Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of the Geriatric Depression Scale to Distinguish Apathy From Depression in Alzheimer's Disease and Related Dementias.

Early detection of Alzheimer's disease (AD) and related dementias in the elderly is critical for improving treatment methods and is a necessary component for improving public health interventions. One of the earliest and most common behavioral syndromes of AD is apathy and is associated with executive dysfunction. Apathy in AD is often misdiagnosed as depression due to an overlap in symptoms. Studies that have found depression to be associated with executive dysfunction have not always controlled for the presence of apathy. The Geriatric Depression Scale (GDS) is a widely used instrument designed to assess depression in the elderly. This study utilized the GDS and a set of standard neuropsychological instruments to investigate the relationship between apathy, depression, and executive functions in individuals with AD and related dementias. The first objective of this study was to determine if apathy has a greater impact on executive functions compared to depression in AD and related dementias. The second objective was to determine the effectiveness of the GDS as a screen for apathy. The results of the analyses did not support the hypotheses. However, exploratory analyses suggested a possible non-linear relationship with apathy and various levels of dementia severity. Exploratory analysis also suggested mean levels of endorsement for apathy varied by diagnosis. Further research is warranted to investigate this relationship and the GDS endorsement patterns for caregivers regarding their impression of the demented individual.
Simulating Weighted Automata over Sequences and Trees with Transformers

Transformers are ubiquitous models in the natural language processing (NLP) community and have shown impressive empirical successes in the past few years. However, little is understood about how they reason and the limits of their computational capabilities. These models do not process data sequentially, and yet outperform sequential neural models such as RNNs. Recent work has shown that these models can compactly simulate the sequential reasoning abilities of deterministic finite automata (DFAs). This leads to the following question: can transformers simulate the reasoning of more complex finite state machines? In this work, we show that transformers can simulate weighted finite automata (WFAs), a class of models which subsumes DFAs, as well as weighted tree automata (WTA), a generalization of weighted automata to tree structured inputs. We prove these claims formally and provide upper bounds on the sizes of the transformer models needed as a function of the number of states the target automata. Empirically, we perform synthetic experiments showing that transformers are able to learn these compact solutions via standard gradient-based training.
Gradient dynamics approach to reactive thin-film hydrodynamics

Wetting and dewetting dynamics of simple and complex liquids is described by kinetic equations in gradient dynamics form that incorporates the various coupled dissipative processes in a fully thermodynamically consistent manner. After briefly reviewing this, we also review how chemical reactions can be captured by a related gradient dynamics description, assuming detailed balanced mass action type kinetics. Then, we bring both aspects together and discuss mesoscopic reactive thin-film hydrodynamics illustrated by two examples, namely, models for reactive wetting and reactive surfactants. These models can describe the approach to equilibrium but may also be employed to study out-of-equilibrium dynamics. In the latter case, one breaks the gradient dynamics form by chemostatting to obtain active systems. In this way, for reactive wetting we recover running drops that are driven by chemically sustained wettability gradients and for drops covered by autocatalytic reactive surfactants we find complex forms of self-propulsion and self-excited oscillations.
Perspectivas Futuras do Aquecimento Residencial de Água no Brasil: uma Análise a Partir da Construção de Cenários

Electricity is the most common energy carrier used for water heating in Brazil. However, high electricity consumption resulting from this end use has been receiving increasing PROFUTURO: PROGRAMA DE ESTUDOS DO FUTURO Editor Científico: James Terence Coulter Wright Avaliação: Double Blind Review, pelo SEER/OJS Revisão: gramática, normativa e formatação Recebido em:09/09/2014 Aprovado em:08/10/2014 Future Studies Research Journal ISSN 2175-5825 São Paulo, v.6, n.2, p. 157 – 186, Julho. /Dez. 2014 159 attention from those involved in energy planning in the country the extent to which analysis indicates that the expansion of electricity generation capacity must occur by increasing the participation of non-renewable sources in detriment of hydroelectricity. Besides, massive usage of electric showerheads in a short period of a time significantly contributes to electricity peak load. In light of this, stimulating fuel gas consumption (more specifically liquefied petroleum gas and natural gas) for water heating has been seen as a viable alternative, from the point of view of energy efficiency and infrastructure, to electricity. In this work, a scenario methodology is used to map the variables and actors related to electric showerheads usage and analyze how the most relevant elements influencing the adoption could behave in the future. From this, opportunities to promote the rationalization of energy resources consumption in Brazil by expanding the use of fuel gas for residential water heating are discussed. KEY-WORDS: Energy scenarios. Electric showerheads. Fuel gases. Brazil. Residential end uses. Felipe de Albuquerque Sgarbi, Danielle Johann Guilherme, Taluia Croso, Alberto Fossa, Edmilson Moutinho dos Santos, Murilo Tadeu Werneck Fagá Future Studies Research Journal ISSN 2175-5825 São Paulo, v.6, n.2, p. 157 – 186, Julho. /Dez. 2014
Verification of Quantum Computations without Trusted Preparations or Measurements

With the advent of delegated quantum computing as a service, verifying quantum computations is becoming a question of great importance. Existing information theoretically Secure Delegated Quantum Computing (SDQC) protocols require the client to possess the ability to perform either trusted state preparations or measurements. Whether it is possible to verify universal quantum computations with information-theoretic security without trusted preparations or measurements was an open question so far. In this paper, we settle this question in the affirmative by presenting a modular, composable, and efficient way to turn known verification schemes into protocols that rely only on trusted gates. Our first contribution is an extremely lightweight reduction of the problem of quantum verification for BQP to the trusted application of single-qubit rotations around the Z axis and bit flips. The second construction presented in this work shows that it is generally possible to information-theoretically verify arbitrary quantum computations with quantum output without trusted preparations or measurements. However, this second protocol requires the verifier to perform multi-qubit gates on a register whose size is independent of the size of the delegated computation.
Preventing Implant-Associated Infections by Silver Coating

ABSTRACT Implant-associated infections (IAIs) are a dreaded complication mainly caused by biofilm-forming staphylococci. Implant surfaces preventing microbial colonization would be desirable. We examined the preventive effect of a silver-coated titanium-aluminum-niobium (TiAlNb) alloy. The surface elicited a strong, inoculum-dependent activity against Staphylococcus epidermidis and Staphylococcus aureus in an agar inhibition assay. Gamma sterilization and alcohol disinfection did not alter the effect. In a tissue cage mouse model, silver coating of TiAlNb cages prevented perioperative infections in an inoculum-dependent manner and led to a 100% prevention rate after challenge with 2 × 106 CFU of S. epidermidis per cage. In S. aureus infections, silver coating had only limited effect. Similarly, daptomycin or vancomycin prophylaxis alone did not prevent S. aureus infections. However, silver coating combined with daptomycin or vancomycin prophylaxis thwarted methicillin-resistant S. aureus infections at a prevention rate of 100% or 33%, respectively. Moreover, silver release from the surface was independent of infection and occurred rapidly after implantation. On day 2, a peak of 82 μg Ag/ml was reached in the cage fluid, corresponding to almost 6× the MIC of the staphylococci. Cytotoxicity toward leukocytes in the cage was low and temporary. Surrounding tissue did not reveal histological signs of silver toxicity. In vitro, no emergence of silver resistance was observed in several clinical strains of staphylococci upon serial subinhibitory silver exposures. In conclusion, our data demonstrate that silver-coated TiAlNb is potent for prevention of IAIs and thus can be considered for clinical application.
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections

In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information. To tackle this challenge head-on, we introduce FakeWatch, a comprehensive framework carefully designed to detect fake news. Leveraging a newly curated dataset of North American election-related news articles, we construct robust classification models. Our framework integrates a model hub comprising of both traditional machine learning (ML) techniques and cutting-edge Language Models (LMs) to discern fake news effectively. Our overarching objective is to provide the research community with adaptable and precise classification models adept at identifying the ever-evolving landscape of misinformation. Quantitative evaluations of fake news classifiers on our dataset reveal that, while state-of-the-art LMs exhibit a slight edge over traditional ML models, classical models remain competitive due to their balance of accuracy and computational efficiency. Additionally, qualitative analyses shed light on patterns within fake news articles. This research lays the groundwork for future endeavors aimed at combating misinformation, particularly concerning electoral processes. We provide our labeled data and model publicly for use and reproducibility.
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience

We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios. Our aim is to contribute to the development of popular 3D scene reconstruction algorithms such as IBRnet, NeRF, and 3D Gaussian splitting. More importantly, the collected dataset, which is much denser than existing datasets, may also inspire space-oriented light field reconstruction, which is potentially different from object-centric 3D reconstruction, for immersive VR/AR experiences. We utilized a total of 40 GoPro 10 cameras, capturing images of 5k resolution. The number of photos captured for each scene is no less than 1000, and the average density (view number within a unit sphere) is 134.68. It is also worth noting that our system is capable of efficiently capturing large outdoor scenes. Addressing the current lack of large-space and dense light field datasets, we made efforts to include elements such as sky, reflections, lights and shadows that are of interest to researchers in the field of 3D reconstruction during the data capture process. Finally, we validated the effectiveness of our provided dataset on three popular algorithms and also integrated the reconstructed 3DGS results into the Unity engine, demonstrating the potential of utilizing our datasets to enhance the realism of virtual reality (VR) and create feasible interactive spaces. The dataset is available at our project website.
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction

Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRec cannot use longer training data due to constraints imposed by iteration efficiency. Most prior studies alleviate the above problems by introducing pre-trained knowledge(e.g. pre-trained user model or multi-modal embeddings). However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model. Therefore, most of them cannot employ the unified model of end-to-end training with IDRec in industrial recommender systems, thus limiting the potential of the pre-trained model. To this end, we propose a $\textbf{P}$re-trained $\textbf{P}$lug-in CTR $\textbf{M}$odel, namely PPM. PPM employs multi-modal features as input and utilizes large-scale data for pre-training. Then, PPM is plugged in IDRec model to enhance unified model's performance and iteration efficiency. Upon incorporating IDRec model, certain intermediate results within the network are cached, with only a subset of the parameters participating in training and serving. Hence, our approach can successfully deploy an end-to-end model without causing huge latency increases. Comprehensive offline experiments and online A/B testing at JD E-commerce demonstrate the efficiency and effectiveness of PPM.
Power of non‐parametric linkage analysis in mapping genes contributing to human longevity in long‐lived sib‐pairs

This report investigates the power issue in applying the non‐parametric linkage analysis of affected sib‐pairs (ASP) [Kruglyak and Lander, 1995: Am J Hum Genet 57:439–454] to localize genes that contribute to human longevity using long‐lived sib‐pairs. Data were simulated by introducing a recently developed statistical model for measuring marker‐longevity associations [Yashin et al., 1999: Am J Hum Genet 65:1178–1193], enabling direct power comparison between linkage and association approaches. The non‐parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated to assess the statistical power for different genetic (allele frequency and risk) and heterogeneity parameters under various sampling schemes (age‐cut and sample size). Based on the genotype‐specific survival function, we derived a heritability calculation as an overall measurement for the effect of causal genes with different parameter settings so that the power can be compared for different modes (dominant, recessive) of inheritance. Our results show that the ASP approach is a powerful tool in mapping very strong effect genes, both dominant and recessive. To map a rare dominant genetic variation that reduces hazard of death by half, a large sample (above 600 pairs) with at least one extremely long‐lived (over age 99) sib in each pair is needed. Again, with large sample size and high age cut‐off, the method is able to localize recessive genes with relatively small effects, but the power is very limited in case of a dominant effect. Although the power issue may depend heavily on the true genetic nature in maintaining survival, our study suggests that results from small‐scale sib‐pair investigations should be referred with caution, given the complexity of human longevity. © 2004 Wiley‐Liss, Inc.
A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream

Topic detection is a complex process and depends on language because it somehow needs to analyze text. There have been few studies on topic detection in Persian, and the existing algorithms are not remarkable. Therefore, we aimed to study topic detection in Persian. The objectives of this study are: 1) to conduct an extensive study on the best algorithms for topic detection, 2) to identify necessary adaptations to make these algorithms suitable for the Persian language, and 3) to evaluate their performance on Persian social network texts. To achieve these objectives, we have formulated two research questions: First, considering the lack of research in Persian, what modifications should be made to existing frameworks, especially those developed in English, to make them compatible with Persian? Second, how do these algorithms perform, and which one is superior? There are various topic detection methods that can be categorized into different categories. Frequent pattern and clustering are selected for this research, and a hybrid of both is proposed as a new category. Then, ten methods from these three categories are selected. All of them are re-implemented from scratch, changed, and adapted with Persian. These ten methods encompass different types of topic detection methods and have shown good performance in English. The text of Persian social network posts is used as the dataset. Additionally, a new multiclass evaluation criterion, called FS, is used in this paper for the first time in the field of topic detection. Approximately 1.4 billion tokens are processed during experiments. The results indicate that if we are searching for keyword-topics that are easily understandable by humans, the hybrid category is better. However, if the aim is to cluster posts for further analysis, the frequent pattern category is more suitable.
FDGaussian: Fast Gaussian Splatting from Single Image via Geometric-aware Diffusion Model

Reconstructing detailed 3D objects from single-view images remains a challenging task due to the limited information available. In this paper, we introduce FDGaussian, a novel two-stage framework for single-image 3D reconstruction. Recent methods typically utilize pre-trained 2D diffusion models to generate plausible novel views from the input image, yet they encounter issues with either multi-view inconsistency or lack of geometric fidelity. To overcome these challenges, we propose an orthogonal plane decomposition mechanism to extract 3D geometric features from the 2D input, enabling the generation of consistent multi-view images. Moreover, we further accelerate the state-of-the-art Gaussian Splatting incorporating epipolar attention to fuse images from different viewpoints. We demonstrate that FDGaussian generates images with high consistency across different views and reconstructs high-quality 3D objects, both qualitatively and quantitatively. More examples can be found at our website https://qjfeng.net/FDGaussian/.
Cyberbullying among youngsters: profiles of bullies and victims

A survey among 2052 primary and secondary school children reveals that cyberbullying among youngsters is not a marginal problem. However, there are discrepancies between the prevalence figures based on direct measurement versus indirect measurement of cyberbullying. Youngsters who have bullied someone via the internet or mobile phone during the last three months are younger, and are more often victims and bystanders of bullying via the internet or mobile phone, and are more often the perpetrators of traditional bullying. Youngsters who have been bullied via the internet or mobile phone during the last three months are more dependent upon the internet, feel less popular, take more internet-related risks, are more often a bystander and perpetrator of internet and mobile phone bullying, and are less often a perpetrator and more often a victim of traditional bullying. The implications for future research into cyberbullying and for cyberbullying prevention strategies are discussed.
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics

A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one solution specialized for a specific problem. We introduce Quality-Diversity Actor-Critic (QDAC), an off-policy actor-critic deep reinforcement learning algorithm that leverages a value function critic and a successor features critic to learn high-performing and diverse behaviors. In this framework, the actor optimizes an objective that seamlessly unifies both critics using constrained optimization to (1) maximize return, while (2) executing diverse skills. Compared with other Quality-Diversity methods, QDAC achieves significantly higher performance and more diverse behaviors on six challenging continuous control locomotion tasks. We also demonstrate that we can harness the learned skills to adapt better than other baselines to five perturbed environments. Finally, qualitative analyses showcase a range of remarkable behaviors, available at: http://bit.ly/qdac.
Beyond structural stabilization of highly-textured AlN thin film: the role of chemical effects

The crystalline quality and degree of c-axis orientation of hexagonal AlN thin films correlate directly with their functional properties. Therefore, achieving AlN thin films of high crystalline quality and texture is of extraordinary importance for many applications, but in particular in electronic devices. Here, we present a systematic study revealing that the growth of c-axis orientated AlN thin films can be governed by a chemical stabilization effect in addition to the conventionally known structural, i.e. strain-induced, stabilization mechanism. The promotion of in-plane growth of AlN grains with c-axis out-of-plane orientation is demonstrated on Y, W or Al seed layers with different thicknesses and crystallinity preliminary exposed to N2 at room temperature. We establish that the stabilization mechanism is chemical in nature: the formation of an N-rich surface layer on the metal seed layers upon exposure to N2 pre-determines the polarity of AlN islands at initial stages of thin film growth while the low energy barrier for the subsequent coalescence of islands of the same polarity contributes to grain growth. These results suggest that the growth of c-axis oriented AlN thin films can be optimized and controlled chemically thus opening more pathways for energy-efficient and controllable AlN thin-film growth processes.
Effective medium theory for the electrical conductance of random resistor networks which mimic crack-template-based transparent conductive films

We studied random resistor networks produced with regular structure and random distribution of edge conductances. These networks are intended to mimic crack-template-based transparent conductive films as well some random networks produced using nano-imprinting technology. Applying an effective medium theory, we found out that the electrical conductance of such networks is proportional to the square root of the number density of conductive edges. This dependence is in agreement with numerical calculations in Voronoi networks, although the effective conductances are about 15%.