Shaukat Ali is a chief research scientist, research professor, and head of department at Simula Research Laboratory, Oslo, Norway. Tao Yue is an adjunct research scientist at Simula Research Laboratory, Oslo, Norway. Rui Abreu is a professor at the University of Porto, Portugal.

We demonstrate and discuss nonasymptotic bounds in probability for the cost of a regression scheme with a general loss function from the perspective of the Rademacher theory, and for the optimality with respect to the average $L^{2}$-distance to the underlying conditional expectations of least squares regression outcomes from the perspective of the Vapnik-Chervonenkis theory. The results follow from an analysis involving independent but possibly nonstationary training samples and can be extended, in a manner that we explain and illustrate, to relevant cases in which the training sample exhibits dependence.

Speaking behind a podium bearing the Ukrainian state emblem, President Volodymyr Zelenskiy, in his now signature green attire, calls on his soldiers to lay down their weapons and return to their families. The one-minute clip is a deepfake, the term for a sophisticated hoax that uses artificial intelligence to create a phoney image, most commonly fake videos of people. A deepfake of Ukrainian President Volodymyr Zelensky calling on his soldiers to lay down their weapons was reportedly uploaded to a hacked Ukrainian news website today, per @Shayan86 pic.twitter.com/tXLrYECGY4 What unfolded next was the latest episode in the infowar that has accompanied the Russia-Ukraine conflict, a war being waged across social media platforms, via satellite images of battlefields and on hackers' keyboards. Zelenskiy posted a bona fide response on his Instagram account on Wednesday dismissing the "childish provocation" and telling Russian troops to return home.

In this paper with study phase transitions of the $q$-state Potts model, through a number of unsupervised machine learning techniques, namely Principal Component Analysis (PCA), $k$-means clustering, Uniform Manifold Approximation and Projection (UMAP), and Topological Data Analysis (TDA). Even though in all cases we are able to retrieve the correct critical temperatures $T_c(q)$, for $q = 3, 4$ and $5$, results show that non-linear methods as UMAP and TDA are less dependent on finite size effects, while still being able to distinguish between first and second order phase transitions. This study may be considered as a benchmark for the use of different unsupervised machine learning algorithms in the investigation of phase transitions.

Data-driven methods are becoming an essential part of computational mechanics due to their unique advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-form approximations. However, imposing the physics-based mathematical requirements that any material model must comply with is not straightforward for data-driven approaches. In this study, we use a novel class of neural networks, known as neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy function with respect to the deformation gradient, a condition needed for the existence of minimizers for boundary value problems in elasticity. We take advantage of the properties of ordinary differential equations to create monotonic functions that approximate the derivatives of the strain energy function with respect to the invariants of the right Cauchy-Green deformation tensor. The monotonicity of the derivatives guarantees the convexity of the energy. The N-ODE material model is able to capture synthetic data generated from closed-form material models, and it outperforms conventional models when tested against experimental data on skin, a highly nonlinear and anisotropic material. We also showcase the use of the N-ODE material model in finite element simulations. The framework is general and can be used to model a large class of materials. Here we focus on hyperelasticity, but polyconvex strain energies are a core building block for other problems in elasticity such as viscous and plastic deformations. We therefore expect our methodology to further enable data-driven methods in computational mechanics

Pre-trained language models such as BERT have been successful at tackling many natural language processing tasks. However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding - BPE) are sub-optimal at handling morphologically rich languages. Even given a morphological analyzer, naive sequencing of morphemes into a standard BERT architecture is inefficient at capturing morphological compositionality and expressing word-relative syntactic regularities. We address these challenges by proposing a simple yet effective two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality. Despite the success of BERT, most of its evaluations have been conducted on high-resource languages, obscuring its applicability on low-resource languages. We evaluate our proposed method on the low-resource morphologically rich Kinyarwanda language, naming the proposed model architecture KinyaBERT. A robust set of experimental results reveal that KinyaBERT outperforms solid baselines by 2% in F1 score on a named entity recognition task and by 4.3% in average score of a machine-translated GLUE benchmark. KinyaBERT fine-tuning has better convergence and achieves more robust results on multiple tasks even in the presence of translation noise.

SER models built on such limited sized corpora don't generalize Alongside acoustic information, linguistic features based on well to out-of-domain speech. Second, while previous studies speech transcripts have been proven useful in Speech Emotion proposed using ASR to generate transcripts for SER [5], ASR Recognition (SER). However, due to the scarcity of on emotional speech can often result in relatively high error emotion labelled data and the difficulty of recognizing emotional rates. Previous research has shown that emotion in speech speech, it is hard to obtain reliable linguistic features degrades ASR performance, with emotional speech assumed and models in this research area. In this paper, we propose to be a distortion of neutral speech [6]. However, with the to fuse Automatic Speech Recognition (ASR) outputs into advancement of deep learning technologies, transfer learning the pipeline for joint training SER. The relationship between for SER from ASR and joint training of ASR and SER have ASR and SER is understudied, and it is unclear what and recently emerged [7, 8]. Nevertheless, the relationship between how ASR features benefit SER. By examining various ASR ASR and SER is still poorly studied, particularly what outputs and fusion methods, our experiments show that in and how ASR features can benefit SER.

The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic ATL, hence ATL∗, under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we investigate a notion of bounded recall under incomplete information. We present a novel three-valued semantics for ATL∗ in this setting and analyse the corresponding model checking problem. We show that the three-valued semantics here introduced is an approximation of the classic two-valued semantics, then give a sound, albeit partial, algorithm for model checking two-valued perfect recall via its approximation as three-valued bounded recall. Finally, we extend MCMAS, an open-source model checker for ATL and other agent specifications, to incorporate bounded recall; we illustrate its use and present experimental results.

In development for more than a year, Auto-Itemization from TripActions Liquid is a solution that automatically splits transactions and attributes each line item to specific expense policies. The technology allows a user to upload a receipt for automatic itemisation; using AI, foreign language translation and fuzzy matching, each line item is categorised and assigned to a specific policy with 90 per cent accuracy. The use of machine learning also ensures that accuracy will increase over time. Single transactions often consist of multiple parts or line items -- like variable daily service charges for a hotel stay -- that need to be reported independently for accounting and tax purposes. The cost of the room needs to align with a company's hotel policy, while the room service that employee orders can align to a per diem for meal costs.

Imagine you live in a time before the internet. When we all had to work in offices, go to shops to buy things, when TV couldn't be streamed on demand and when most monetary transactions were made using notes and coins. Now imagine someone coming up to you and telling you how something that didn't even exist was going to make all those things seem like ancient history. You'd have found the idea fantastical, laughable even – exactly the way you might feel about the metaverse and the fact that there's a real estate boom going on there right now. Prices have risen by as much as 500% since Facebook changed the name of its holding company to Meta in October 2021, with people paying millions of dollars to buy plots of pixellated land in this virtual world, even though it doesn't fully exist yet.