation is not capable of detecting adversarial examples in a robust way, and propose a DNN-based approach referred to as \emph{AdvJudge}, which combines scores of 9 image transformations. Without knowing which individual scores are misleading or not misleading, AdvJudge can make the right judgment, and achieve a significant improvement in detection rate. Finally, we utilize an explainable AI tool to show the contribution of each image transformation to adversarial detection. Experimental results show that the contribution of image transformations to adversarial detection is significantly different, the combination of them can significantly improve the generic detection ability against state-of-the-art adversarial attacks.