Miracle-dz/UniFAR-FTUs huggingface dataset:
Nontrivially Topological Phase Structure of Ideal Bose Gas System within Different Boundary Conditions

The phase structure of ideal Bose gas system within different boundary conditions, i.e., the periodic boundary condition and Dirichlet boundary condition in this work, in an infinite volume, is investigated. It is found that the ground states of ideal Bose gas within those two boundary conditions are both topologically nontrivial, which can not be classified by the traditional symmetry breaking theory. The ground states are different topological phases corresponding to those two boundary conditions, which can be distinguished by the off--diagonal particle number susceptibility. Moreover, this result is universal. The boundary condition may play an important role in pining the critical endpoint of QCD diagram on the approach of the lattice simulations and the computation of some solvable statistical models .
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition

Human activities often occur in specific scene contexts, e.g., playing basketball on a basketball court. Training a model using existing video datasets thus inevitably captures and leverages such bias (instead of using the actual discriminative cues). The learned representation may not generalize well to new action classes or different tasks. In this paper, we propose to mitigate scene bias for video representation learning. Specifically, we augment the standard cross-entropy loss for action classification with 1) an adversarial loss for scene types and 2) a human mask confusion loss for videos where the human actors are masked out. These two losses encourage learning representations that are unable to predict the scene types and the correct actions when there is no evidence. We validate the effectiveness of our method by transferring our pre-trained model to three different tasks, including action classification, temporal localization, and spatio-temporal action detection. Our results show consistent improvement over the baseline model without debiasing.
Medication Regimen Extraction From Clinical Conversations

Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach, and other baselines. We use a small corpus of 6,692 annotated doctor-patient conversations for the task. Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus scarce. We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task (summarization) to improve the model's performance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions' ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tags from spontaneous doctor-patient conversations with about $\approx$71% accuracy.
SMiRL: Surprise Minimizing RL in Dynamic Environments

All living organisms struggle against the forces of nature to carve out niches where they can maintain homeostasis. We propose that such a search for order amidst chaos might offer a unifying principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement learning method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the objective of maximizing the probability of observed states under a model trained on previously seen states. The resulting agents can acquire proactive behaviors that seek out and maintain stable conditions, such as balancing and damage avoidance, that are closely tied to an environment's prevailing sources of entropy, such as wind, earthquakes, and other agents. We demonstrate that our surprise minimizing agents can successfully play Tetris, Doom, control a humanoid to avoid falls and navigate to escape enemy agents, without any task-specific reward supervision. We further show that SMiRL can be used together with a standard task reward to accelerate reward-driven learning.
Filter Distillation for Network Compression

In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between filter responses within network layers to recommend a smaller network that maintain as much as possible the accuracy of the full model. We propose two algorithms: the first allows users to target compression to specific network property, such as number of trainable variable (footprint), and produces a compressed model that satisfies the requested property while preserving the maximum amount of spectral energy in the responses of each layer, while the second is a parameter-free heuristic that selects the compression used at each layer by trying to mimic an ideal set of uncorrelated responses. Since PFA compresses networks based on the correlation of their responses we show in our experiments that it gains the additional flexibility of adapting each architecture to a specific domain while compressing. PFA is evaluated against several architectures and datasets, and shows considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. Our tests show that PFA is competitive with state-of-the-art approaches while removing adoption barriers thanks to its practical implementation, intuitive philosophy and ease of use.
Breaking the Limits of Redundancy Systems Analysis

Redundancy mechanisms such as triple modular redundancy protect safety-critical components by replication and thus improve systems fault tolerance. However, the gained fault tolerance comes along with costs to be invested, e.g., increasing execution time, energy consumption, or packaging size, for which constraints have to be obeyed during system design. This turns the question of finding suitable combinations of components to be protected into a challenging task as the number of possible protection combinations grows exponentially in the number of components. We propose family-based approaches to tackle the combinatorial blowup in redundancy systems modeling and analysis phases. Based on systems designed in SIMULINK we show how to obtain models that include all possible protection combinations and present a tool chain that, given a probabilistic error model, generates discrete Markov chain families. Using symbolic techniques that enable concise family representation and analysis, we show how SIMULINK models of realistic size can be protected and analyzed with a single family-based analysis run while a one-by-one analysis of each protection combination would clearly exceed any realistic time constraints.
Dynamic Algorithmic Service Agreements Perspective

A multi-disciplinary understanding of the concepts of identity, agency, relationships, interactions, and information could help us develop mitigation strategies for responsible human-algorithmic systems in the field of AI. It is imperative for us to question the use of the Terms of Service (ToS) agreements model in the context of algorithmic systems, specifically AI systems that make decisions which affect people and their livelihoods. In this position paper, we identify five areas of concern in traditional ToS agreements by drawing on studies of sociotechnical systems in Science and Technology Studies - accommodating and enabling change, co-constitution, reflective directionality, friction, and generativity. We aim to address these ToS shortcomings and propose components of a novel Dynamic Algorithmic Service Agreements (DASA) framework. The DASA could be employed as a self-regulation framework while also enabling additional feedback loops between people and algorithmic systems. Rich interaction frameworks could enable us to better negotiate and cooperate with AI systems towards accomplishing the real-world goals we use them for. We illustrate the DASA framework in the context of a Recommender System used in the curation of real and synthetic data. We do not intend for the DASA framework to replace the ToS model, but instead think it will provide practitioners with an alternative point of view for the design of dynamic interaction interfaces for AI systems that account for human identity and agency.
MetaMT, a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation

Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. We propose to split parameters in the model into two groups: model parameters and meta parameters. The former are used to model the translation while the latter are used to adjust the representational space to generalize the model to different domains. We mimic the domain adaptation of the machine translation model to low-resource domains using multiple translation tasks on different domains. A new training strategy based on meta-learning is developed along with the proposed model to update the model parameters and meta parameters alternately. Experiments on datasets of different domains showed substantial improvements of NMT performances on a limited amount of data.
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization

We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall, the results collected on a wide variety of qualitatively different benchmark problems indicate that these methods are generally effective and scale well with respect to the number of parameters and the complexity of the problem. Moreover, they are relatively robust with respect to the setting of hyper-parameters. The comparison of the most promising methods indicates that the OpenAI-ES algorithm outperforms or equals the other algorithms on all considered problems. Moreover, we demonstrate how the reward functions optimized for reinforcement learning methods are not necessarily effective for evolutionary strategies and vice versa. This finding can lead to reconsideration of the relative efficacy of the two classes of algorithm since it implies that the comparisons performed to date are biased toward one or the other class.
Tail Risks, Investment Horizons, and Asset Prices

We examine how extreme market risks are priced in the cross-section of asset returns at various horizons. Based on the frequency decomposition of covariance between indicator functions, we define the quantile cross-spectral beta of an asset capturing tail-specific as well as horizon-, or frequency-specific risks. Further, we work with two notions of frequency-specific extreme market risks. First, we define tail market risk that captures dependence between extremely low market as well as asset returns. Second, extreme market volatility risk is characterized by dependence between extremely high increments of market volatility and extremely low asset return. Empirical findings based on the datasets with long enough history, 30 Fama-French Industry portfolios, and 25 Fama-French portfolios sorted on size and book-to-market support our intuition. Results suggest that both frequency-specific tail market risk and extreme volatility risks are significantly priced and our five-factor model provides improvement over specifications considered by previous literature.
Triaxiality can Explain the Alleged Dark Matter Deficiency in some Dwarf Galaxies

Dark Matter (DM) is an ingredient essential to the current cosmological concordance model. It provides the gravitational pull needed for the baryons to form galaxies. Therefore, the existence of galaxies without DM is both disquieting and extremely interesting. Guo et al. recently presented "further evidence for a population of DM-deficient dwarf galaxies", however, their analysis bypasses the triaxiality of the dwarf galaxies. We carry out a Monte Carlo simulation showing how triaxiality must be considered to measure dynamical masses from projected axial ratios, calling into question the evidence for a population of DM-deficient dwarf galaxies. Such a population may consist of normal almost face-on HI disks with their inclination overestimated.
Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS

Recent work on Neural-Symbolic systems that learn the discrete planning model from images has opened a promising direction for expanding the scope of Automated Planning and Scheduling to the raw, noisy data. However, previous work only partially addressed this problem, utilizing the black-box neural model as the successor generator. In this work, we propose Double-Stage Action Model Acquisition (DSAMA), a system that obtains a descriptive PDDL action model with explicit preconditions and effects over the propositional variables unsupervized-learned from images. DSAMA trains a set of Random Forest rule-based classifiers and compiles them into logical formulae in PDDL. While we obtained a competitively accurate PDDL model compared to a black-box model, we observed that the resulting PDDL is too large and complex for the state-of-the-art standard planners such as Fast Downward primarily due to the PDDL-SAS+ translator bottleneck. From this negative result, we argue that this translator bottleneck cannot be addressed just by using a different, existing rule-based learning method, and we point to the potential future directions.
An Ensemble Method for Producing Word Representations for the Greek Language

In this paper we present a new ensemble method, Continuous Bag-of-Skip-grams (CBOS), that produces high-quality word representations for the Greek language. The CBOS method combines the pioneering approaches for learning word representations: Continuous Bag-of-Words (CBOW) and Continuous Skip-gram. These methods are compared through a word analogy task on three different sources of data: the English Wikipedia corpus, the Greek Wikipedia corpus, and the Greek Web Content corpus. By comparing these methods across different datasets, it is evident that the CBOS method achieves state-of-the-art performance.
Bottleneck detection by slope difference distribution: a robust approach for separating overlapped cells

To separate the overlapped cells, a bottleneck detection approach is proposed in this paper. The cell image is segmented by slope difference distribution (SDD) threshold selection. For each segmented binary clump, its one-dimensional boundary is computed as the distance distribution between its centroid and each point on the two-dimensional boundary. The bottleneck points of the one-dimensional boundary is detected by SDD and then transformed back into two-dimensional bottleneck points. Two largest concave parts of the binary clump are used to select the valid bottleneck points. Two bottleneck points from different concave parts with the minimum Euclidean distance is connected to separate the binary clump with minimum-cut. The binary clumps are separated iteratively until the number of computed concave parts is smaller than two. We use four types of open-accessible cell datasets to verify the effectiveness of the proposed approach and experimental results showed that the proposed approach is significantly more robust than state of the art methods.
Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI

Objective. Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR). Approach. The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs); n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database. Main results. We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data. Significance. APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters’ preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
Integration of Simultaneous Resting-State EEG, fMRI, and Eye Tracker Methods to Determine and Verify EEG Vigilance Measure.

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used for studying the (presumably) awake and alert human brain. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixation cross, objective and verified experimental measures to quantify degree of alertness (e.g., vigilance) are not readily available. Concurrent electroencephalography and fMRI (EEG-fMRI) measurements are also widely used to study human brain with high spatial/temporal resolution. EEG is the modality extensively used for estimating vigilance during eyes-closed resting state. On the other hand, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. In this study, we investigated whether simultaneous multimodal EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with pupil size changes (e.g., vigilance measure) in healthy human subjects (n=10) during brain rest with eyes open. We found that EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r=0.306, p<0.001). Moreover, FOBP also correlated with heart rate (r=0.255, p<0.001), as well as several brain regions in the anti-correlated network, including the bilateral insula and inferior parietal lobule. These results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects.
Just Add Functions: A Neural-Symbolic Language Model

Neural network language models (NNLMs) have achieved ever-improving accuracy due to more sophisticated architectures and increasing amounts of training data. However, the inductive bias of these models (formed by the distributional hypothesis of language), while ideally suited to modeling most running text, results in key limitations for today's models. In particular, the models often struggle to learn certain spatial, temporal, or quantitative relationships, which are commonplace in text and are second-nature for human readers. Yet, in many cases, these relationships can be encoded with simple mathematical or logical expressions. How can we augment today's neural models with such encodings? In this paper, we propose a general methodology to enhance the inductive bias of NNLMs by incorporating simple functions into a neural architecture to form a hierarchical neural-symbolic language model (NSLM). These functions explicitly encode symbolic deterministic relationships to form probability distributions over words. We explore the effectiveness of this approach on numbers and geographic locations, and show that NSLMs significantly reduce perplexity in small-corpus language modeling, and that the performance improvement persists for rare tokens even on much larger corpora. The approach is simple and general, and we discuss how it can be applied to other word classes beyond numbers and geography.
Breaking a chain of interacting Brownian particles

We investigate the behaviour of a finite chain of Brownian particles, interacting through a pairwise quadratic potential, with one end of the chain fixed and the other end pulled away at slow speed, in the limit of slow speed and small Brownian noise. We study the instant when the chain "breaks", that is, the distance between two neighboring particles becomes larger than a certain limit. There are three different regimes depending on the relation between the speed of pulling and the Brownian noise. We prove weak limit theorems for the break time and the break position for each regime.