The complexity characteristics of texts written in natural languages are significantly related to the rules of punctuation. In particular, the distances between punctuation marks measured by the number of words quite universally follow the family of Weibull distributions known from survival analyses. However, the values of two parameters marking specific forms of these distributions distinguish specific languages. This is such a strong constraint that the punctuation distributions of texts translated from the original language into another adopt quantitative characteristics of the target language. All these changes take place within Weibull distributions such that the corresponding hazard functions are always increasing. Recent previous research shows that James Joyce's famous "Finnegans Wake" is subject to such extreme distribution from the Weibull family that the corresponding hazard function is clearly decreasing. At the same time, the distances of sentence ending punctuation marks, determining the variability of sentence length, have an almost perfect multifractal organization, so far to such an extent found nowhere else in the literature. In the present contribution based on several available translations (Dutch, French, German, Polish, Russian) of "Finnegans Wake", it is shown that the punctuation characteristics of this work remain largely translation invariant, contrary to the common cases. These observations may constitute further evidence that "Finnegans Wake" is a translinguistic work in this respect as well, in line with Joyce's original intention.

In recent years, artificial intelligence (AI) has become an integral part of daily life, serving as a transformative tool across various professional domains [1] and driving personal applications through advancements in transformer models that power large language models (LLMs) [2]. However, both training and inference of AI models demand substantial computational and energy resources, which are becoming increasingly challenging to access [3, 4]. While server-class GPUs are effective for training, their energy inefficiency [5] and high costs present significant barriers [6]. Additionally, the environmental impact of energy-intensive AI systems has raised critical concerns about their role in exacerbating climate change [4]. Amdahl's law predicts that performance and efficiency gains are best achieved through innovative application-specific accelerator architectures rather than scaling up multi-core general-purpose processors [7]. Consequently, applicationspecific integrated circuits (ASICs), both digital and analog, have emerged as critical solutions for enabling highefficiency training and inference of artificial neural networks [7, 8, 9]. Digital accelerators are widely adopted for training workloads. Notable examples include the Brainwave Neural Processing Unit (NPU) [10], Google's Tensor Processing Unit (TPU) [11], and low-precision inference accelerators such as YodaNN [5], the Unified Neural Processing Unit (UNPU) [12], and BRein Memory [13].

Representation learning from graphs has been an active research area over the past decade. DeepWalk [24], one of the pioneering approaches in this field, learns node embeddings by generating random walks on the graph. A standard and highly efficient method for optimizing the embedding objective is to train a binary classification model that distinguishes between positive and negative node pairs, known as the negative sampling approach. Positive pairs are generated by applying the skip-gram model [21] to the node sequences and represent nodes whose embeddings should be similar, in contrast to negative pairs. Many works have since extended the original DeepWalk algorithm in three main directions: i) presenting different (random) walk strategies for traversing the graph and generating node sequences [1, 9, 10, 25, 29, 31, 39, 41], ii) designing new embedding learning models [1, 2, 11, 12, 26], and iii) designing new techniques for negative pair sampling [3, 16, 19, 35]. Inspired by observations on real graphs, we take a different approach and propose a general regularization technique that adjusts the frequency distribution of positive node pairs. In the standard negative sampling setting, when a positive pair u, v is generated, we also sample k 1 negative pairs u, x, where the node x is selected at random from some distribution on the graph nodes.

Early detection of patient deterioration is essential for timely treatment, with vital signs like heart rates being key health indicators. Existing methods tend to solely analyze vital sign waveforms, ignoring transition relationships of waveforms within each vital sign and the correlation strengths among various vital signs. Such studies often overlook nuanced illness deterioration, which is the early sign of worsening health but is difficult to detect. In this paper, we introduce CAND, a novel method that organizes the transition relationships and the correlations within and among vital signs as domain-specific and cross-domain knowledge. CAND jointly models these knowledge in a unified representation space, considerably enhancing the early detection of nuanced illness deterioration. In addition, CAND integrates a Bayesian inference method that utilizes augmented knowledge from domain-specific and cross-domain knowledge to address the ambiguities in correlation strengths. With this architecture, the correlation strengths can be effectively inferred to guide joint modeling and enhance representations of vital signs. This allows a more holistic and accurate interpretation of patient health. Our experiments on a real-world ICU dataset demonstrate that CAND significantly outperforms existing methods in both effectiveness and earliness in detecting nuanced illness deterioration. Moreover, we conduct a case study for the interpretable detection process to showcase the practicality of CAND.

In machine learning, Riemannian manifolds offer a useful abstraction for approximating commonly encountered, non-Euclidean empirical data distributions and optimization state spaces. While Euclidean machine learning algorithms have been adapted to Riemannian manifolds, these adaptations rely on computationally intensive differential-geometric primitives, such as exponential maps and parallel transports. Here, we present a framework for efficiently approximating differentialgeometric primitives on arbitrary manifolds via construction of an atlas graph representation, which leverages the canonical characterization of a manifold as a finite collection, or atlas, of overlapping coordinate charts. We first show the utility of this framework in a setting where the manifold is expressed in closed form, specifically, a runtime advantage, compared with state-of-the-art approaches, for first-order optimization over the Grassmann manifold. Moreover, using point cloud data for which a complex manifold structure was previously established, i.e., high-contrast image patches, we show that an atlas graph with the correct geometry can be directly learned from the point cloud. Finally, we demonstrate that learning an atlas graph enables downstream key machine learning tasks. In particular, we implement a Riemannian generalization of support vector machines that uses the learned atlas graph to approximate complex differential-geometric primitives, including Riemannian logarithms and vector transports. These settings suggest the potential of this framework for even more complex settings, where ambient dimension and noise levels may be much higher. Taken together, our results speed up and render more broadly applicable Riemannian optimization routines at the forefront of modern data science and machine learning.

Understanding structure-property relationships is an essential yet challenging aspect of materials discovery and development. To facilitate this process, recent studies in materials informatics have sought latent embedding spaces of crystal structures to capture their similarities based on properties and functionalities. However, abstract feature-based embedding spaces are human-unfriendly and prevent intuitive and efficient exploration of the vast materials space. Here we introduce Contrastive Language--Structure Pre-training (CLaSP), a learning paradigm for constructing crossmodal embedding spaces between crystal structures and texts. CLaSP aims to achieve material embeddings that 1) capture property- and functionality-related similarities between crystal structures and 2) allow intuitive retrieval of materials via user-provided description texts as queries. To compensate for the lack of sufficient datasets linking crystal structures with textual descriptions, CLaSP leverages a dataset of over 400,000 published crystal structures and corresponding publication records, including paper titles and abstracts, for training. We demonstrate the effectiveness of CLaSP through text-based crystal structure screening and embedding space visualization.

Causal discovery --the process of identifying cause-and-effect relationships from observational data-- is a pivotal challenge in artificial intelligence (AI) and machine learning. Unveiling causal structures enables robust predictions, facilitates counterfactual reasoning, and enhances decision-making processes in complex systems [1]. Traditional methods for causal discovery often rely on statistical tests for independence and structural equation modeling, which may not scale efficiently with high-dimensional data or effectively capture intricate non-linear relationships [2, 3]. In recent years, machine learning models, particularly deep learning architectures, have achieved remarkable success in predictive tasks. However, these models are typically considered "black boxes" due to their lack of interpretability. This opacity has led to a growing interest in explainable AI (XAI) techniques, with Shapley values emerging as a prominent method for interpreting model predictions [4]. Shapley values, grounded in cooperative game theory, provide a principled approach to attributing the contribution of each feature to the output of a model by quantifying the average marginal contribution of a feature across all possible subsets of features [5]. While Shapley values offer valuable insights into feature importance within a model's predictive framework, the link between feature importance and causal influence is non-trivial.

Context: Empirical Software Engineering (ESE) drives innovation in SE through qualitative and quantitative studies. However, concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on SE. Objective: To analyze three decades of SE research, identify mistakes in statistical methods, and evaluate experts' ability to detect and address these issues. Methods: We conducted a literature survey of ~27,000 empirical studies, using LLMs to classify statistical methodologies as adequate or inadequate. Additionally, we selected 30 primary studies and held a workshop with 33 ESE experts to assess their ability to identify and resolve statistical issues. Results: Significant statistical issues were found in the primary studies, and experts showed limited ability to detect and correct these methodological problems, raising concerns about the broader ESE community's proficiency in this area. Conclusions. Despite our study's eventual limitations, its results shed light on recurring issues from promoting information copy-and-paste from past authors' works and the continuous publication of inadequate approaches that promote dubious results and jeopardize the spread of the correct statistical strategies among researchers. Besides, it justifies further investigation into empirical rigor in software engineering to expose these recurring issues and establish a framework for reassessing our field's foundation of statistical methodology application. Therefore, this work calls for critically rethinking and reforming data analysis in empirical software engineering, paving the way for our work soon.

Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63\% accuracy for predicting the next day and 64\%, 67\% and 82\% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72\% to 2.85\% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.

Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their focus to pairwise interactions rather than sequences. Building on the connection between Autoregressive Moving Average (ARMA) and SSM, in this paper, we introduce GRAMA, a Graph Adaptive method based on a learnable Autoregressive Moving Average (ARMA) framework that addresses these limitations. By transforming from static to sequential graph data, GRAMA leverages the strengths of the ARMA framework, while preserving permutation equivariance. Moreover, GRAMA incorporates a selective attention mechanism for dynamic learning of ARMA coefficients, enabling efficient and flexible long-range information propagation. We also establish theoretical connections between GRAMA and Selective SSMs, providing insights into its ability to capture long-range dependencies. Extensive experiments on 14 synthetic and real-world datasets demonstrate that GRAMA consistently outperforms backbone models and performs competitively with state-of-the-art methods.