Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical CoMorbidity according to ICD-10 Criteria among Patients with Depressive Disorder

OBJECTIVE: To detect the prevalence of physical co-morbidities according to International Classification of Diseases(ICD-10) among patients with Depressive disorder. DESIGN: Descriptive cross sectional study. SETTING: Psychiatric consultation clinic of JPMC Karachi. METHODS: Patients of either gender and age >18 years were consecutively selected after informed consent, Ethical issues were addressed according to Institutional Review Board (IRB), JPMC, Karachi. Patients with psychotic symptoms, personality disorders, dementia, and history of substance use were excluded. ICD-10 criteria, beside complete history, physical examination & lab investigations were used to confirm the diagnosis of depression and physical co-morbid. Prescribed questionnaire was used to collect the data and analyzed in SPSS version-17. Descriptive statistics were calculated for age, gender, marital status, education, socioeconomic status and co-morbidity. RESULTS: Total 100 cases fulfilled the inclusion criteria. Their Mean ±SD age was 32.7 ±16.7 years. Both genders were equally vulnerable, (50% each). Fifty three percent had moderate, 31% mild and 16% had severe depressive disorder. Hypertension was the most common (18%) in male patients & sinusitis was the most common (28%) in female patients with depression. Other co-morbidities included Diabetes Mellitus, Epilepsy, Migraine, HCV, Anemia, Chronic Kidney Disease (CKD), Urinary Tract Infection (UTI) & Menopausal syndrome (in women), Obesity, Asthma and Facial pain. CONCLUSION: Physical co-morbidities are prevalent among patients with depressive disorder. Presence of physical co-morbid may decreases the chances of treatment compliance, increases risk of treatment failure, relapse of depression, worse prognosis and elevated the treatment cost. Early detection and simultaneous management of these co-morbid with depression are suggested.
Experimental investigation of uranium concentration, radium content and radon exhalation rates in food crops consumed in Babil governorate, Iraq

Background : This study focuses on the uranium concentration (UC), radium content (RC) and radon exhalation rates (RER) in selected food crops consumed in Babil governorate, in the centre of Iraq. Materials and Methods: Neutron activation technique and sealed cup with CR - 39 alpha track detector were used respectively to investigate the natural radioactivity of selected food crops collected from Babil governorate, Iraq. Results: In the current study, the highest UC was found to be 0.0346 ppm in the turnip crop, whereas the lowest value of UC (0.0142 ppm) was found in grape crop. The highest RC was found to be 0.651 Bq/kg in turnip, while the lowest RC (0.169 Bq/kg) was found in the fig crop. The values of RER ranged from 0.016 Bq/m 2 . h found in the fig to 0.065 Bq/m 2 . h found in the turnip. Conclusion: The levels of UC in food crops were lower than 1.7 ppm, the recommended limits of UNSCEAR. A strong direct correlation was found between the UC and RC in selected food crops.
Learning Contextually Fused Audio-Visual Representations For Audio-Visual Speech Recognition

With the advance in self-supervised learning for audio and visual modalities, it has become possible to learn a robust audio-visual speech representation. This would be beneficial for improving the audio-visual speech recognition (AVSR) performance, as the multi-modal inputs contain more fruitful information in principle. In this paper, based on existing self-supervised representation learning methods for audio modality, we therefore propose an audio-visual representation learning approach. The proposed approach explores both the complementarity of audio-visual modalities and long-term context dependency using a transformer-based fusion module and a flexible masking strategy. After pre-training, the model is able to extract fused representations required by AVSR. Without loss of generality, it can be applied to single-modal tasks, e.g., audio/visual speech recognition by simply masking out one modality in the fusion module. The proposed pre-trained model is evaluated on speech recognition and lipreading tasks using one or two modalities, where the superiority is revealed.
Synthetically Controlled Bandits

This paper presents a new dynamic approach to experiment design in settings where, due to interference or other concerns, experimental units are coarse. `Region-split' experiments on online platforms are one example of such a setting. The cost, or regret, of experimentation is a natural concern here. Our new design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes the regret associated with experimentation at no practically meaningful loss to inferential ability. We provide theoretical guarantees characterizing the near-optimal regret of our approach, and the error rates achieved by the corresponding treatment effect estimator. Experiments on synthetic and real world data highlight the merits of our approach relative to both fixed and `switchback' designs common to such experimental settings.
An Automated Analysis Framework for Trajectory Datasets

Trajectory datasets of road users have become more important in the last years for safety validation of highly automated vehicles. Several naturalistic trajectory datasets with each more than 10.000 tracks were released and others will follow. Considering this amount of data, it is necessary to be able to compare these datasets in-depth with ease to get an overview. By now, the datasets' own provided information is mainly limited to meta-data and qualitative descriptions which are mostly not consistent with other datasets. This is insufficient for users to differentiate the emerging datasets for application-specific selection. Therefore, an automated analysis framework is proposed in this work. Starting with analyzing individual tracks, fourteen elementary characteristics, so-called detection types, are derived and used as the base of this framework. To describe each traffic scenario precisely, the detections are subdivided into common metrics, clustering methods and anomaly detection. Those are combined using a modular approach. The detections are composed into new scores to describe three defined attributes of each track data quantitatively: interaction, anomaly and relevance. These three scores are calculated hierarchically for different abstract layers to provide an overview not just between datasets but also for tracks, spatial regions and individual situations. So, an objective comparison between datasets can be realized. Furthermore, it can help to get a deeper understanding of the recorded infrastructure and its effect on road user behavior. To test the validity of the framework, a study is conducted to compare the scores with human perception. Additionally, several datasets are compared.
Constant-weight PIR: Single-round Keyword PIR via Constant-weight Equality Operators

Equality operators are an essential building block in tasks over secure computation such as private information retrieval. In private information retrieval (PIR), a user queries a database such that the server does not learn which element is queried. In this work, we propose \emph{equality operators for constant-weight codewords}. A constant-weight code is a collection of codewords that share the same Hamming weight. Constant-weight equality operators have a multiplicative depth that depends only on the Hamming weight of the code, not the bit-length of the elements. In our experiments, we show how these equality operators are up to 10 times faster than existing equality operators. Furthermore, we propose PIR using the constant-weight equality operator or \emph{constant-weight PIR}, which is a PIR protocol using an approach previously deemed impractical. We show that for private retrieval of large, streaming data, constant-weight PIR has a smaller communication complexity and lower runtime compared to SEALPIR and MulPIR, respectively, which are two state-of-the-art solutions for PIR. Moreover, we show how constant-weight PIR can be extended to keyword PIR. In keyword PIR, the desired element is retrieved by a unique identifier pertaining to the sought item, e.g., the name of a file. Previous solutions to keyword PIR require one or multiple rounds of communication to reduce the problem to normal PIR. We show that constant-weight PIR is the first practical single-round solution to single-server keyword PIR.
Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimization

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security.
ASC me to Do Anything: Multi-task Training for Embodied AI

Embodied AI has seen steady progress across a diverse set of independent tasks. While these varied tasks have different end goals, the basic skills required to complete them successfully overlap significantly. In this paper, our goal is to leverage these shared skills to learn to perform multiple tasks jointly. We propose Atomic Skill Completion (ASC), an approach for multi-task training for Embodied AI, where a set of atomic skills shared across multiple tasks are composed together to perform the tasks. The key to the success of this approach is a pre-training scheme that decouples learning of the skills from the high-level tasks making joint training effective. We use ASC to train agents within the AI2-THOR environment to perform four interactive tasks jointly and find it to be remarkably effective. In a multi-task setting, ASC improves success rates by a factor of 2x on Seen scenes and 4x on Unseen scenes compared to no pre-training. Importantly, ASC enables us to train a multi-task agent that has a 52% higher Success Rate than training 4 independent single task agents. Finally, our hierarchical agents are more interpretable than traditional black-box architectures.
Investigating the Genomic Background of CRISPR-Cas Genomes for CRISPR-Based Antimicrobials

CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against foreign genetic elements. Genetic templates acquired during past infection events enable DNA-interacting enzymes to recognize foreign DNA for destruction. Due to the programmability and specificity of these genetic templates, CRISPR-Cas systems are potential alternative antibiotics that can be engineered to self-target antimicrobial resistance genes on the chromosome or plasmid. However, several fundamental questions remain to repurpose these tools against drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting, antimicrobial resistance genes and functional CRISPR-Cas systems have to co-occur in the target cell. Furthermore, these tools have to outplay DNA repair pathways that respond to the nuclease activities of Cas proteins, even for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the average number of these genes varies greatly by the CRISPR-Cas type, and some CRISPR-Cas types (IE and IIIA) have over 20 genes per genome. Next, we investigate the DNA repair pathways of these CRISPR-Cas genomes, revealing that the diversity and frequency of these pathways differ by the CRISPR-Cas type. The interplay between CRISPR-Cas systems and DNA repair pathways is essential for the acquisition of new spacers in CRISPR arrays. We conduct simulation studies to demonstrate that the efficiency of these DNA repair pathways may be inferred from the time-series patterns in the RNA structure of CRISPR repeats. This bioinformatic survey of CRISPR-Cas genomes elucidates the necessity to consider multifaceted interactions between different genes and systems, to design effective CRISPR-based antimicrobials that can specifically target drug-resistant bacteria in natural microbial communities.
Development of Combined CAA-CFD Algorithm for the Efficient Simulation of Aerodynamic Noise Generation and Propagation

The purpose of this work is to develop efficient method for the prediction of aerodynamic noise generation and propagation in low Mach number flows in which dipole source, originating from unsteady pressure fluctuation on a solid surface, is known to be more efficient than quadrupole sources. This method is based on flow noise solvers using acoustic governing equations forced with immersed dipole source terms. The dipole source terms consist of point body forces immersed on each grid point on the solid surface. Their magnitudes are set by unsteady hydrodynamic pressure data previously calculated by incompressible Computational Fluid Dynamics Techniques. For comparison, modeled quadrupole source terms are also considered. These aeroacoustic solvers combined with CFD flow solver is applied to two-dimensional cylinder flow, which generate Karman vortex streets and resultant Aeolian tones. The properties and characteristics of sound generation process on and around the cylinder and of sound propagation through the background flows are analyzed in some detail.
Estimating risks of option books using neural-SDE market models

In this paper, we examine the capacity of an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying. We subsequently demonstrate its use as a risk simulation engine for option portfolios. Through backtesting analysis, we show that our models are more computationally efficient and accurate for evaluating the Value-at-Risk (VaR) of option portfolios, with better coverage performance and less procyclicality than standard filtered historical simulation approaches.
Building Inspection Toolkit: Unified Evaluation And Strong Baselines For Bridge Damage Recognition

In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on one hand and inconsistent dataset splitting with the absence of consistent metrics on the other hand. This leads to incomparable results. Therefore, we introduce the building inspection toolkit – bikit – which acts as a simple to use data hub containing relevant open-source datasets in the field of damage recognition. The datasets are enriched with evaluation splits and predefined metrics, suiting the specific task and their data distribution. For the sake of compatibility and to motivate researchers in this domain, we also provide a leaderboard and the possibility to share model weights with the community. As a starting point we provide strong baselines utilizing extensive hyperparameter search using three transfer learning approaches for state-of-the-art algorithms. The toolkit1 and the leaderboard2 are available online.
The Hybrid Bootstrap

Finding a method to combine the numerical bootstrap with the analytic lightcone bootstrap is an important goal to advance the conformal bootstrap program. We propose a hybrid bootstrap method to do just that. The numerical and analytic bootstrap approaches are sensitive to different regions of the spectrum and complement each other. When they are effectively combined, the hybrid bootstrap enjoys the best of both worlds and the prediction for the actual CFT can be significantly improved. In this work, we discuss the general strategy to perform such a hybrid bootstrap, and we make a partial implementation of the strategy for 3D Ising CFT {σ, } system. Even at relatively low derivative order Λ = 19, the hybrid bootstrap predicts very precise values for the scaling dimension ∆σ,∆ that are within the previous Λ = 43 rigorous error bars. ar X iv :2 20 2. 07 60 7v 1 [ he pth ] 1 5 Fe b 20 22
XAI for Transformers: Better Explanations through Conservative Propagation

Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transparency. Multiple interpretability methods, often based on gradient information, have been proposed. We show that the gradient in a Transformer reflects the function only locally, and thus fails to reliably identify the contribution of input features to the prediction. We identify Attention Heads and LayerNorm as main reasons for such unreliable explanations and propose a more stable way for propagation through these layers. Our proposal, which can be seen as a proper extension of the well-established LRP method to Transformers, is shown both theoretically and empirically to overcome the deficiency of a simple gradient-based approach, and achieves state-of-the-art explanation performance on a broad range of Transformer models and datasets.
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy

Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can protect privacy in comparison with the traditional centralized learning paradigm. However, sensitive information about the training data can still be inferred from model parameters shared in FL. Differential privacy (DP) is the state-of-the-art technique to defend against those attacks. The key challenge to achieving DP in FL lies in the adverse impact of DP noise on model accuracy, particularly for deep learning models with large numbers of parameters. This paper develops a novel differentially-private FL scheme named Fed-SMP that provides a client-level DP guarantee while maintaining high model accuracy. To mitigate the impact of privacy protection on model accuracy, Fed-SMP leverages a new technique called Sparsified Model Perturbation (SMP) where local models are sparsified first before being perturbed by Gaussian noise. We provide a tight end-to-end privacy analysis for Fed-SMP using Renyi DP and prove the convergence of Fed-SMP with both unbiased and biased sparsifications. Extensive experiments on real-world datasets are conducted to demonstrate the effectiveness of Fed-SMP in improving model accuracy with the same DP guarantee and saving communication cost simultaneously.
Conformally and Disformally Coupled Vector Field Models of Dark Energy

Scalar fields coupled to dark matter by conformal or disformal transformations give rise to a general class of scalar-tensor theories which leads to a rich phenomenology in a cosmological setting. While this possibility has been studied comprehensively in the literature for scalar fields, the vector case has been hardly treated. We build hence models based on vector fields conformally and disformally coupled to dark matter and derive explicitly the general covariant form of the interaction term in an independent way of the gravity theory, whereby this result can be applied to general vector-tensor theories. For concreteness, the standard Proca theory with a vector exponential potential is taken to describe the vector-tensor sector, and some specific coupling functions are assumed to study the cosmological background dynamics by dynamical system techniques. Despite of choosing such a minimalist form for the underlying theory, the parameter space is considerably enriched compared to the uncoupled case due to the novel interactions, leading to new branches of solutions for the vector equation of motion. Thus, different trajectories can exist in phase space depending on the coupling parameters associated to the conformal and disformal functions. From here, new emerging vector-dark matter scaling solutions, and renewed stable attractor points are found to drive the late-time accelerated expansion of the universe. As a first examination about instabilities issues, we derive general conditions to avoid classical instabilities in a more general setup of the theory. Numerical calculations are performed as well to investigate more quantitatively the impact of the conformal and disformal couplings on the cosmological background evolution. These effects depend essentially on the strength on the coupling parameters and, in some specific cases, on their associated signs.
Enhancing Cross-lingual Prompting with Mask Token Augmentation

Prompting 1 shows promising results in few-shot scenarios. However, its strength for multilingual/cross-lingual problems has not been fully exploited. Zhao and Schütze (2021) made initial explorations in this direction by presenting that cross-lingual prompting outperforms cross-lingual ﬁnetuning. In this paper, we conduct empirical analysis on the effect of each component in cross-lingual prompting and derive Universal Prompting across languages, which helps alleviate the discrepancies between source-language training and target-language inference. Based on this, we propose a mask token augmentation framework to further improve the performance of prompt-based cross-lingual transfer. Notably, for XNLI, our method achieves 46.54% with only 16 English training examples per class, signiﬁcantly better than 34.99% of ﬁnetuning.
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decision-making problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The highly combinatorial nature of the problems commonly addressed by MCTS requires the use of efficient exploration strategies for navigating the planning tree and quickly convergent value backup methods. These crucial problems are particularly evident in recent advances that combine MCTS with deep neural networks for function approximation. In this work, we propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization. We provide strong theoretical guarantees to bound convergence rate, approximation error, and regret of our methods. Moreover, we introduce a mathematical framework based on the use of the α-divergence for backup and exploration in MCTS. We show that this theoretical formulation unifies different approaches, including our newly introduced ones, under the same mathematical framework, allowing to obtain different methods by simply changing the value of α. In practice, our unified perspective offers a flexible way to balance between exploration and exploitation by tuning the single α parameter according to the problem at hand. We validate our methods through a rigorous empirical study from basic toy problems to the complex Atari games, and including both MDP and POMDP problems. ———————