Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the Heterogeneity of Cognitive Functioning in Alzheimer’s Disease to extend 1 the Placebo-Treatment Dichotomy: Latent class analysis of Individual-Participant Data 2 from Five Pivotal Randomized Clinical Trials of Donepezil 3 Heterogeneity of Cognitive Functioning in Alzheimer’s Disease

30 Background: The extent and profiles of heterogeneity in cognitive functioning among 31 participants in clinical trials of antidementia medication are unknown. We aimed to quantify and 32 identify profiles of heterogeneity of cognition in Alzheimer’s disease. 33 Methods: Individual-level participant data were analyzed from five pivotal clinical trials of 34 donepezil for Alzheimer’s disease (N= 2,919). Based on Alzheimer’s Disease Assessment Scale- 35 Cognitive Subscale total scores from baseline up to week 12, a latent class model was used to 36 identify heterogeneous groups. A logistic regression model was used to examine factors 37 associated with group membership. Sensitivity analysis was conducted, restricted to the 38 donepezil, and then the placebo arm. 39 Results: The latent class model identified three classes labeled as low scorers (i.e., least cognitive 40 impairment; N=1,666, 76.04%), improvers (N=27, 1.23%), and high scorers (N=498, 22.73%). 41 Logistic modeling showed that donepezil compared to placebo was significantly (P<0.05) 42 positively associated with membership in the improvers class (OR = 6.88, 95% CI = 2.03, 43 42.95), and negatively with high scorers (OR=0.79, 95% CI =0.64, 0.98). Sensitivity analysis 44 restricted to the placebo, then donepezil arms replicated similar heterogeneity patterns. 45 Conclusions: Our results inform clinicians regarding the extent of heterogeneity in cognitive 46 functioning during treatment and contribute to trial design considerations.
ABSTRACT OF THE THESIS Does Choice of Eyewitness Research Materials Influence Findings?

The misinformation effect refers to memory distortion following exposure to inaccurate post-event information. The present study sought to take a critical look at the materials used in eyewitness memory research through the lens of this misinformation effect. One criticism of applying research on eyewitness memory to the real-world legal system is that many studies rely on pallid materials. Research on the misinformation effect usually uses a video or slideshow to expose participants to a crime. While videos provide a more realistic scenario with the moving picture, slideshows are easier to manipulate. The present study sought to explore whether these two modes of stimuli, differing in realism, produce similar effects for participants in response to post-event misinformation. Participants were randomly assigned to view the same crime via a video or slideshow, and then to be exposed to post-event information about the crime that was either misinformation or consistent information. Finally, all participants took a memory test which tested their memory for details of the crime. Results showed that participants who viewed the crime via video or slideshow did not significantly differ in susceptibility to post-event misinformation. The relationship between eyewitness confidence and accuracy was also examined. The need for more critical consideration of the stimuli used in research is discussed.
A Novel Rough - Surface Direct Wave Suppression Algorithm based on Energy Adaptive Analysis

As a nondestructive testing technology, ground penetrating radar (GPR) is widely used in the detection and recognition of high-speed railway subgrade diseases. Rough-surface direct wave suppression caused by locomotive vibration and subgrade irregularity is a challenge in the GPR signal process. Flat-surface assumption makes traditional method difficult to apply directly to the rough terrain environment. To remove rough-surface direct wave and improve the signal-to-noise ratio, a new adaptive algorithm is proposed in this paper. First, according to the characteristic of electromagnetic wave propagation, the echo model of GPR was constructed, and the composition of ground radar echo signal was analyzed. Next, the eigenvalue and eigenvector of the echo signal and ground direct wave were studied. With the increase of relative permittivity, the energy eigenvector of direct wave and echo signal converged to the same vector space. On this basis, a rough-surface direct wave suppression framework based on energy feature adaptive analysis was proposed. Finally, a numerical simulation and field experiment were carried out to verify the feasibility of the algorithm. Results showed that the algorithm effectively suppresses the rough ground direct wave, and the processing result preserves the target echo more completely. Without intervention, this method can be applied in automatic direct wave suppression, which will promote the development of automatic identification technology in the detection of railway subgrade diseases.
Electoral Backlash or Positive Reinforcement? Wind Power and Congressional Elections in the United States

Divisive facilities, such as wind turbines, are important for society but controversial because they often carry local costs. Elections are the most important means for citizens to express their views about divisive facilities, but the political science literature on the topic has little to say about their electoral effects. To fill the gap, we examine wind turbine construction in the United States between 2003 and 2012. An instrumental variable analysis shows that wind turbine construction generated large electoral benefits for (prorenewables) Democratic candidates: every megawatt of additional wind power capacity over statewide trend increased the Democratic vote share in US House elections by 0.03 percentage points. This electoral shift has contributed to a pro-environmental shift in congressional roll call voting on the environment. While the result is inconsistent with naive models of retrospective voting, it accords with positive reinforcement theories on how policies endogenously create their political support.
Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes

For a mobile robot, navigation skills that are safe, efficient, and socially compliant in crowded, dynamic environments are essential. This is a particularly challenging problem as it requires the robot to accurately predict pedestrians’ movements, analyse developing traffic situations, and plan its own path or trajectory accordingly. Previous approaches still exhibit low accuracy for pedestrian trajectory prediction, and they are prone to generate infeasible trajectories under complex crowded conditions. In this paper, we develop an improved socially conscious model to learn and predict a pedestrian’s future trajectory. To generate more efficient and safer trajectories in a changing crowed space, an online path planning algorithm considering pedestrians’ predicted movements and the feasibility of the candidate trajectories is proposed. Then, multiple traffic states are defined to guide the robot finding the optimal navigation strategies under changing traffic situations in a crowded area. We have demonstrated the performance of our approach outperforms state-of-the-art approaches with public datasets, in low-density and simulated medium-density crowded scenarios.
An Empirical Survey on Long Document Summarization: Datasets, Models, and Metrics

Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An automatic summarization system that can effectively condense long documents into short and concise texts to encapsulate the most important information would thus be significant in aiding the reader’s comprehension. Recently, with the advent of neural architectures, significant research efforts have been made to advance automatic text summarization systems, and numerous studies on the challenges of extending these systems to the long document domain have emerged. In this survey, we provide a comprehensive overview of the research on long document summarization and a systematic evaluation across the three principal components of its research setting: benchmark datasets, summarization models, and evaluation metrics. For each component, we organize the literature within the context of long document summarization and conduct an empirical analysis to broaden the perspective on current research progress. The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics. Based on the overall findings, we conclude by proposing possible directions for future exploration in this rapidly growing field.
Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations. Our study of three popular messengers (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram, we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings. Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool “JTR,” we can iterate through the entire worldwide mobile phone number space in < 150 s on a consumer-grade GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest. Regarding mitigations, we most notably propose two novel rate-limiting schemes: our incremental contact discovery for services without server-side contact storage strictly improves over Signal’s current approach while being compatible with private set intersection, whereas our differential scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information.
Resolving or living with conflicts at work? The case of high-tech Jewish Ultra-Orthodox employees

ABSTRACT The objectives of this article are to describe and analyze the perceptions and experiences of Ultra-Orthodox men working in the high-tech industry: the identity conflict that they might experience and how they live with, or resolve, this conflict. In-depth interviews were conducted with 20 self-identified Jewish Ultra-Orthodox men. The study shows that employees from segregated religious backgrounds face four specific challenges, relating to their faith, spirituality, and emotions, the meaning attributed to work, their cultural identity, and their social identity. These challenges may be experienced as sources of identity conflict. The study also explored the various ways in which employees interpret and or justify their positions in relation to these challenges and, accordingly, their workplace integration.
The ethical in Jan Patočka’s thought: Sacrifice and care for the soul

Abstract In his two works from the 1970s, Patočka proposes a very personal way that the spiritual crisis, which manifests itself as a techno-scientific reality of Gestell, can be overcome. Patočka argues that the only way to escape spiritual decline is through sacrifice. This study examines how the ethical is represented in Patočka’s philosophy. It focuses on his two main concepts of sacrifice and care for the soul and explores the relationship between them. Through a close reading of Plato and Europe (1973), ‘Four Seminars’ (1973), and his essay ‘The dangers of technicization’ (1973), this study reveals how Patočka proposes that the ethical can be implemented within the realm of the political. Drawing a parallel between Socrates’ and Patočka’s lives and fates, this study points to the significance of ethics in political life – both in the ancient Greek polis of Socrates’ time and in communist Czechoslovakia in Patočka’s time. This approach highlights the influence that the philosophy of Socrates had on Patočka’s thinking.
Divergent Evolution of Early Terrestrial Fungi Reveals the Evolution of Mucormycosis Pathogenicity Factors

Fungi have evolved over millions of years and their species diversity is predicted to be the second largest on the earth. Fungi have cross-kingdom interactions with many organisms which have mutually shaped their evolutionary trajectories. Zygomycete fungi hold a pivotal position in the fungal tree of life and provide important perspectives on the early evolution of fungi from aquatic to terrestrial environments. Phylogenomic analyses have found that zygomycete fungi diversified into two separate clades, the Mucoromycota which are frequently associated with plants and Zoopagomycota that are commonly animal-associated fungi. Genetic elements that contributed to the fitness and divergence of these lineages may have been shaped by the varied interactions these fungi have had with plants, animals, bacteria and other microbes. To investigate this, we performed comparative genomic analyses of the two clades in the context of Kingdom Fungi, benefiting from our generation of a new collection of zygomycete genomes. We identified lineage-specific genomic content which may contribute to the disparate biology observed in these zygomycetes. Our findings include the discovery of undescribed diversity in CotH, a Mucormycosis pathogenicity factor, which was found in a broad set of zygomycetes. Reconciliation analysis identified multiple duplication events and an expansion of CotH copies throughout Mucoromycotina, Mortierellomycotina, Neocallimastigomycota, and Basidiobolus lineages. A kingdom-level phylogenomic analysis also identified new evolutionary relationships within the sub-phyla of Mucoromycota and Zoopagomycota.
Female LSD1 Conditional Knockout Mice Have an Increased Bone Mass

Osteoclasts are large multinucleated cells that degrade bone mineral and extracellular matrix. Investigating the mechanisms by which osteoclast differentiation is epigenetically regulated is a key to understanding the pathogenesis of skeletal related diseases such as periodontitis and osteoporosis. Lysine specific demethylase 1 (LSD1/KDM1A) is a member of the histone demethylase family that regulates gene expression via the removal of mono-and dimethyl groups from H3K4 and H3K9. Prior to our study, little was known about the effect of LSD1 on skeletal development and osteoclast differentiation. Here we show conditional deletion of LSD1 in the myeloid lineage results in a decrease in osteoclast differentiation and activity. Furthermore, Lsd1cKO female, but not male, mice have an increased bone mass. Lastly, we demonstrate that LSD1 can form a complex with CoREST, HDAC1 and HDAC2 suggesting a mechanism by which the combination of methylation and acetylation of histone residues regulates osteoclast gene expression.
Supplementary Data: Learned deconvolution using physics priors for light-sheet microscopy with propagation-invariant beams.

the highest resolution, but are visually inconsistent with the GT. The CMSR network was trained on TxRed labelled endothelial cells and presents improved generality across sample types. However, the relative intensity in the images emphasise bright features over the GT. We note that this is common to U-Net architectures (Supplementary Note 4), which can lead to greater potential for improved resolution at the expense of a reduction in PSNR in dense tissues, as evident by comparing the performance of our DL method with DL (U-Net). The CARE Denoise network was trained on high-and low-SNR acquisitions of the sample sample and system. This training favoured system noise reduction instead of image content transformation, which is evident by the low resolution of its outputs. However, when provided with good SNR images, the network has attenuated regions of low image intensity, which is especially evident in the brain. DSP-Net Upscale was trained on high-resolution GTs that have been digitally downsampled with added noise. Thus, the training favoured the physical transformation of upscaling and denoising. While DSP-Net Upscale demonstrates outputs consistent with the GT, the outputs offer little quantitative improvement over RL deconvolution. In contrast, our method (DL), which is designed for deconvolution
TD Classifier: Automatic Identification of Java Classes with High Technical Debt

To date, the identification and quantification of Technical Debt (TD) rely heavily on a few sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue, we present a tool that employs machine learning on a dataset built upon the convergence of three widely-adopted TD Assessment tools to automatically assess the class-level TD for any arbitrary Java project. The proposed tool is able to classify software classes as high-TD or not, by synthesizing source code and repository ac-tivity information retrieved by employing four popular open source analyzers. The classification results are combined with proper vi-sualization techniques, to enable the identification of classes that are more likely to be problematic. To demonstrate the proposed tool and evaluate its usefulness, a case study is conducted based on a real-world open-source software project. The proposed tool is expected to facilitate TD management activities and enable fur-ther experimentation through its use in an academic or industrial setting. Video: https://youtu.be/umgXU8u7lIA Running Instance: http://160.40. 52.130:3000/tdclassifier Source Code: https://gitlab.seis. iti.gr/root/td-classifier.git
Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness

Data-driven solutions are playing an increasingly important role in numerous practical problems across multiple disciplines. The shift from the traditional model-driven approaches to those that are data driven naturally emphasizes the importance of the explainability of solutions, as, in this case, the connection to a physical model is often not obvious. Explainability is a broad umbrella and includes interpretability, but it also implies that the solutions need to be complete, in that one should be able to “audit” them, ask appropriate questions, and hence gain further insight about their inner workings [1]. Thus, interpretability, reproducibility, and, ultimately, our ability to generalize these solutions to unseen scenarios and situations are all strongly tied to the starting point of explainability.
Performance of the Cornell Main Linac Prototype Cryomodule for the CBETA Project

The Cornell Main Linac Cryomodule (MLC) is a key component in the Cornell-BNL ERL Test Accelerator (CBETA) project, which is a 4-turn FFAG ERL under construction at Cornell University. The MLC houses six 7cell SRF cavities with individual higher order-modes (HOMs) absorbers, cavity frequency tuners, and one magnet/BPM section. Here we present final results from the MLC cavity performance and report on the studies on the MLC HOMs, slow tuner, and microphonics.
Analysis of Complex Customer Networks: A Real-World Banking Example

Complex customer networks are an important tool for better understanding of customer behaviour and customers’ mutual interdependence. Hence, they have proven to be insightful in numerous customer-centric domains such as recommender systems and customer relationship management. Although previous research in this field is predominantly associated with telecommunication and e-commerce sectors, similar construction principles could be transferred to the financial sector, e.g. a customer network could be created based on transaction history or domain-specific relations such as loan debtor–guarantor or loan debtor–co-debtor. However, this research path is still underdeveloped. In this paper, we analyse real-world complex customer networks of a Croatian bank. Numerous graph metrics were calculated, and the obtained results prove that these networks are non-random. We also show that these networks are scale-free and exhibit the small-world effect based on graph properties like the node degree distribution and the average shortest path.
Microarchitectural Performance Evaluation of AV1 Video Encoding Workloads

Video encoding/decoding is an extremely relevant workload in our society today. Videos account for a significant percentage of the world’s network traffic, which is expected only to be growing. Thus, it is important to understand these workloads to optimize the hardware to handle them better.This paper explores the reasons for the large runtimes taken by AV1 encoding workloads. We discover that the runtime of the SVT-AV1 encoder is significantly higher than other encoders because it requires a larger number of instructions to encode the same video, rather than any significant microarchitectural inefficiencies. We also compare the thread scaling of SVT-AV1 against other codecs and observe that SVT-AV1 contains the highest degree of parallelism of the tested encoders.
Evaluation of Professional Taxi Drivers’ Car-Following Behaviour on Capacity of Urban Highway Segments

In the field of traffic operation analysis, one of the most important topics is that of estimating highway capacity, which determines the reliability of capacity-related traffic optimization strategy. As proposed by the Highway Capacity Manual, various factors are considered to improve the accuracy of capacity estimation under prevailing conditions. In particular, the driver population factor, which accounts for the heterogeneity of driving behaviour, is determined by “arbitrarily” judging whether highway facilities serve drivers travelling for commuting (or otherwise-accustomed drivers). However, since a significant proportion of professional taxi drivers plays important role in urban transportation system, major difference between driver populations can origin from the heterogeneity of driving experience associated with drivers’ professionalism. In this paper, an adjustment factor is developed, aiming to precisely evaluate the impacts of professional taxi drivers’ car-following characteristics on capacity of urban highway segments. Based on the car-following data measured by our vehicle-mounted system, it is found that professional taxi drivers have statistically significant difference in spacing as compared to non-professional drivers. Then regression models are developed to relate speeds to spacing by means of the equilibrium relation in the Intelligent Driver Model, which achieve high performance of explaining car-following data as well as significant interpretability on a macroscopic traffic flow level. Being compatible with the paradigm of capacity estimation in the HCM, a quantitative adjustment factor was proposed for capacity determination by identifying the impacts of professional taxi drivers’ car-following characteristics. It is found that the presence of professional drivers can lead to at most 31% capacity increment. Moreover, this study compared the estimated and the measured capacities. The results suggested that the estimation accuracy of capacity is significantly improved by taking into account the proposed adjustment factor.