Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulant-interactive Transformer with Dimension-aware Fusion for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) is gaining traction as a critical tool for understanding human behavior and enabling a wide range of applications. Since data of different modalities might lie in completely distinct spaces, it is very challenging to perform effective fusion and analysis from asynchronous multimodal streams. Most of the previous works focused on aligned fusion, which is unpractical in real-world scenarios. The recent Multimodal Transformer (MulT) approach attends to model the correlations between elements from different modalities in an unaligned manner. However, it collects temporal information by self-attention transformer which is a sequence model, implying that interactions across distinct time steps are not sufficient. In this paper, we propose the Citculant-interactive Transformer Network with dimension-aware fusion (CITN-DAF), which enables parallel computation of different modalities among different time steps and alleviates inter-modal temporal sensitivity while preserving intra-modal semantic order. By incorporating circulant matrices into the cross-modal attention mechanism, CITN-DAF is aimed to examine all conceivable interactions between vectors of different modalities. In addition, a dimension-aware fusion method is presented, which projects feature representations into different subspaces for an in-depth fusion. We evaluate CITN-DAF on three commonly used sentiment analysis benchmarks including CMU-MOSEI, CMU-MOSI and IEMOCAP. Extensive experimental results reveal that CITN-DAF is superior in cross-modal semantic interactions and outperforms the state-of-the-art multimodal methods.
Volume-based Performance not Guaranteed by Promising Patch-based Results in Medical Imaging

Whole-body MRIs are commonly used to screen for early signs of cancer. In addition to the small size of tumours at onset, variations in individuals, tumour types, and MRI machines increase the difficulty of finding tumours in these scans. Using patches, rather than whole-body scans, to train a deep-learning-based segmentation model with a custom compound patch loss function, several augmentations, and additional synthetically generated training data to identify areas where there is a high probability of a tumour provided promising results at the patch-level. However, applying the patch-based model to the entire volume did not yield great results despite all of the state-of-the-art improvements, with over 50% of the tumour sections in the dataset being missed. Our work highlights the discrepancy between the commonly used patch-based analysis and the overall performance on the whole image and the importance of focusing on the metrics relevant to the ultimate user − in our case, the clinician. Much work remains to be done to bring state-of-the-art segmentation to the clinical practice of cancer screening.
Spatial Effects Analysis on Individual-Tree Aboveground Biomass in a Tropical Pinus kesiya var. langbianensis Natural Forest in Yunnan, Southwestern China

It is essential to analyze the spatial autocorrelation and heterogeneity of aboveground biomass (AGB). But it is difficult to accurately describe due to the lack of data in clear-cutting plots. Thus, measuring the AGB directly in a clear-cutting plot can provide a reference for accurately describing the spatial variation. Therefore, a 0.3-hectare clear-cutting sample plot of Pinus kesiya var. langbianensis natural forest was selected, and the AGB was calculated by each component. The intra-group variance was quantitatively described in terms of spatial heterogeneity, and the spatial autocorrelation was explored by global and local Moran’s I. The results indicated that (1) there was different spatial heterogeneity for the different trees and organs. The intra-group variance tended to be stable after 20 m for P. kesiya var. langbianensis (PK) and other upper trees (UPs) and after 10 m for the other lower trees (LTs). (2) The spatial autocorrelation of AGB and wood biomass was similar, while the bark biomass and foliage biomass were consistent. PK and other UPs also exhibited strong spatial autocorrelation, with maximum Moran’s I values of 0.1537 and 0.1644, respectively. (3) There was spatial heterogeneity in the different components except for the bark of PK. The lowest spatial heterogeneity was found for LT.
Reference Models of Satellite Segment Integration for NTN 5G

Currently, international communications organizations are carrying out a large amount of research on the use of satellite communications in the unified telecommunications of a new generation, related to the 5G and 6G generations. These studies were initiated by the SaT5G project, which is part of EU Horizon 2020 Research Program (FP-8). The concept of the SaT5G project was to develop cost-effective "plug and play" solutions for 5G satellite communications, which can enable telecom operators and service providers to accelerate the deployment of 5G in all geographical regions and at the same time create new and growing market opportunities for stakeholders in the satellite communications industry. This article analyzes the research results of several international organizations for standardization and development of telecommunications as ITU-R, 3GPP, and ETSI, and their applicability for the construction of satellite segment in the non-terrestrial network (NTN) in 5G integrated network.
3D Manifold Topology Based Medical Image Data Augmentation

Data augmentation is an effective and universal technique for improving the generalization performance of deep neural networks. Current data augmentation implementations usually involve geometric and photometric transformations. However, none of them considers the topological information in images, which is an important global invariant of the three-dimensional manifold. In our implementation, we design a novel method that finds the generator of the first homology group, i
Dispositional mindfulness and fatigue in Chinese nurses during the COVID-19 pandemic: a possible mediating role of sleep quality (preprint)

Purpose: During the COVID-19 epidemicin China, clinical nurses are at an elevated risk of suffering fatigue. This research sought to investigate the correlation between dispositional mindfulness and fatigue among nurses, as well as the potential mediation role of sleep quality in this relationship. Methods: This online cross-sectional survey of nurses was performed from August to September 2022 after the re-emergence of COVID-19 in China. The Mindful Attention Awareness Scale (MAAS), 14-item Fatigue Scale (FS-14), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess the levels of dispositional mindfulness, fatigue, and sleep quality, respectively. The significance of the mediation effect was determined through a bootstrap approach with SPSS PROCESS macro. Results: A total of 2143 nurses completed the survey. Higher levels of dispositional mindfulness were significantly negatively related to fatigue (r = -0.518, P < 0.001) and sleep disturbance (r = -0.344, P < 0.001). Besides, there was a positive relationship between insufficient sleep and fatigue (r = 0.547, P < 0.001). Analyses of mediation revealed that sleep quality partly mediated the correlation between dispositional mindfulness and fatigue (β= -0.551, 95% Confidence Interval = [-0.630, -0.474]). Conclusions: Chinese nurses' dispositional awareness was related to the reduction of fatigue during the COVID-19 pandemic, and this relationship indirectly operates through sleep quality. Intervention strategies and measures should be adapted to improve dispositional mindfulness and sleep quality to reduce fatigue in nurses during the pandemic.
On the bias of K-fold cross validation with stable learners

This paper investigates the efficiency of the K-fold cross-validation (CV) procedure and a debiased version thereof as a means of estimating the generalization risk of a learning algorithm. We work under the general assumption of uniform algorithmic stability. We show that the K-fold risk estimate may not be consistent under such general stability assumptions, by constructing non vanishing lower bounds on the error in realistic contexts such as regularized empirical risk minimisation and stochastic gradient descent. We thus advocate the use of a debiased version of the K-fold and prove an error bound with exponential tail decay regarding this version. Our result is applicable to the large class of uniformly stable algorithms, contrarily to earlier works focusing on specific tasks such as density estimation. We illustrate the relevance of the debiased K-fold CV on a simple model selection problem and demonstrate empirically the usefulness of the promoted approach on real world classification and regression datasets.
Distributionally Robust Policy Gradient for Offline Contextual Bandits

Learning an optimal policy from offline data is notoriously challenging, which requires the evaluation of the learning policy using data pre-collected from a static logging policy. We study the policy optimization problem in offline contextual bandits using policy gradient methods. We employ a distributionally robust policy gradient method, DROPO, to account for the distributional shift between the static logging policy and the learning policy in policy gradient. Our approach conservatively estimates the conditional reward distributional and updates the policy accordingly. We show that our algorithm converges to a stationary point with rate O (1 /T ) , where T is the number of time steps. We conduct experiments on real-world datasets under various scenarios of logging policies to compare our proposed algorithm with baseline methods in offline contextual bandits. We also propose a variant of our algorithm, DROPO-exp, to further improve the performance when a limited amount of online interaction is allowed. Our results demonstrate the effectiveness and robustness of the proposed algorithms, especially under heavily biased offline data.
The Entropy Density Behavior across a Plane Shock Wave

Entropy density behavior poses many problems when we study non-equilibrium situations. In particular, the local equilibrium hypothesis (LEH) has played a very important role and is taken for granted in non-equilibrium problems, no matter how extreme they are. In this paper we would like to calculate the Boltzmann entropy balance equation for a plane shock wave and show its performance for Grad’s 13-moment approximation and the Navier–Stokes–Fourier equations. In fact, we calculate the correction for the LEH in Grad’s case and discuss its properties.
Deep Value Function Networks for Large-Scale Multistage Stochastic Programs

A neural networks-based stagewise decomposition algorithm called Deep Value Function Networks (DVFN) is proposed for large-scale multi-stage stochastic programming (MSP) problems. Traditional approaches such as nested Benders decomposition and its stochastic variant, stochastic dual dynamic programming (SDDP) approximates value functions as piecewise linear convex functions by gradually accumulating subgradient cuts from dual solutions of stagewise subproblems. Although they have been proven effective for linear problems, nonlinear problems may suffer from the increasing number of subgradient cuts as they proceed. A recently developed algo-rithm called Value Function Gradient Learning (VFGL) replaced the piecewise linear approximation with parametric function approximation, but its performance heavily depends upon the choice of parametric forms like most of traditional parametric machine learning algorithms did. On the other hand, DVFN approximates value functions using neural networks, which are known to have huge capacity in terms of their functional representations. The art of choosing appropriate parametric form becomes a simple labor of hyperpa-rameter search for neural networks. However, neural networks are non-convex in general, and it would make the learning process unstable. We resolve this issue by using input convex neural networks that guarantee convexity with respect to inputs. We compare DVFN with SDDP and VFGL for solving large-scale linear and nonlinear MSP problems: production optimization and energy planning. Numerical examples clearly indicate that DVFN provide accurate and computationally efficient solutions.
Koopman Constrained Policy Optimization: A Koopman operator theoretic method for differentiable optimal control in robotics

We introduce Koopman Constrained Policy Optimization (KCPO), combining implicitly differentiable model predictive control with a deep Koop-man autoencoder for robot learning in unknown and nonlinear dynamical systems. KCPO is a new policy optimization algorithm that trains neural policies end-to-end with hard box constraints on controls. Guaranteed satisfaction of hard constraints helps ensure the performance and safety of robots. We perform imitation learning with KCPO to recover expert policies on the Simple Pendulum, Cartpole Swing-Up, Reacher, and Differential Drive environments, outperforming base-line methods in generalizing to out-of-distribution constraints in most environments after training.
Automated Identification of Authorial Styles

The problem of improvement of the software for authorial style identification is topical today and requires new approaches. The proposed approach consists in the use of the efficient classical and machine learning methods which ensure reliable data with a test validity of 95%. These are the following methods: the chi-square test and the discriminant analysis. The methods have been applied on the level of letters of Cyrillic alphabet which proved to be appropriate for an author identification task. Typical statistical characteristics have been established for Ukrainian authors’ styles. With the help of these characteristics, the author can be identified. The proposed structure of the software system is the novelty of the research. The developed software system is based on a modular principle. The algorithm of the author’s identification is realized on the Python programming language. The level of automation is high.
The grammar of parts and wholes in Yucatec: Volumes, surfaces, and curvature extremes

Overview • meronymy • the MesoSpace project • MesoSpace meronym tasks • Yucatec • findings • conclusions 2 Meronymy • meronyms-object-part designators • artifacts – Indo-European languages: labeling by function – Mesoamerican (MA) languages: labeling by form 3 English Yucatec Maya blade handle /hilt y=òok " its foot/leg " u=táan " its front " Figure 1. Categorizing parts by function vs. form Meronymy (cont.) • meronyms in Mesoamerica: productivity – used across large heterogeneous classes of objects – labeling any arbitrary geometrically defined part of any arbitrary object • cf. MacLaury 1989 for Ayoquesco Zapotec and Levinson 1994 for Tenejapa Tseltal (Mayan) 4 Figure 2. Productivity of MA meronyms: some uses of s=pat " its back " in Tseltal (Levinson 1994: 811) Meronymy (cont.) • what makes this productivity possible? – two proposals • global analogies (MacLaury) • shape-analytical algorithms (Levinson) 5 Meronymy (cont.) • MacLaury: Ayoquesco Zapotec meronymy operates on global analogical mapping – a set of seven body part terms are freely extended to non-human bodies and inanimates
An algorithm that constructs irreducible triangulations of once-punctured surfaces

A triangulation of a surface is irreducible if there is no edge whose contraction produces another triangulation of the surface. In this work we propose an algorithm that constructs the set of irreducible triangulations of any surface with precisely one boundary component. By implementing the algorithm on computer, we have found a list of 297 nonisomorphic combinatorial types of irreducible triangulations on the once-punctured torus.
Human and AI Trust: Trust Attitude Measurement Instrument Development

With the current progress of Artificial Intelligence (AI) technology and its increasingly broader applications, trust is seen as a required criterion for AI usage, acceptance, and deployment. A robust measurement instrument is essential to correctly evaluate trust from a human-centered perspective. This paper describes the development and validation process of a trust measure instrument, which follows psychometric principles, and consists of a 16-items trust scale. The instrument was built explicitly for research in human-AI interaction to measure trust attitudes towards AI systems from layperson (non-expert) perspective, in the context of AI medical support systems (specifically cancer/health prediction). The results of the six-stages evaluation show that the proposed trust measurement instrument is empirically reliable and valid for systematically measuring and comparing non-experts’ trust in AI medical support systems.
Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.
On Integral Equation Methods for Electromagnetic Scattering by Biperiodic Structures

In this thesis, we use integral equation methods to study the scattering of timeharmonic electromagnetic plane waves by biperiodic multilayered structures. Here, such structures are modeled by vertically arranging finitely many non-self-intersecting polyhedral Lipschitz interfaces. We distinguish single profile scatterers and scatterers composed of at least two surfaces. The electromagnetic scattering problem, which arises from the presence of obstacles of the first type, serves as a basic model that we reuse in the multilayered case. By treating it with a combined potential ansatz, we obtain an equivalent new periodic singular integral equation with Fredholm index zero. The Fredholmness is verified by Garding inequalities. This property is crucial for proving one of the main results of our analysis in the single interface setting: an existence theorem for the derived integral equation under certain assumptions on the electromagnetic material parameters. The second central result, a uniqueness theorem, is proven by a variational argumentation and again depends on the values of the electromagnetic material parameters. In the main part of this thesis, we then extend the considered obstacles to “real” multilayered obstacles consisting of at least two grating interfaces. For these, we propose two recursive integral equation algorithms to determine the generated reflected and transmitted waves. On the one hand, the recursive scattering matrix method and, on the other hand, a recursive algorithm based on the transmission conditions across the interfaces of the multilayered structure. In addition to their accurate derivation, new existence and uniqueness results, depending on the electromagnetic properties of the considered obstacle, are presented for each algorithm. Their proofs partially exploit the insights originating from the treatment of the single interface model problem. Our analytical investigation is intended to provide a basis for new efficient numerical implementations of the biperiodic multilayered electromagnetic scattering problem.
IN HIGH RESOLUTION MICROWAVE IMAGING

'Modern siectral estimation techniques offer higher angular resolution than Fourier-based methods in microwave imaging with large phased arrays. Two problems limit the applicability of these methods. Both are solved by data-adaptive feedback control of the weight vector of the array. The method is adaptive beamforming, developed for the radio camera. The problems are 1) intrinsic mechanical and electrical distortion in large systems of antennas and 2) the l ack of spatial stationarity in the radiation field from near-field targets. at X-band (3-cm wavelength) are presented.
Multi-Vehicle Function Tracking by Moment Matching

Multi-Vehicle Function Tracking by Moment Matching Trevor Avant Chair of the Supervisory Committee: Professor Kristi Morgansen Aeronautics & Astronautics The evolution of many natural and man-made environmental events can be represented as scalar functions of time and space. Examples include the boundary and intensity of wildfires, of waste spills in bodies of water, and of natural emissions of methane from the earth. The difficult task of understanding and monitoring these processes can be accomplished through the use of coordinated groups of vehicles. This thesis devises a method to determine positions of the members of a group of vehicles in the domain of a scalar function which lead to effective sensing of the function. This method involves equating the moments of a scalar function to the moments of a group of positions, which results in a system of polynomial equations to be solved. This methodology also allows for other explicit geometric constraints, in the form of polynomial equations, to be imposed on the vehicles. Several example simulations are shown to demonstrate the advantages and challenges associated with the moment matching technique.
Predicting the effect of instance representations on inductive learning

The ability to generalize from examples depends on the algorithm employed for learning and the instance representation used to describe the examples to the learning algorithm. Recently a considerable effort has been devoted to the design of algorithms that generalize well in a large number of situations. However, the effect of the instance representation on the accuracy of the generalizations made by the learning algorithm is poorly understood. This dissertation describes how the duality between finding a compact description for the examples and generalizing from the examples can be utilized to determine the suitability of an instance representation for a learning algorithm. In particular, a heuristic algorithm to compare representations, called ACR, is described. Given a learning algorithm, a set of examples, and alternative instance representations for the examples, ACR attempts to identify the instance representation that will enable the learning algorithm to produce the most accurate hypothesis. For each instance representation, ACR estimates the minimum number of bits with which the learning algorithm can express the examples. Experiments with a variety of learning tasks show that ACR is effective in ranking representations. In addition, ACR was found to rank representations faster than rankings obtained by directly estimating the accuracy of the hypotheses produced with different representations. The conclusion that one representation is better than another because it improves the compressibility of the examples suggests that different methods for improving the compressibility of the examples should result in different techniques for improving representations. Two types of representation change are identified by analyzing the reason for change in compressibility of the examples with the different representations.