Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Evolution of Poststroke Cognitive Impairment Using the Montreal Cognitive Assessment

Background and Purpose— The Montreal Cognitive Assessment (MoCA) is nowadays recommended for the screening of poststroke cognitive impairment. However, little is known about the temporal evolution of MoCA-assessed cognition after stroke. The objective of this study was to examine the temporal pattern of overall and domain-specific cognition at 2 and 6 months after stroke using the MoCA and to identify patient groups at risk for cognitive impairment at 6 months after stroke. Methods— Prospective cohort study in which 324 patients were administered the MoCA at 2 and 6 months post stroke. Cognitive impairment was defined as MoCA<26. Differences in cognitive impairment rates between 2 and 6 months post stroke were analyzed in different subgroups. Patients with MoCA score <26 at 2 months, who improved by ≥2 points by 6 months, were defined as reverters. Logistic regression analyses were used to identify determinants of (1) cognitive impairment at 6 months post stroke and (2) reverter status. Results— Between 2 and 6 months post stroke, mean MoCA score improved from 23.7 (3.9) to 24.7 (3.5), P<0.001. Prevalence of cognitive impairment at 2 months was 66.4%, compared with 51.9% at 6 months (P<0.001). More comorbidity and presence of cognitive impairment at 2 months were significant independent predictors of cognitive impairment 6 months post stroke. No significant determinants of reverter status were identified. Conclusions— Although cognitive improvement is seen ⩽6 months post stroke, long-term cognitive deficits are prevalent. Identifying patients at risk of cognitive impairment is, therefore, important as well as targeting interventions to this group.
Proceedings of the Annual Meeting of the Cognitive Science Society

The extent to which people can infer new mathematical con- cepts in the absence of cultural support is not clear. We test such learning with a simple math concept: additive commuta- tivity. Experimental work with children in industrialized cultures suggests that cultural support is necessary, since children take time to learn commutativity and ultimately show signs of knowing it after entering school. However, children are at a disadvantage in learning because they are not yet cognitively mature. Moreover, they have only had a short time to experi- ence the world and possibly learn principles like commutativity on their own. Unschooled adults, on the other hand, may be in a better position to have inferred commutativity on their own. We test indigneous Amazonians with variable levels of math cultural supports, and find that those with low cultural supports do not show signs of knowing additive commutativ- ity.
Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires $O(ndrq+n(rq)^{2})$ for an $(n\times d)$ input matrix, in which $q$ is a parameter with a small value, $r$ is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from $O(rnd)$ to $O(r^{2}n)$ per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications.
Achieving Massive MIMO Gains in FDD Downlink Systems Without CSI Feedback

The need for channel state information (CSIT) is crucial for the improved spectral efficiency of massive multiple-input multiple-output (MIMO) systems. In FDD massive MIMO systems, CSIT is obtained through downlink channel estimation and user feedback, but this process becomes challenging as the number of antennas increases, resulting in reduced spectral efficiency. In this paper, we show that even in FDD systems, the TDD massive MIMO gain is attainable without explicit CSIT training and feedback, by using UL pilots. We present a novel DL channel reconstruction method from uplink pilots and a robust downlink precoding technique, proving that the FDD massive MIMO gains are achievable without CSI training and feedback. Our results are verified through system-level simulations.
CAMPAIGNS: THE ESSENCE OF OPERATIONAL WARFARE

Abstract : On 6 June 1944, American and British forces stormed across the Normandy beaches to begin Operation Overlord, the far-reaching campaign for the liberation of Europe. The unqualified success of that bold venture, which led to the defeat of Nazi Germany, stressed the importance of a well-designed and well-executed campaign as an adjunct to operational warfare. Yet, shortly after the Second World War, this notion of warfare with its associated campaigns largely disappeared from contemporary military thinking. More recently, however, a resurgent interest in operational warfare as an essential step in achieving overall victory in war has evolved among military leaders. Unfortunately, somewhat less attention has been placed on its indivisible component -- the campaign. To better convey the notion of campaigning, this essay delves into a series of campaign fundamentals from four points of view. First, numerous campaigns of the Second World War, in Europe, the Pacific, and Africa, illustrate the elements of war that must be amalgamated to produce victory. Although Overlord was but one of these memorable campaigns, a brief review of its salient points provides a useful historical perspective of a campaign. Second, a precise definition of operational warfare and its companion, the campaign, serves to simplify many of the complexities inherent in these terms. Third, a comparison of campaign planning with other planning functions further delineates the unique wartime role of the campaign. Finally, by examining some of the campaign's major components -- the mission, the concept of operations, and the logistics concept -- we will see these disparate components quickly coalesce under the unifying rubric of the campaign.
SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocular Object Pose Estimation

Recently, self-supervised 6D object pose estimation, where synthetic images with object poses (sometimes jointly with un-annotated real images) are used for training, has attracted much attention in computer vision. Some typical works in literature employ a time-consuming differentiable renderer for object pose prediction at the training stage, so that (i) their performances on real images are generally limited due to the gap between their rendered images and real images and (ii) their training process is computationally expensive. To address the two problems, we propose a novel Network for Self-supervised Monocular Object pose estimation by utilizing the predicted Camera poses from unannotated real images, called SMOC-Net. The proposed network is explored under a knowledge distillation framework, consisting of a teacher model and a student model. The teacher model contains a backbone estimation module for initial object pose estimation, and an object pose refiner for refining the initial object poses using a geometric constraint (called relative-pose constraint) derived from relative camera poses. The student model gains knowledge for object pose estimation from the teacher model by imposing the relative-pose constraint. Thanks to the relative-pose constraint, SMOC-Net could not only narrow the domain gap between synthetic and real data but also reduce the training cost. Experimental results on two public datasets demonstrate that SMOC-Net outperforms several state-of-the-art methods by a large margin while requiring much less training time than the differentiable-renderer-based methods.
A Computational Design of Unimodular Complementary and Z-complementary Sets

Iterative Twisted Approximation (ITROX), proposed in 2012 by Soltanalian et al., is an interesting algorithm for designing sequences with desired correlation properties. However, when aperiodic correlation of unimodular complementary and Zcomplementary sets (ZCSs) are considered, the correlation magnitudes of the resultant sequence sets of the ITROX algorithm, are not as good as desired. In this work, we have proposed a new modified ITROX algorithm, which can design unimodular ZCSs. Also the correlation levels of the resultant unimodular complementary sequence sets are better as compared to the previous ITROX.
Smart Food Monitoring System Based on IoT and Machine Learning

The monitoring of food quality is crucial in ensuring food safety and reducing food waste. In this paper, we present the design and implementation of a smart food monitoring system that utilizes Internet of Things (IoT) and Machine Learning (ML) technologies. The system includes IoT devices such as temperature and humidity sensors, as well as gas concentration sensors, to continuously monitor food items. The data collected by these sensors is analyzed using machine learning algorithms to determine the freshness of the food. The system also features a web-based dashboard that displays real-time data and alerts when the food items reach a certain threshold level. The dash-board can be accessed remotely using a mobile app, making it easy for users to monitor their food from anywhere. The system's effectiveness was evaluated using various food items, and the results showed that it successfully predicted the freshness of food with high accuracy. The proposed system has significant potential applications in the food industry, reducing food waste and ensuring that consumers can access fresh and healthy food. Additionally, the system provides an efficient way of tracking food inventory, reducing costs and increasing efficiency. The system's real-time monitoring and alerts are ideal for personal use in homes and kitchens. In conclusion, the proposed smart food monitoring system using IoT and machine learning provides a reliable and cost-effective solution for monitoring the freshness of food. The system has significant potential applications in the food industry and personal use, reducing food waste, increasing efficiency, and ensuring access to fresh and healthy food.
Limit Distribution Theory for KL divergence and Applications to Auditing Differential Privacy

The Kullback-Leibler (KL) divergence is a discrepancy measure between probability distribution that plays a central role in information theory, statistics and machine learning. While there are numerous methods for estimating this quantity from data, a limit distribution theory which quantifies fluctuations of the estimation error is largely obscure. In this paper, we close this gap by identifying sufficient conditions on the population distributions for the existence of distributional limits and characterizing the limiting variables. These results are used to derive one- and two-sample limit theorems for Gaussian-smoothed KL divergence, both under the null and the alternative. Finally, an application of the limit distribution result to auditing differential privacy is proposed and analyzed for significance level and power against local alternatives.
Modeling Statistical Delay, Error-Rate, and Joint-Delay/Error-Rate QoS-Exponents Over M-MIMO Mobile Wireless Networks Using FBC

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ϵ-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC.
Split-Ring Enclosed K-shaped Rotational Symmetric Metamaterial Absorber for Multiband Wireless Applications

This article describes a metamaterial absorber (MMA) with an enclosed K-shaped structure based on a rotationally symmetric Square Split Ring Resonator (SSRR). The MMA's unit cell was developed using FR-4, an easily accessible, reasonably priced substrate material with a dielectric constant 4.3. In this work, three resonances covering the 6 GHz band (5.925 to 6.725 GHz), the 8 GHz band (7.725-8.275 GHz), and the 15 GHz band (14.4-15.35 GHz) are shown. These resonances have adjustable multifunctional properties, Single Negative (SNG), Epsilon Negative (ENG), and near zero index characteristics. Additionally, at these frequencies, its refractive index is almost zero, and its electric length is 0. 2λ×0. 2λ×0. 2λ×. 032λ, where λ is the wavelength determined at the lowest resonance frequency (6 GHz). The planned unit cell's increased effective medium ratio (EMR) demonstrates the MMA's compactness. Due to its tiny size, robust EMR, negative permittivity, permeability, and almost zero refractive index, this MTM is perfect for WiFi 6E, Civil, and multiband wireless applications, especially for outstanding gain with enhanced directivity.
Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective

Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r. t. six datasets and three tasks, and experimental results demonstrate its superiority.
Periodic Properties of Commutative Polynomials Defined by Fourth Order Recurrence Relations with Two Variables Over Z2k

In constructing public-key cryptosystems, commutative property is an essential characteristic. Taking advantage of the commutative property of Chebyshev polynomials, a publickey cryptosystem over the residue class ring ${\mathbb{Z}_{{2^k}}}$ has been introduced, which can be implemented very efficiently. Unfortunately, however, the cryptosystem is broken using knowledge of the periodic properties of Chebyshev polynomials. Although commutative polynomials with two variables can be candidates for a public-key cryptosystem instead of Chebyshev polynomials, characteristics of the polynomials should be discussed carefully. In this study, we analyzed some properties of commutative polynomials with two variables over ${\mathbb{Z}_{{2^k}}}$. More precisely, the degree period and the condition for permutation polynomial are given theoretically and verified experimentally. Based on the derived properties, a security evaluation of a key-exchange protocol using the polynomials is also discussed.
Improved Belief Propagation Decoders for Polar Codes

Belief propagation (BP) decoding of polar codes, which can be executed in parallel, is suitable for low-latency applications. In this paper, we first give a sparse parity-check matrix representation for polar codes, based on which several strategies for performance improvement are proposed. We then devise a scheme of which the performance can approach the maximum-likelihood lower bound and that of successive cancellation list (SCL) decoding. For cyclic redundancy check (CRC)-concatenated polar codes, a parity-check matrix representation is also presented so that joint decoding between the CRC code and polar code is possible. An improved CRC-aided BP decoder is hence proposed with performance close to that of the state-of-the-art CRC-aided SCL decoder.
Kirigami Corrugations: Strong, Modular, and Programmable Plate Lattices

Plate lattices are high-performance lightweight structures, exhibiting up to twice the yield strength and stiffness compared to truss lattices of similar geometric arrangement and relative density. Although they are of great interest for research and structural engineering applications, their complex manufacturing and assembly processes limit their practical use, with sandwich panels being an exception. This paper presents a novel approach to the design and modular assembly of folded custom 3-dimensional plate lattices as structural corrugations for use in structural engineering and robotics applications. The plate lattice structural corrugation uses a building block strategy and incorporates custom modified unit cells based on the Miura-ori. This transformation involves expanding the top and bottom zig-zag crease lines into facets and orienting them in space. The resulting modified pattern is referred to as the Kirigami Expanded Miura. The unique structure of these lattices not only provides exceptional mechanical performance as static structures, but also allows for the design of anisotropies in their flexural stiffness by alternating between the Maxwell criterion on bending-dominated or stretchdominated cells. These anisotropies can have value differences of up to 24 with the same geometry, making them ideal for robotic morphing applications. We validate our proposed technology by characterizing the mechanical performance of this new building system and comparing it with state-of-the-art corrugations. We demonstrate the potential of this approach by designing, manufacturing, and modularly assembling multiple structures and robots with single and double curvature.
Neural Estimation of Multi-User Capacity Regions

In this paper, we introduce a data-driven methodology for estimating capacity regions of continuous channels in multi-user communication systems. Computing capacity regions is a long standing open problem, even in simple communication scenarios. Nevertheless, it is often possible to represent their capacity regions as the limit of an optimization problem (a multi-letter expression). In many cases, these multi-letter expressions can be expressed in terms of directed information (DI) rates. Accordingly, our approach utilizes neural networks to estimate capacity regions, leveraging the recent introduction of the directed information neural estimator (DINE). The main idea of our methodology involves training DINE-based models using samples of channel inputs and outputs, and using these models to estimate the DI rate terms that are intrinsic to the studied capacity region. To estimate the capacity region rates, we optimize the DI rates over the involved input distributions which are parameterized by a neural distribution transformer (NDT), and execute an alternating maximization procedure between the NDT models and DINE-based models until convergence is achieved. The methodology is suitable for the case where the channel is treated as a "black-box" and the designer can only gather observations of its inputs and outputs, lacking any knowledge of the explicit channel model. The performance of our proposed algorithm is shown via several well-known settings, including the Gaussian two-way channel and the two-user Gaussian multiple-access channel with and without feedback.
Towards Unlocking Sustainability Potentials - Adoption and Diffusion of Digital Innovation

Today organizations are encouraged to progress towards sustainability. The aim is to achieve more with less. Here, digitalization is an important lever to enable greater sustainability by e.g., making the status-quo transparent and allowing to optimize current production processes, products, and services. The aim of this paper is to support organizations in addressing today's sustain ability challenges with digital potentials in a holistic, coherent, and standardized way. The designed REDUCE (Resource Efficiency via Digitalization as Unfair Competitive Enabler) method adopts, customizes, and complements the dynamic capability theory. It excels at (1) sensing: identifying when and to what sustainability trends to adapt, (2) seizing: designing a fitting (digital innovation) strategy and (3) transforming: its successful adoption and diffusion. Thus, the REDUCE method's purpose is to steer organizations via multi- perspectives and multi-steps towards a more sustainable future. Here, also REDUCE itself can be advanced to an IT -driven REDUCE Tool enabling and guiding its users.
Construction and Decoding of Codes over the Dual-Parameter Barrier Error Model

Barrier-error channels have been suggested as a model for non-binary channels that are milder than symmetric-error channels. Such channels are motivated by practical applications in data storage and communications. The barrier-error model allows errors only to (downward) and from (upward) a specific symbol within the alphabet. We study a generalization of prior barrier-error models that considers different numbers of downward and upward errors. The results of this paper include a sufficient condition for error correction, code-size upper and lower bounds, a code construction decomposing to just two constituent symmetric-error codes (prior ones used many), and a decoding algorithm that decodes the two codes jointly. The decoder is shown empirically to achieve better block error rates when compared to previous algorithms.