Miracle-dz/UniFAR-FTUs huggingface dataset:
Can You Solve Closest String Faster than Exhaustive Search?

We study the fundamental problem of finding the best string to represent a given set, in the form of the Closest String problem: Given a set $X \subseteq \Sigma^d$ of $n$ strings, find the string $x^*$ minimizing the radius of the smallest Hamming ball around $x^*$ that encloses all the strings in $X$. In this paper, we investigate whether the Closest String problem admits algorithms that are faster than the trivial exhaustive search algorithm. We obtain the following results for the two natural versions of the problem: $\bullet$ In the continuous Closest String problem, the goal is to find the solution string $x^*$ anywhere in $\Sigma^d$. For binary strings, the exhaustive search algorithm runs in time $O(2^d poly(nd))$ and we prove that it cannot be improved to time $O(2^{(1-\epsilon) d} poly(nd))$, for any $\epsilon>0$, unless the Strong Exponential Time Hypothesis fails. $\bullet$ In the discrete Closest String problem, $x^*$ is required to be in the input set $X$. While this problem is clearly in polynomial time, its fine-grained complexity has been pinpointed to be quadratic time $n^{2 \pm o(1)}$ whenever the dimension is $\omega(\log n)<d<n^{o(1)}$. We complement this known hardness result with new algorithms, proving essentially that whenever $d$ falls out of this hard range, the discrete Closest String problem can be solved faster than exhaustive search. In the small-$d$ regime, our algorithm is based on a novel application of the inclusion-exclusion principle. Interestingly, all of our results apply (and some are even stronger) to the natural dual of the Closest String problem, called the Remotest String problem, where the task is to find a string maximizing the Hamming distance to all the strings in $X$.
Magnetic fields around black holes

Title of Dissertation: Magnetic Fields Around Black Holes David A. G. Garofalo, Doctor of Philosophy, 2008 Dissertation directed by: Professor Christopher Stephen Reynolds Department of Astronomy Active Galactic Nuclei are the most powerful long-lived objects in the universe. They are thought to harbor supermassive black holes that range from 1 million solar masses to 1000 times that value and possibly greater. Theory and observation are converging on a model for these objects that involves the conversion of gravitational potential energy of accreting gas to radiation as well as Poynting flux produced by the interaction of the rotating spacetime and the electromagnetic fields originating in the ionized accretion flow. The presence of black holes in astrophysics is taking center stage, with the output from AGN in various forms such as winds and jets influencing the formation and evolution of the host galaxy. This dissertation addresses some of the basic unanswered questions that plague our current understanding of how rotating black holes interact with their surrounding magnetized accretion disks to produce the enormous observed energy. Two magnetic configurations are examined. The first involves magnetic fields connecting the black hole with the inner accretion disk and the other involves large scale magnetic fields threading the disk and the hole. We study the effects of the former type by establishing the consequences that magnetic torques between the black hole and the inner accretion disk have on the energy dissipation profile. We attempt a plausible explanation to the observed “Deep Minimum” state in the Seyfert galaxy MCG-6-30-15. For the latter type of magnetic geometry, we study the effects of the strength of the magnetic field threading the black hole within the context of the cherished Blandford & Znajek mechanism for black hole spin energy extraction. We begin by addressing the problem in the non-relativistic regime where we find that the black hole-threading magnetic field is stronger for greater disk thickness, larger magnetic Prandtl number, and for a larger accretion disk. We then study the problem in full relativity where we show that our Newtonian results are excellent approximations for slowly spinning black holes. We proceed to address the issue of the spin dependence of the Blandford & Znajek power. The result we choose to highlight is our finding that given the validity of our assumption for the dynamical behavior of the so-called plunge region in black hole accretors, rotating black holes produce maximum Poynting flux via the Blandford & Znajek process for a black hole spin parameter of about a≈ 0.8. This is contrary to the conventional claim that the maximum electromagnetic flux is achieved for highest black hole spin. Magnetic Fields Around Black Holes by David A. G. Garofalo Dissertation submitted to the Faculty of the Graduate School of the University of Maryland at College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2008
Automated Summarization of Stack Overflow Posts

Software developers often resort to Stack Overflow (SO) to fill their programming needs. Given the abundance of relevant posts, navigating them and comparing different solutions is tedious and time-consuming. Recent work has proposed to automatically summarize SO posts to concise text to facilitate the navigation of SO posts. However, these techniques rely only on information retrieval methods or heuristics for text summarization, which is insufficient to handle the ambiguity and sophistication of natural language. This paper presents a deep learning based framework called Assortfor SO post summarization. Assortincludes two complementary learning methods, $\mathbf{Assort}_{S}$ and $\mathbf{Assort}_{IS}$, to address the lack of labeled training data for SO post summarization. $\mathbf{Assort}_{S}$ is designed to directly train a novel ensemble learning model with BERT embeddings and domain-specific features to account for the unique characteristics of SO posts. By contrast, $\mathbf{Assort}_{IS}$ is designed to reuse pre-trained models while addressing the domain shift challenge when no training data is present (i.e., zero-shot learning). Both $\mathbf{Assort}_{S}$ and $\mathbf{Assort}_{IS}$ outperform six existing techniques by at least 13% and 7% respectively in terms of the F1 score. Furthermore, a human study shows that participants significantly preferred summaries generated by $\mathbf{Assort}_{S}$ and $\mathbf{Assort}_{IS}$ over the best baseline, while the preference difference between $\mathbf{Assort}_{S}$ and $\mathbf{Assort}_{IS}$ was small.
Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods.
Don’t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text

Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite adversarial inputs to make them non-adversarial for a downstream text classifier. Our experiments on four datasets and five attack mechanisms reveal that ATINTER is effective at providing better adversarial robustness than existing defense approaches, without compromising task accuracy. For example, on sentiment classification using the SST-2 dataset, our method improves the adversarial accuracy over the best existing defense approach by more than 4% with a smaller decrease in task accuracy (0.5 % vs 2.5%). Moreover, we show that ATINTER generalizes across multiple downstream tasks and classifiers without having to explicitly retrain it for those settings. For example, we find that when ATINTER is trained to remove adversarial perturbations for the sentiment classification task on the SST-2 dataset, it even transfers to a semantically different task of news classification (on AGNews) and improves the adversarial robustness by more than 10%.
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach

Ranking is at the core of many artificial intelligence (AI) applications, including search engines, recommender systems, etc. Modern ranking systems are often constructed with learning-to-rank (LTR) models built from user behavior signals. While previous studies have demonstrated the effectiveness of using user behavior signals (e.g., clicks) as both features and labels of LTR algorithms, we argue that existing LTR algorithms that indiscriminately treat behavior and non-behavior signals in input features could lead to suboptimal performance in practice. Particularly because user behavior signals often have strong correlations with the ranking objective and can only be collected on items that have already been shown to users, directly using behavior signals in LTR could create an exploitation bias that hurts the system performance in the long run. To address the exploitation bias, we propose EBRank, an empirical Bayes-based uncertainty-aware ranking algorithm. Specifically, to overcome exploitation bias brought by behavior features in ranking models, EBRank uses a sole non-behavior feature based prior model to get a prior estimation of relevance. In the dynamic training and serving of ranking systems, EBRank uses the observed user behaviors to update posterior relevance estimation instead of concatenating behaviors as features in ranking models. Besides, EBRank additionally applies an uncertainty-aware exploration strategy to explore actively, collect user behaviors for empirical Bayesian modeling and improve ranking performance. Experiments on three public datasets show that EBRank is effective, practical and significantly outperforms state-of-the-art ranking algorithms.
Accelerating Value Iteration with Anchoring

Value Iteration (VI) is foundational to the theory and practice of modern reinforcement learning, and it is known to converge at a $\mathcal{O}(\gamma^k)$-rate, where $\gamma$ is the discount factor. Surprisingly, however, the optimal rate for the VI setup was not known, and finding a general acceleration mechanism has been an open problem. In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators. Our method, called Anc-VI, is based on an \emph{anchoring} mechanism (distinct from Nesterov's acceleration), and it reduces the Bellman error faster than standard VI. In particular, Anc-VI exhibits a $\mathcal{O}(1/k)$-rate for $\gamma\approx 1$ or even $\gamma=1$, while standard VI has rate $\mathcal{O}(1)$ for $\gamma\ge 1-1/k$, where $k$ is the iteration count. We also provide a complexity lower bound matching the upper bound up to a constant factor of $4$, thereby establishing optimality of the accelerated rate of Anc-VI. Finally, we show that the anchoring mechanism provides the same benefit in the approximate VI and Gauss--Seidel VI setups as well.
Gender, Smoking History, and Age Prediction from Laryngeal Images

Flexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. The diagnostic performance can be improved when patients’ demographic information is incorporated into models. However, the manual entry of patient data is time-consuming for clinicians. In this study, we made the first endeavor to employ deep learning models to predict patient demographic information to improve the detector model’s performance. The overall accuracy for gender, smoking history, and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a new laryngoscopic image set for the machine learning study and benchmarked the performance of eight classical deep learning models based on CNNs and Transformers. The results can be integrated into current learning models to improve their performance by incorporating the patient’s demographic information.
Talking with Machines: A Comprehensive Survey of Emergent Dialogue Systems

From the earliest experiments in the 20th century to the utilization of large language models and transformers, dialogue systems research has continued to evolve, playing crucial roles in numerous fields. This paper offers a comprehensive review of these systems, tracing their historical development and examining their fundamental operations. We analyze popular and emerging datasets for training and survey key contributions in dialogue systems research, including traditional systems and advanced machine learning methods. Finally, we consider conventional and transformer-based evaluation metrics, followed by a short discussion of prevailing challenges and future prospects in the field.
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering

The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and node redundancy, which challenges CSKG representation and completion. On the one hand, edge sparsity limits the performance of graph representation learning; On the other hand, node redundancy makes different nodes corresponding to the same concept have inconsistent relations with other nodes. To address the two problems, we propose a new CSKG completion framework based on Contrastive Pretraining and Node Clustering (CPNC). Contrastive Pretraining constructs positive and negative head-tail node pairs on CSKG and utilizes contrastive learning to obtain better semantic node representation. Node Clustering aggregates nodes with the same concept into a latent concept, assisting the task of CSKG completion. We evaluate our CPNC approach on two CSKG completion benchmarks (CN-100K and ATOMIC), where CPNC outperforms the state-of-the-art methods. Extensive experiments demonstrate that both Contrastive Pretraining and Node Clustering can significantly improve the performance of CSKG completion. The source code of CPNC is publicly available on \url{https://github.com/NUSTM/CPNC}.
Sources of Uncertainty in Machine Learning - A Statisticians' View

Machine Learning and Deep Learning have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction, which is the primary strength of most supervised machine learning algorithms, the quantification of uncertainty is relevant and necessary as well. While first concepts and ideas in this direction have emerged in recent years, this paper adopts a conceptual perspective and examines possible sources of uncertainty. By adopting the viewpoint of a statistician, we discuss the concepts of aleatoric and epistemic uncertainty, which are more commonly associated with machine learning. The paper aims to formalize the two types of uncertainty and demonstrates that sources of uncertainty are miscellaneous and can not always be decomposed into aleatoric and epistemic. Drawing parallels between statistical concepts and uncertainty in machine learning, we also demonstrate the role of data and their influence on uncertainty.
Imprints of the primordial Universe in the Cosmic Microwave and gravitational wave backgrounds

La recherche presentee dans cette these represente d’importantes briques sur le chemin de la construction d’une comprehension plus complete et profonde des mecanismes physique caracteristiques des premieres secondes apres la creation de notre Univers, un regime de temperature et d’energies autrement inaccessibles en laboratoire. De plus, ceci souligne egalement les questions ouvertes qui restent non resolues.
Privacy-aware Gaussian Process Regression

We propose the first theoretical and methodological framework for Gaussian process regression subject to privacy constraints. The proposed method can be used when a data owner is unwilling to share a high-fidelity supervised learning model built from their data with the public due to privacy concerns. The key idea of the proposed method is to add synthetic noise to the data until the predictive variance of the Gaussian process model reaches a prespecified privacy level. The optimal covariance matrix of the synthetic noise is formulated in terms of semi-definite programming. We also introduce the formulation of privacy-aware solutions under continuous privacy constraints using kernel-based approaches, and study their theoretical properties. The proposed method is illustrated by considering a model that tracks the trajectories of satellites.
ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models

Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec. Modern large language models (LLMs), however, are primarily evaluated on extrinsic measures based on benchmarks such as GLUE and SuperGLUE, and there are only a few investigations on whether LLMs can draw analogies between long texts. In this paper, we present ANALOGICAL, a new benchmark to intrinsically evaluate LLMs across a taxonomy of analogies of long text with six levels of complexity -- (i) word, (ii) word vs. sentence, (iii) syntactic, (iv) negation, (v) entailment, and (vi) metaphor. Using thirteen datasets and three different distance measures, we evaluate the abilities of eight LLMs in identifying analogical pairs in the semantic vector space. Our evaluation finds that it is increasingly challenging for LLMs to identify analogies when going up the analogy taxonomy.
Counterfactual Probing Intergroup Bias for Affect and Specificity

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts — thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship labels. Coun-terfactual probing further reveals that while neural models finetuned for predicting IGR labels reliably use affect in classification, the model’s usage of specificity is inconclusive.
Are there clinical, psychopathological and therapy outcomes correlates associated with self-exclusion from gambling?

Abstract Background and aims Self-exclusion in gambling disorder (GD) is considered a measure to decrease the negative consequences of gambling behavior. Under a formal self-exclusion program, gamblers request to be banned from accessing to the gambling venues or online gambling. The aims of the present study are 1) to determine sociodemographic characteristics of a clinical sample of seeking-treatment patients with GD who are self-excluded before arriving at the care unit; 2) to identify personality traits and general psychopathology of this clinical population; 3) to analyze the response to treatment, in terms of relapses and dropouts. Methods 1,416 adults seeking treatment for GD, who are self-excluded completed screening tools to identify GD symptomatology, general psychopathology, and personality traits. The treatment outcome was measured by dropout and relapses. Results Self-exclusion was significantly related to female sex and a high sociodemographic status. Also, it was associated with a preference for strategic and mixed gambling, longest duration and severity of the disorder, high rates of general psychopathology, more presence of illegal acts and high sensation seeking rates. In relation to treatment, self-exclusion was associated with low relapse rates. Conclusions The patients who self-exclude before seeking treatment have a specific clinical profile, including high sociodemographic status, highest severity of GD, more years of evolution of the disorder and high emotional distress rates; however, these patients' presents better response to treatment. Clinically, it could be expected that this strategy could be used as a facilitating variable in the therapeutic process.
Worker Reproduction and Caste Polymorphism Impact Genome Evolution and Social Genes Across the Ants

Abstract Eusocial insects are characterized by several traits, including reproductive division of labor and caste polymorphisms, which likely modulate genome evolution. Concomitantly, evolution may act on specific genes and pathways underlying these novel, sociality-associated phenotypes. Reproductive division of labor should increase the magnitude of genetic drift and reduce the efficacy of selection by reducing effective population size. Caste polymorphism has been associated with relaxed selection and may facilitate directional selection on caste-specific genes. Here, we use comparative analyses of 22 ant genomes to test how reproductive division of labor and worker polymorphism influence positive selection and selection intensity across the genome. Our results demonstrate that worker reproductive capacity is associated with a reduction in the degree of relaxed selection but is not associated with any significant change to positive selection. We find decreases in positive selection in species with polymorphic workers, but no increase in the degree of relaxed selection. Finally, we explore evolutionary patterns in specific candidate genes associated with our focal traits in eusocial insects. Two oocyte patterning genes previously implicated in worker sterility evolve under intensified selection in species with reproductive workers. Behavioral caste genes generally experience relaxed selection associated with worker polymorphism, whereas vestigial and spalt, both associated with soldier development in Pheidole ants, experience intensified selection in worker polymorphic species. These findings expand our understanding of the genetic mechanisms underlying elaborations of sociality. The impacts of reproductive division of labor and caste polymorphisms on specific genes illuminate those genes’ roles in generating complex eusocial phenotypes.
Prognostic nutritional score based on pretreatment lymphocyte, platelet, and prealbumin predicts prognosis in patients with pancreatic cancer

Pretreatment immunological indicators and nutritional factors are associated with survival of many malignancies. This study aims to develop a prognostic nutritional score based on a combination of pretreatment lymphocyte, platelet, and prealbumin (Co‐LPPa) in patients with pancreatic cancer (PC) and to investigate the prognostic significance of this score.