Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability. Although emerging foundation models aim for broader applicability, their rigid dependence on fixed, high-density multi-channel montages restricts their use across heterogeneous datasets and in missing-channel or practical low-channel settings. To address these limitations, we introduce SingLEM, a self-supervised foundation model that learns robust, general-purpose representations from single-channel EEG, making it inherently hardware agnostic. The model employs a hybrid encoder architecture that combines convolutional layers to extract local features with a hierarchical transformer to model both short- and long-range temporal dependencies. SingLEM is pretrained on 71 public datasets comprising over 9,200 subjects and 357,000 single-channel hours of EEG. When evaluated as a fixed feature extractor across six motor imagery and cognitive tasks, aggregated single-channel representations consistently outperformed leading multi-channel foundation models and handcrafted baselines. These results demonstrate that a single-channel approach can achieve state-of-the-art generalization while enabling fine-grained neurophysiological analysis and enhancing interpretability. The source code and pretrained models are available at this https URL.

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.

The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what 'good generalisation' entails and how it should be evaluated is not well understood, nor are there any evaluation standards for generalisation. In this paper, we lay the groundwork to address both of these issues. We present a taxonomy for characterising and understanding generalisation research in NLP. Our taxonomy is based on an extensive literature review of generalisation research, and contains five axes along which studies can differ: their main motivation, the type of generalisation they investigate, the type of data shift they consider, the source of this data shift, and the locus of the shift within the modelling pipeline. We use our taxonomy to classify over 400 papers that test generalisation, for a total of more than 600 individual experiments. Considering the results of this review, we present an in-depth analysis that maps out the current state of generalisation research in NLP, and we make recommendations for which areas might deserve attention in the future. Along with this paper, we release a webpage where the results of our review can be dynamically explored, and which we intend to update as new NLP generalisation studies are published. With this work, we aim to take steps towards making state-of-the-art generalisation testing the new status quo in NLP.

Controlling magnetism of transition metal atoms by pairing with $\pi$ electronic states of graphene is intriguing. Herein, through first - principle computation we explore the possibility of switching magnetization by forming the tetrahedral $sp^3$ - metallic $d$ hybrid bonds. Graphene multilayers capped by single - layer cobalt atoms can transform into the $sp^3$ - bonded diamond films upon the hydrogenation of the bottom surface. While the conversion is favored by hybridization between the $sp^3$ dangling bonds and metallic $d_{z^2}$ states, such a strong hybridization can lead to the reorientation of magnetization easy axis of cobalt adatoms in plane to perpendicular. The further investigations identify that this anisotropic magnetization even can be modulated upon the change in charge carrier density, suggesting the possibility of an electric - field control of magnetization reorientation. These results provide a novel alternative that would represent tailoring magnetism by means of degree of the interlayer hybrid bonds in the layered materials.

This paper provides a detailed expository and computational account of the elementary methods developed by P. L. Chebyshev and J. J. Sylvester to establish explicit bounds on the prime counting function. The core of the method involves replacing the Möbius function with a finitely supported arithmetic function in the convolution identities, relating the Chebyshev function psi(x) to the summatory logarithm function T(x) = log([x]!). We present a comprehensive analysis of the various schemes proposed by Chebyshev and Sylvester, with a central focus on Sylvester's innovative iterative refinement procedure. By implementing this procedure computationally, we replicate, verify, and optimize the historical results, providing a self-contained pedagogical resource for this pivotal technique in analytic number theory.

The popular subword tokenizers of current language models, such as Byte-Pair Encoding (BPE), are known not to respect morpheme boundaries, which affects the downstream performance of the models. While many improved tokenization algorithms have been proposed, their evaluation and cross-comparison is still an open problem. As a solution, we propose a combined intrinsic-extrinsic evaluation framework for subword tokenization. Intrinsic evaluation is based on our new UniMorph Labeller tool that classifies subword tokenization as either morphological or alien. Extrinsic evaluation, in turn, is performed via the Out-of-Vocabulary Generalization Challenge 1.0 benchmark, which consists of three newly specified downstream text classification tasks. Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that, in all language models studied (including ALBERT, BERT, RoBERTa, and DeBERTa), alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings.

This study introduces marginal density functions of the general Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process. In the case of the Bayesian MS-VAR process, we provide closed-form density functions and Monte-Carlo simulation algorithms, including the importance sampling method. The Monte-Carlo simulation method departs from the previous simulation methods because it removes the duplication in a regime vector.

Recent advancements in medical image analysis have predominantly relied on Convolutional Neural Networks (CNNs), achieving impressive performance in chest X-ray classification tasks, such as the 92% AUC reported by AutoThorax-Net and the 88% AUC achieved by ChexNet in classifcation tasks. However, in the medical field, even small improvements in accuracy can have significant clinical implications. This study explores the application of Vision Transformers (ViT), a state-of-the-art architecture in machine learning, to chest X-ray analysis, aiming to push the boundaries of diagnostic accuracy. I present a comparative analysis of two ViT-based approaches: one utilizing full chest X-ray images and another focusing on segmented lung regions. Experiments demonstrate that both methods surpass the performance of traditional CNN-based models, with the full-image ViT achieving up to 97.83% accuracy and the lung-segmented ViT reaching 96.58% accuracy in classifcation of diseases on three label and AUC of 94.54% when label numbers are increased to eight. Notably, the full-image approach showed superior performance across all metrics, including precision, recall, F1 score, and AUC-ROC. These findings suggest that Vision Transformers can effectively capture relevant features from chest X-rays without the need for explicit lung segmentation, potentially simplifying the preprocessing pipeline while maintaining high accuracy. This research contributes to the growing body of evidence supporting the efficacy of transformer-based architectures in medical image analysis and highlights their potential to enhance diagnostic precision in clinical settings.

A serious threat today is malicious executables. It is designed to damage computer system and some of them spread over network without the knowledge of the owner using the system. Two approaches have been derived for it i.e. Signature Based Detection and Heuristic Based Detection. These approaches performed well against known malicious programs but cannot catch the new malicious programs. Different researchers have proposed methods using data mining and machine learning for detecting new malicious programs. The method based on data mining and machine learning has shown good results compared to other approaches. This work presents a static malware detection system using data mining techniques such as Information Gain, Principal component analysis, and three classifiers: SVM, J48, and Na\"ive Bayes. For overcoming the lack of usual anti-virus products, we use methods of static analysis to extract valuable features of Windows PE file. We extract raw features of Windows executables which are PE header information, DLLs, and API functions inside each DLL of Windows PE file. Thereafter, Information Gain, calling frequencies of the raw features are calculated to select valuable subset features, and then Principal Component Analysis is used for dimensionality reduction of the selected features. By adopting the concepts of machine learning and data-mining, we construct a static malware detection system which has a detection rate of 99.6%.

The processes $X(3872)\to D^{*0}\bar{D^{0}}+c.c.,~\gamma J/\psi,~\gamma \psi(2S),$and$\gamma D^{+}D^{-}$are searched for in a$9.0~\rm fb^{-1}$ data sample collected at center-of-mass energies between $4.178$ and $4.278$ GeV with the BESIII detector. We observe $X(3872)\to D^{*0}\bar{D^{0}}+c.c.$ and find evidence for $X(3872)\to\gamma J/\psi$ with statistical significances of $7.4\sigma$ and $3.5\sigma$, respectively. No evident signals for $X(3872)\to\gamma\psi(2S)$ and $\gamma D^{+}D^{-}$ are found, and upper limit on the relative branching ratio $R_{\gamma \psi} \equiv\frac{\mathcal{B}(X(3872)\to\gamma\psi(2S))}{\mathcal{B}(X(3872)\to\gamma J/\psi)}<0.59$is set at 90$\%$ confidence level. Measurements of branching ratios relative to decay $X(3872)\to\pi^+\pi^- J/\psi$ are also reported for decays $X(3872)\to D^{*0}\bar{D^{0}}+c.c., ~\gamma\psi(2S),~\gamma J/\psi$, $\gamma D^{+}D^{-}$, as well as the non-$D^{*0}\bar{D}^{0}$ three-body decays $\pi^0 D^{0}\bar{D}^{0}$ and $\gamma D^{0}\bar{D}^{0}$.

We calculated the infrared conductivity spectrum of orbitally ordered LaMnO$_3$ in phonon frequency and overtone frequency ranges. We considered orbital exchange, Jahn-Teller electron-phonon coupling, and phonon--phonon coupling. The fundamental excitation of the phonon-coupled orbiton was only Raman active, not infrared active, while its overtone modes were both Raman and infrared active. Our calculations reproduced the small peaks near 1300 cm$^{-1}$ observed both in Raman scattering and infrared conductivity spectra, as consistent with previous experimental results.

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

In NLP, models are usually evaluated by reporting single-number performance scores on a number of readily available benchmarks, without much deeper analysis. Here, we argue that - especially given the well-known fact that benchmarks often contain biases, artefacts, and spurious correlations - deeper results analysis should become the de-facto standard when presenting new models or benchmarks. We present a tool that researchers can use to study properties of the dataset and the influence of those properties on their models' behaviour. Our Text Characterization Toolkit includes both an easy-to-use annotation tool, as well as off-the-shelf scripts that can be used for specific analyses. We also present use-cases from three different domains: we use the tool to predict what are difficult examples for given well-known trained models and identify (potentially harmful) biases and heuristics that are present in a dataset.

In this paper, we have studied option pricing methods that are based on a Bayesian Markov-Switching Vector Autoregressive (MS-BVAR) process using a risk-neutral valuation approach. A BVAR process, which is a special case of the Bayesian MS-VAR process is widely used to model interdependencies of economic variables and forecast economic variables. Here we assumed that a regime-switching process is generated by a homogeneous Markov process and a residual process follows a conditional heteroscedastic model. With a direct calculation and change of probability measure, for some frequently used options, we derived pricing formulas. An advantage of our model is it depends on economic variables and is easy to use compared to previous option pricing papers, which depend on regime-switching.

In this study, we introduce new estimation methods for the required rate of returns on equity and liabilities of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We also provide a method that evaluates the market values of liabilities. We apply the model to a set of firms from the S\&P 500 index using historical dividend and price data over a 32--year period. Overall, the suggested methods can be used to estimate the required rate of returns.

Using initial-state radiation events from a total integrated luminosity of 11.957 fb$^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 3.773 and 4.258 GeV with the BESIII detector at BEPCII, the cross section for the process $e^{+}e^{-}\rightarrow\Lambda\bar{\Lambda}$ is measured in 16 $\Lambda\bar{\Lambda}$ invariant mass intervals from the production threshold up to 3.00 GeV$/c^{2}$. The results are consistent with previous results from BaBar and BESIII, but with better precision and with narrower $\Lambda\bar{\Lambda}$ invariant mass intervals than BaBar.

Data reuse is fundamental for reducing the data integration effort required to build data supporting new applications, especially in data scarcity contexts. However, data reuse requires to deal with data heterogeneity, which is always present in data coming from different sources. Such heterogeneity appears at different levels, like the language used by the data, the structure of the information it represents, and the data types and formats adopted by the datasets. Despite the valuable insights gained by reusing data across contexts, dealing with data heterogeneity is still a high price to pay. Additionally, data reuse is hampered by the lack of data distribution infrastructures supporting the production and distribution of quality and interoperable data. These issues affecting data reuse are amplified considering cross-country data reuse, where geographical and cultural differences are more pronounced. In this paper, we propose LiveData, a cross-country data distribution network handling high quality and diversity-aware data. LiveData is composed by different nodes having an architecture providing components for the generation and distribution of a new type of data, where heterogeneity is transformed into information diversity and considered as a feature, explicitly defined and used to satisfy the data users purposes. This paper presents the specification of the LiveData network, by defining the architecture and the type of data handled by its nodes. This specification is currently being used to implement a concrete use case for data reuse and integration between the University of Trento (Italy) and the National University of Mongolia.

In this paper, we developed the Merton's structural model for public companies under an assumption that liabilities of the companies are observed. Using Campbell and Shiller's approximation method, we obtain formulas of risk-neutral equity and liability values and default probabilities for the public companies. Also, the paper provides ML estimators of suggested model's parameters.

In this paper, we introduce a dynamic Gordon growth model, which is augmented by a time--varying spot interest rate and the Gordon growth model for dividends. Using the risk--neutral valuation method and locally risk--minimizing strategy, we obtain pricing and hedging formulas for the dividend--paying European call and put options and equity--linked life insurance products. Also, we provide ML estimator of the model.

Smartphones enable understanding human behavior with activity recognition to support people's daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people's daily lives and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and over 216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries.