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Localizing the seizure onset zone (SOZ) as a step of presurgical planning leads to higher efficiency in surgical and stimulation treatments. However, the clinical localization including structural, ictal, and invasive data acquisition and assessment is a difficult and long procedure with increasing challenges in patients with complex epileptic foci. The interictal methods are proposed to assist in presurgical planning with simpler data acquisition and higher speed. This study presents a spatiotemporal component classification for the localization of epileptic foci using resting-state functional magnetic resonance imaging data. This method is based on spatiotemporal independent component analysis on rsfMRI with a component-sorting procedure upon dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. This method utilized the rs-fMRI potential to reach a high spatial accuracy in localizing epileptic foci from interictal data while retaining the reliability of results for clinical usage. Thirteen patients with temporal lobe epilepsy who underwent surgical resection and had seizure-free surgical outcomes after a 12-month follow-up were included in this study. All patients had presurgical structural MRI and rsfMRI while postsurgical MRI images were available for ten. Based on the relationship between the localized foci and resection, the results were classified into three groups fully concordant, partially concordant, and discordant. These groups had the resulting cluster aligned with, in the same lobe with, and outside the lobe of the resection area, respectively.

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.

Road Extraction is a sub-domain of Remote Sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. The DeepLab series, known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects' features, addresses some of these challenges caused by the varying nature of roads. The present work proposes the utilization of DeepLabV3+, the latest version of the DeepLab series, by introducing an innovative Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) module and integrating it in place of the conventional Atrous Spatial Pyramid Pooling (ASPP) module. This modification enhances the extraction of complex road structures from satellite images. This study hypothesizes that the integration of DenseDDSSPP, combined with an appropriately selected backbone network and a Squeeze-and-Excitation block, will generate an efficient dense feature map by focusing on relevant features, leading to more precise and accurate road extraction from Remote Sensing images. The results section presents a comparison of our model's performance against state-of-the-art models, demonstrating better results that highlight the effectiveness and success of the proposed approach., Comment: 9 pages, 5 figures

One fundamental task for NLP is to determine the similarity between two texts and evaluate the extent of their likeness. The previous methods for the Persian language have low accuracy and are unable to comprehend the structure and meaning of texts effectively. Additionally, these methods primarily focus on formal texts, but in real-world applications of text processing, there is a need for robust methods that can handle colloquial texts. This requires algorithms that consider the structure and significance of words based on context, rather than just the frequency of words. The lack of a proper dataset for this task in the Persian language makes it important to develop such algorithms and construct a dataset for Persian text. This paper introduces a new transformer-based model to measure semantic similarity between Persian informal short texts from social networks. In addition, a Persian dataset named FarSSiM has been constructed for this purpose, using real data from social networks and manually annotated and verified by a linguistic expert team. The proposed model involves training a large language model using the BERT architecture from scratch. This model, called FarSSiBERT, is pre-trained on approximately 104 million Persian informal short texts from social networks, making it one of a kind in the Persian language. Moreover, a novel specialized informal language tokenizer is provided that not only performs tokenization on formal texts well but also accurately identifies tokens that other Persian tokenizers are unable to recognize. It has been demonstrated that our proposed model outperforms ParsBERT, laBSE, and multilingual BERT in the Pearson and Spearman's coefficient criteria. Additionally, the pre-trained large language model has great potential for use in other NLP tasks on colloquial text and as a tokenizer for less-known informal words.

This study introduces a methodology integrating Zero Trust Architecture (ZTA) principles and Transparent Shaping into an AWS-hosted Online File Manager (OFM) application, enhancing security without substantial code modifications. We evaluate our approach with the Mozilla Observatory, highlighting significant security improvements and outlining a promising direction for applying Transparent Shaping and ZTA in cloud environments., Comment: 8 pages, 2 figures, 2 tables

We present a model in which the Gauss-Bonnet invariant holds the quintessence at a fixed point, respecting an initial $Z_2$ symmetry in the radiation-dominated era. This results in an early dark energy that becomes significant around the matter-radiation equality era. However, due to $Z_2$ symmetry breaking, scalarization occurs, leading to a rapid reduction in the early dark energy density. The model then quickly behaves like the $\Lambda$CDM model. This scenario may also explain the reduction of sound horizon and aligns with the assumption that the gravitational wave speed is infinitesimally close to the speed of light., Comment: 18 pages, 6 figures. Major revision. Accepted for publication in Physics Letters B

In the global challenge of understanding and characterizing biodiversity, short species-specific genomic sequences known as DNA barcodes play a critical role, enabling fine-grained comparisons among organisms within the same kingdom of life. Although machine learning algorithms specifically designed for the analysis of DNA barcodes are becoming more popular, most existing methodologies rely on generic supervised training algorithms. We introduce BarcodeBERT, a family of models tailored to biodiversity analysis and trained exclusively on data from a reference library of 1.5M invertebrate DNA barcodes. We compared the performance of BarcodeBERT on taxonomic identification tasks against a spectrum of machine learning approaches including supervised training of classical neural architectures and fine-tuning of general DNA foundation models. Our self-supervised pretraining strategies on domain-specific data outperform fine-tuned foundation models, especially in identification tasks involving lower taxa such as genera and species. We also compared BarcodeBERT with BLAST, one of the most widely used bioinformatics tools for sequence searching, and found that our method matched BLAST's performance in species-level classification while being 55 times faster. Our analysis of masking and tokenization strategies also provides practical guidance for building customized DNA language models, emphasizing the importance of aligning model training strategies with dataset characteristics and domain knowledge. The code repository is available at https://github.com/bioscan-ml/BarcodeBERT., Comment: Main text: 14 pages, Total: 23 pages, 10 figures, formerly accepted at the 4th Workshop on Self-Supervised Learning: Theory and Practice (NeurIPS 2023)

We consider a model consisting of a quintessence coupled to the Ricci scalar and the Gauss-Bonnet invariant. We propose a mechanism for the rising of dark energy in the matter era, alleviating the coincidence problem. The role of the Gauss-Bonnet invariant is to restore the initial $Z_2$ symmetry. The Ricci scalar breaks this symmetry during the Universe's expansion and activates the quintessence. In our model, the observational constraint on gravitational wave speed is respected., Comment: 14 pages, 5 figures

Small metal-containing molecules have been detected and recognized as one of the hybrid species efficiently formed in space; especially in the circumstellar envelopes of evolved stars. It has been predicted also that more complex hybrid species like those formed by metals and fullerenes (metallofullerenes) could be present in such circumstellar environments. Recently, quantum-chemical simulations of metallofullerenes have shown that they are potential emitters contributing to the observed mid-IR spectra in the fullerene-rich circumstellar environments of different types of evolved stars. Here we present the individual simulated mid-IR (~5-50 um) spectra of twenty-eight metallofullerene species; both neutral and charged endo- and exohedral metallofullerenes for seven different metals (Li, Na, K, Ca, Mg, Ti, and Fe) have been considered. The changes induced by the metal-C60 interaction on the intensity and position of the spectral features are highlighted using charge density difference maps and electron density partitioning. Our calculations identify the fundamental IR spectral regions where, depending on the metal binding nature, there should be a major spectral contribution from each of the metallofullerenes. The metallofullerenes IR spectra are made publicly available to the astronomical community, especially James Webb Space Telescope users, for comparisons that could eventually lead to the detection of these species in space., Comment: Accepted for publication in The Astrophysical Journal Supplement Series on 19 September 2023 (in press) (13 pages, 7 figures, and 1 table)

We introduce a dynamical dark energy model wherein quintessence interacts with both the Gauss-Bonnet invariant and dark matter. Initially, the Gauss-Bonnet invariant stabilizes the quintessence at a fixed point, resulting in a negligible density of dark energy. Subsequently, the conformal coupling to dark matter triggers the evolution of dark energy. This model proposes an explanation for the initial absence of dark energy in radiation era and its later emergence during the matter-dominated era, achieving a magnitude comparable to dark matter in the present epoch. In this scenario, the Gauss-Bonnet term does not directly influence late-time cosmic evolution. Our model aligns with the assumption that the speed of gravitational wave is infinitesimally close to the speed of light., Comment: 20 pages, 7 figures, major revision, accepted for publication in Physics Letters B

Despite its broad practical applications such as in fraud prevention, open-set speaker identification (OSI) has received less attention in the speaker recognition community compared to speaker verification (SV). OSI deals with determining if a test speech sample belongs to a speaker from a set of pre-enrolled individuals (in-set) or if it is from an out-of-set speaker. In addition to the typical challenges associated with speech variability, OSI is prone to the "false-alarm problem"; as the size of the in-set speaker population (a.k.a watchlist) grows, the out-of-set scores become larger, leading to increased false alarm rates. This is in particular challenging for applications in financial institutions and border security where the watchlist size is typically of the order of several thousand speakers. Therefore, it is important to systematically quantify the false-alarm problem, and develop techniques that alleviate the impact of watchlist size on detection performance. Prior studies on this problem are sparse, and lack a common benchmark for systematic evaluations. In this paper, we present the first public benchmark for OSI, developed using the VoxCeleb dataset. We quantify the effect of the watchlist size and speech duration on the watchlist-based speaker detection task using three strong neural network based systems. In contrast to the findings from prior research, we show that the commonly adopted adaptive score normalization is not guaranteed to improve the performance for this task. On the other hand, we show that score calibration and score fusion, two other commonly used techniques in SV, result in significant improvements in OSI performance., Comment: 8 pages

Many biological active agents respond to gradients of environmental cues by redirecting their motion. Besides the well-studied prominent examples such as photo- and chemotaxis, there has been considerable recent interest in topotaxis, i.e.\ the ability to sense and follow topographic environmental cues. We numerically investigate the topotaxis of active agents moving in regular arrays of circular pillars. While a trivial topotaxis is achievable through a spatial gradient of obstacle density, here we show that imposing a gradient in the characteristics of agent-obstacle interaction can lead to an effective topotaxis in an environment with a spatially uniform density of obstacles. As a proof of concept, we demonstrate how a gradient in the angle of sliding around pillars -- as e.g.\ observed in bacterial dynamics near surfaces -- breaks the spatial symmetry and biases the direction of motion. We provide an explanation for this phenomenon based on effective reflection at the imaginary interface between pillars with different sliding angles. Our results are of technological importance for design of efficient taxis devices., Comment: 5 pages,5 figures

The presence of neutral C60 fullerenes in circumstellar environments has been firmly established by astronomical observations as well as laboratory experiments and quantum-chemistry calculations. However, the large variations observed in the C60 17.4um/18.9um band ratios indicate that either additional emitters should contribute to the astronomical IR spectra or there exist unknown physical processes besides thermal and UV excitation. Fullerene-based molecules such as metallofullerenes and fullerene-adducts are natural candidate species as potential additional emitters, but no specific species has been identified to date. Here we report a model based on quantum-chemistry calculations and IR spectra simulation of neutral and charged endo(exo)hedral metallofullerenes, showing that they have a significant contribution to the four strongest IR bands commonly attributed to neutral C60. These simulations may explain the large range of 17.4um/18.9um band ratios observed in very different fullerene-rich circumstellar environments like those around planetary nebulae and chemically peculiar R Coronae Borealis stars. Our proposed model also reveals that the 17.4um/18.9um band ratio in the metallofullerenes simulated IR spectra mainly depends on the metal abundances, ionization level, and endo/exo concentration in the circumstellar envelopes. We conclude that metallofullerenes are potential emitters contributing to the observed IR spectra in fullerene-rich circumstellar envelopes. Our simulated IR spectra indicate also that the James Webb Space Telescope has the potential to confirm or refute the presence of metallofullerenes (or even other fullerene-based species) in circumstellar enviroments., Comment: Accepted for publication in The Astrophysical Journal on 23 November 2022 (in press) (10 pages, 4 figures, and 1 tables)

Critically elastic materials - those that are rigid with a single state of self-stress - can be generated from parent systems with two states of self-stress by the removal of one of many constraints. We show that the elastic moduli of the resulting homogeneous and isotropic daughter systems are interrelated by a universal functional form parametrized by properties of the parent. In simulations of both spring networks and packings of soft spheres, judicious choice of parent systems and bond removal allows for the selection of a wide variety of moduli and Poisson's ratios in the critically elastic systems, providing a framework for versatile deterministic selection of mechanical properties.

Regarding the proximity of dark energy scale and neutrino mass, one can get new insight into the probe signatures of vector field dark energy component on neutrino flavor conversion. A vector field is assumed to be conformally coupled to various matter components through a screening paradigm. We show how the oscillation probabilities evolve differently with a finite-size matter density like the Sun for different values of relevant model parameters. Considering existing neutrino flavor conversion data, we point out how this non-standard coupling affects neutrino fluxes and also we compute the discrepancy in the total flux of solar electron-neutrinos., Comment: 20 pages, 12 figures, to be published in Physics Letters B