Miracle-dz/UniFAR-FTUs huggingface dataset:
Examination of childhood trauma and self-esteem of individuals who applied to the probation office due to substance use

Abstract Substance use is increasing worldwide, and this creates serious social, economic and psychological problems. This study aims to examine the sociodemographic, childhood trauma (CT), self-esteem level and clinical data of individuals diagnosed with substance use disorder (SUD) who applied to the Probation Unit and the control group and it also aims to compare these data. In this prospective cohort study, 73 individuals diagnosed with SUD, and a control group consisting of 73 simple randomly selected healthy individuals. It was determined that the participants in the SUD group had extremely significantly lower self-esteem and had a high level of CT exposure compared to the control group. When all the cases were examined in the study, it was observed that as the level of self-esteem decreased, the rate of exposure of CT was significantly higher. Participants with SUD were more exposed to emotional abuse, physical abuse, physical neglect, and physical abuse compared to control group. It was determined that those with substance use had a high rate of childhood trauma and low self-esteem. In effective fight against substance abuse, it was thought that sociocultural programs should be implemented, and accessibility of these programs should be expanded in order to increase individuals’ self-esteem levels.
Metastatic neuroendocrine tumor masquerading as orbital cysticercosis

Orbital metastases secondary to neuroendocrine tumors are exceedingly rare. We present a unique case of a 30-year-old female initially presenting with fever, chills, periorbital swelling, and painful proptosis. CT orbits revealed two ovoid-shaped ring-enhancing lesions in the right lateral and superior rectus muscles and clear sinuses, atypical for infectious post-septal cellulitis. Further work-up included serologic analysis, auto-immune panel, and MRI. Further imaging showed pseudocystic orbital lesions mimicking orbital cysticercosis. Additionally, given the bilateral nature of the lesions and patient’s country of origin, this parasitic process was highly suspected. A course of albendazole and steroids led to resolution of symptoms. With a presentation at age 30, this is by far the youngest case reported in literature to date.
A New Approach of Linear Theory of Tearing Instability in Uniform Resistivity

The linear perturbation equation of the tearing instability derived in LSC theory (Loureiro, Schekochihin, and Cowley, PoP2007) is numerically examined as an initial value problem, where the inner and outer regions are seamlessly solved under uniform resistivity. Hence, all regions are solved as the resistive MHD (magnetohydrodynamics). To comprehensively study physically acceptable perturbation solutions, the behaviors of the local maximum points required for physically acceptable solutions and zero-crossing points, at which \phi=0 and \psi=0, are examined. Eventually, the uniform resistivity assumed in the outer region is shown to play an important role in improving some conclusions derived from the theory. In conclusion, the upper limit \lambda_{up} of the growth rate obtained in the improved (modified) LSC theory is shown to be regulated by the Alfven speed measured in the outer region. It is also shown to be partially consistent with the growth rate in the linear developing stage of the impulsive tearing instability observed in the compressible MHD simulation of the plasmoid instability (PI) based on uniform resistivity.
Identifying Films with Noir Characteristics Using Audience's Tags on MovieLens

We consider the noir classiﬁcation problem by exploring noir attributes and what ﬁlms are likely to be regarded as noirish from the perspective of a wide Internet audience. We use a dataset consisting of more than 30,000 ﬁlms with relevant tags added by users of MovieLens, a web-based recommendation system. Based on this data, we develop a statistical model to identify ﬁlms with noir characteristics using these free-form tags. After retrieving information for describing ﬁlms from tags, we implement a one-class nearest neighbors algorithm to recognize noirish ﬁlms by learning from IMDb-labeled noirs. Our analysis evidences ﬁlm noirs’ close relationship with German Expressionism, French Poetic Realism, British thrillers, and American pre-code crime pictures, revealing the similarities and diﬀerences between neo noirs after 1960 and noirs in the classic period.
Progressive Fusion for Multimodal Integration

Integration of multimodal information from various sources has been shown to boost the performance of machine learning models and thus has received increased attention in recent years. Often such models use deep modality-specific networks to obtain unimodal features which are combined to obtain"late-fusion"representations. However, these designs run the risk of information loss in the respective unimodal pipelines. On the other hand,"early-fusion"methodologies, which combine features early, suffer from the problems associated with feature heterogeneity and high sample complexity. In this work, we present an iterative representation refinement approach, called Progressive Fusion, which mitigates the issues with late fusion representations. Our model-agnostic technique introduces backward connections that make late stage fused representations available to early layers, improving the expressiveness of the representations at those stages, while retaining the advantages of late fusion designs. We test Progressive Fusion on tasks including affective sentiment detection, multimedia analysis, and time series fusion with different models, demonstrating its versatility. We show that our approach consistently improves performance, for instance attaining a 5% reduction in MSE and 40% improvement in robustness on multimodal time series prediction.
Linear stability analysis of wake vortices by a spectral method using mapped Legendre functions

Abstract A spectral method using associated Legendre functions with algebraic mapping is developed for a linear stability analysis of wake vortices. These functions serve as Galerkin basis functions, capturing correct analyticity and boundary conditions for vortices in an unbounded domain. The incompressible Euler or Navier–Stokes equations linearised on a swirling flow are transformed into a standard matrix eigenvalue problem of toroidal and poloidal streamfunctions, solving perturbation velocity eigenmodes with their complex growth rate as eigenvalues. This reduces the problem size for computation and distributes collocation points adjustably clustered around the vortex core. Based on this method, strong swirling $q$ vortices with linear perturbation wavenumbers of order unity are examined. Without viscosity, neutrally stable eigenmodes associated with the continuous eigenvalue spectrum having critical-layer singularities are successfully resolved. The inviscid critical-layer eigenmodes numerically tend to appear in pairs, implying their singular degeneracy. With viscosity, the spectra pertaining to physical regularisation of critical layers stretch out toward an area, referring to potential eigenmodes with wavepackets found by Mao & Sherwin (J. Fluid Mech., vol. 681, 2011, pp. 1–23). However, the potential eigenmodes exhibit no spatial similarity to the inviscid critical-layer eigenmodes, doubting that they truly represent the viscous remnants of the inviscid critical-layer eigenmodes. Instead, two distinct continuous curves in the numerical spectra are identified for the first time, named the viscous critical-layer spectrum, where the similarity is noticeable. Moreover, the viscous critical-layer eigenmodes are resolved in conformity with the $Re^{-1/3}$ scaling law. The onset of the two curves is believed to be caused by viscosity breaking the singular degeneracy.
Self-Supervised Pretraining for 2D Medical Image Segmentation

Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems. However, the need for copious labelled training data limits the capabilities of these algorithms in scenarios where such input is scarce or expensive. Self-supervised learning offers a way to lower the need for manually annotated data by pretraining models for a specific domain on unlabelled data. In this approach, labelled data are solely required to fine-tune models for downstream tasks. Medical image segmentation is a field where labelling data requires expert knowledge and collecting large labelled datasets is challenging; therefore, self-supervised learning algorithms promise substantial improvements in this field. Despite this, self-supervised learning algorithms are used rarely to pretrain medical image segmentation networks. In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency. We find that self-supervised pretraining on natural images and target-domain-specific images leads to the fastest and most stable downstream convergence. In our experiments on the ACDC cardiac segmentation dataset, this pretraining approach achieves 4-5 times faster fine-tuning convergence compared to an ImageNet pretrained model. We also show that this approach requires less than five epochs of pretraining on domain-specific data to achieve such improvement in the downstream convergence time. Finally, we find that, in low-data scenarios, supervised ImageNet pretraining achieves the best accuracy, requiring less than 100 annotated samples to realise close to minimal error.
Multi-View Reconstruction Using Signed Ray Distance Functions (SRDF)

In this paper, we investigate a new optimization framework for multi-view 3D shape reconstructions. Recent differentiable rendering approaches have provided breakthrough performances with implicit shape representations though they can still lack precision in the estimated geometries. On the other hand multi-view stereo methods can yield pixel wise geometric accuracy with local depth predictions along viewing rays. Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays. The approach retains pixel-accuracy while benefiting from volumetric integration in the optimization. To this aim, depths are optimized by evaluating, at each 3D location within the volumetric discretization, the agreement between the depth prediction consistency and the photometric consistency for the corresponding pixels. The optimization is agnostic to the associated photo-consistency term which can vary from a median-based baseline to more elaborate criteria, e.g. learned functions. Our experiments demonstrate the benefit of the volumetric integration with depth predictions. They also show that our approach outperforms existing approaches over standard 3D benchmarks with better geometry estimations.
Large magnetic anisotropy and magnetostriction in thin films of 
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Spinel Cobalt Vanadate CoV$_2$O$_4$ has been grown on (001) SrTiO$_3$ substrates. Using torque magnetometry experiments, we find that the previously observed temperature induced anisotropy change, where the easy axis changes from the out of plane [001] direction to a biaxial anisotropy with planar<100>easy axes, occurs in a gradual second-order structural phase transition. This work characterizes this transition and the magnetic anisotropies in the (001), (100), and (-110) rotation planes, and explores their field dependence up to 30~T. Below 80~K, hysteretic features appear around the hard axes, i.e., the out-of-plane direction in (-110) and (010) rotations and the planar<110>directions in (001) rotations. This is due to a Zeeman Energy that originates from the lag of the magnetization with respect to the applied magnetic field as the sample is rotated. The appearance of the hysteresis, which persist up to very high fields, shows that the anisotropy at low temperature is rather strong. Additionally, field dependent distortions to the symmetry of the torque response in increasing applied fields shows that magnetostriction plays a large role in determining the direction and magnitude of the anisotropy.
Visual Prompting via Image Inpainting

How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically produce the output image, consistent with the given examples. We show that posing this problem as simple image inpainting - literally just filling in a hole in a concatenated visual prompt image - turns out to be surprisingly effective, provided that the inpainting algorithm has been trained on the right data. We train masked auto-encoders on a new dataset that we curated - 88k unlabeled figures from academic papers sources on Arxiv. We apply visual prompting to these pretrained models and demonstrate results on various downstream image-to-image tasks, including foreground segmentation, single object detection, colorization, edge detection, etc.
New Lower Bounds For Essential Covers Of The Cube

An essential cover of the vertices of the n -cube { 0 , 1 } n by hyperplanes is a minimal covering where no hyperplane is redundant and every variable appears in the equation of at least one hyperplane. Linial and Radhakrishnan gave a construction of an essential cover with (cid:100) n 2 (cid:101) +1 hyperplanes and showed that Ω( √ n ) hyperplanes are required. Recently, Yehuda and Yehudayoﬀ improved the lower bound by showing that any essential cover of the n -cube contains at least Ω( n 0 . 52 ) hyperplanes. In this paper, building on the method of Yehuda and Yehudayoﬀ, we prove that Ω (cid:16) hyperplanes are needed.
Non-Cooperative Resource Management for Intelligent Reflecting Surface Aided Networks

Intelligent reflecting surface (IRS) has emerged as a promising and revolutionizing technology for future wireless networks. Most existing IRS studies focus on simple cooperative systems which usually have a single frequency band. In realistic non-cooperative multi-band networks, however, the existing IRS designs may be not applicable or have severe performance degradation. Thus, in the complex network environment, it is more rational to consider IRSs as public resources to be dynamically allocated to appropriate users. In this paper, we first introduce the auction theory to tackle the resource management problem for a multi-IRS-assisted non-cooperative network. An efficient auction algorithm framework is introduced to sub-optimally solve this non-convex problem. Simulation result illustrates that the significant performance improvement can be achieved by applying the auction algorithm in the complex multi-IRS-assisted non-cooperative network.
An Incremental Learning framework for Large-scale CTR Prediction

In this work we introduce an incremental learning framework for Click-Through-Rate (CTR) prediction and demonstrate its effectiveness for Taboola’s massive-scale recommendation service. Our approach enables rapid capture of emerging trends through warm-starting from previously deployed models and fine tuning on “fresh” data only. Past knowledge is maintained via a teacher-student paradigm, where the teacher acts as a distillation technique, mitigating the catastrophic forgetting phenomenon. Our incremental learning framework enables significantly faster training and deployment cycles (x12 speedup). We demonstrate a consistent Revenue Per Mille (RPM) lift over multiple traffic segments and a significant CTR increase on newly introduced items.
Which anonymization technique is best for which NLP task? - It depends. A Systematic Study on Clinical Text Processing

Clinical text processing has gained more and more attention in recent years. The access to sensitive patient data, on the other hand, is still a big challenge, as text cannot be shared without legal hurdles and without removing personal information. There are many techniques to modify or remove patient related information, each with different strengths. This paper investigates the inﬂuence of different anonymization techniques on the performance of ML models using multiple datasets corresponding to ﬁve different NLP tasks. Several learnings and recommendations are presented. This work conﬁrms that particularly stronger anonymization techniques lead to a signiﬁcant drop of performance. In addition to that, most of the presented techniques are not secure against a re-identiﬁcation attack based on similarity search.
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations

Equilibrium propagation (EP) is an alternative to backpropagation (BP) that allows the training of deep neural networks with local learning rules. It thus provides a compelling framework for training neuromorphic systems and understanding learning in neurobiology. However, EP requires infinitesimal teaching signals, thereby limiting its applicability in noisy physical systems. Moreover, the algorithm requires separate temporal phases and has not been applied to large-scale problems. Here we address these issues by extending EP to holomorphic networks. We show analytically that this extension naturally leads to exact gradients even for finite-amplitude teaching signals. Importantly, the gradient can be computed as the first Fourier coefficient from finite neuronal activity oscillations in continuous time without requiring separate phases. Further, we demonstrate in numerical simulations that our approach permits robust estimation of gradients in the presence of noise and that deeper models benefit from the finite teaching signals. Finally, we establish the first benchmark for EP on the ImageNet 32x32 dataset and show that it matches the performance of an equivalent network trained with BP. Our work provides analytical insights that enable scaling EP to large-scale problems and establishes a formal framework for how oscillations could support learning in biological and neuromorphic systems.
Visibility Representations of Toroidal and Klein-bottle Graphs

In this paper, we study visibility representations of graphs that are embedded on a torus or a Klein bottle. Mohar and Rosenstiehl showed that any toroidal graph has a visibility representation on a ﬂat torus bounded by a parallelogram, but left open the question whether one can assume a rectangular ﬂat torus, i.e., a ﬂat torus bounded by a rectangle. Independently the same question was asked by Tamassia and Tollis. We answer this question in the positive. With the same technique, we can also show that any graph embedded on a Klein bottle has a visibility representation on the rectangular ﬂat Klein bottle.
The infrared emission bands. I - Correlation studies and the dependence on C/O ratio. [in planetary and reflection nebulae and H II regions]

Airborne measurements obtained for the unidentified IR (UIR) 5-8 micron emission bands of eight planetaries, eight locations in five reflection nebulae, and seven locations in four H II regions (including the Orion Bar), are presently compared with existing and new ground-based observations of the 3.3, 8.7, and 11.3 micron bands. The good correlations found between the strengths of all pairs of bands lead to the conclusion that all seven UIR features form a 'generic spectrum', although there are significant variations in the relative strengths of the features among the sources. The fraction of total far-IR luminosity radiated by a planetary in the strongest UIR feature at 7.7 microns is strongly correlated with the nebular C/O ratio, strongly suggesting that hydrocarbons are the carriers of these features.
Secrecy Analysis for IRS-aided Wiretap MIMO Communications: Fundamental Limits and System Design

In order to meet the demands of future innovative applications, many efforts have been made to exceed the limits predicted by Shannon's Theory. Besides the investigation of beyond-Shannon metrics such as security, latency, and semantics, another direction is to jointly design the transceiver and the environment by utilizing the intelligent reflecting surface (IRS). In this paper, we consider the analysis and design of IRS-aided multiple-input multiple-output (MIMO) secure communications, which has attracted much research attention but still in its infancy. For example, despite their importance, the fundamental limits of IRS-aided wiretap MIMO communications are not yet available in the literature. In this paper, we will investigate these fundamental limits by determining the ergodic secrecy rate (ESR) and secrecy outage probability (SOP). For that purpose, the central limit theorem (CLT) for the joint distributions of the mutual information (MI) statistics over the IRS-aided MIMO secure communication channel is derived by utilizing the random matrix theory (RMT). The derived CLT is then used to obtain the closed form expressions for the ESR and SOP, which are also extended to the scenario with multiple multi-antenna eavesdroppers. Based on the theoretical results, algorithms for maximizing the artificial noise (AN) aided ESR and minimizing the SOP are proposed. Numerical results validate the accuracy of the theoretical results and effectiveness of the proposed optimization algorithms.
Fair learning with Wasserstein barycenters for non-decomposable performance measures

This work provides several fundamental characterizations of the optimal classification function under the demographic parity constraint. In the awareness framework, akin to the classical unconstrained classification case, we show that maximizing accuracy under this fairness constraint is equivalent to solving a corresponding regression problem followed by thresholding at level $1/2$. We extend this result to linear-fractional classification measures (e.g., ${\rm F}$-score, AM measure, balanced accuracy, etc.), highlighting the fundamental role played by the regression problem in this framework. Our results leverage recently developed connection between the demographic parity constraint and the multi-marginal optimal transport formulation. Informally, our result shows that the transition between the unconstrained problems and the fair one is achieved by replacing the conditional expectation of the label by the solution of the fair regression problem. Finally, leveraging our analysis, we demonstrate an equivalence between the awareness and the unawareness setups in the case of two sensitive groups.