Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual hallucinations in the elderly: a case series and discussion of aetiology and treatment

Background Visual hallucinations are a common phenomenon, among the older adult population. They can be functional or organic in aetiology. However, new onset visual hallucinations in this population are strongly suggestive of organic brain disease. Visual impairment, cerebrovascular disease and Parkinson’s disease are three causes of visual hallucinations, considered in this case series. The evidence in the literature, for the treatment of these conditions is scant at best. There is a paucity of randomised controlled trials available concerning possible therapeutic options. Aims/Methods We describe three case reports of visual hallucinations due to diverse underlying aetiologies. We then discuss the aetiologies of visual hallucinations in general and then in these particular cases and finally include results of a literature search examining the available evidence for any therapeutic options proposed. Results Our three cases have different, underlying aetiologies. One case is of Charles Bonnet syndrome. The next is of visual hallucinations associated with vascular dementia. The final case is of visual hallucinations associated with Parkinson’s disease. The first two cases are of particular interest due to the efficacy of Amisulpride in both clinical scenarios. Conclusions Visual hallucinations are a common phenomenon in the elderly population They can be due to a myriad of underlying causes. There are a number of neurochemical factors and neuroanatomical structures implicated. The evidence for psychopharmacological interventions is scanty. Randomised controlled trials are lacking in the area. An interesting finding in this case series, was of the clinical utility of Amisulpiride. Given this agent’s unique psychopharmacological profile it is possible that it may be efficacious in other cases of visual hallucinations associated with particular neurochemical factors.
Security Policy Violations in SDN Data Plane

Software-defined networking (SDN) utilizes a centralized controller to distribute packet processing rules to network switches. However, rules are often generated by the applications developed by different organizations, so they may conflict with each other in data plane and lead to violations with security rules. The problem is similar to firewall conflicts in IP networks. Rule conflict resolution should incur negligible process delay, such that all rules can be correctly and safely enforced in the data plane in real time. However, since SDN allows users to use more than 35 fields to specify rules (including field transition rules), it is much more complicated to prevent enforcement of SDN rules from violating with security rules than to resolve firewall rule violation, and in particular, field transition rules are enforced. Therefore, it is extremely difficult to resolve such rule conflicts in real time before the rules are installed in SDN data plane. In this paper, we investigate the rule conflict problem in SDN and identify new covert channel attacks due to rule conflicts. To the end, we propose the covert channel defender (CCD) that prevents covert channel attacks by verifying and resolving rule conflicts. Specifically, CCD tracks all rule insertion and modification messages from applications running on the controller. It analyzes the correlation among rules based on multiple packet header fields and resolves any identified rule conflict in real time before rule installation. We implement CCD with the Floodlight controller and evaluate its performance with the real-world Stanford topology. We show that CCD can efficiently detect and prevent rule conflicts in the data plane that may raise covert channels within hundreds of microseconds and brings small overhead to the packet delivery.
Adversarial Collaborative Auto-encoder for Top-N Recommendation

Recently, deep learning-based recommendation models have been proved to have state-of-the-art recommendation accuracy. However, most of the existing work assume that user feedbacks are noise-free, on which the neural networks (NN) are trained. Although some methods apply man-made noises on the input data to train the networks more effectively (e.g. the collaborative denoising auto-encoder), the noises are randomly generated. To gain further improvements, we focus on boosting the overall recommendation performance through adversarial noises. We propose a general framework to adversarially train a NN-based item recommendation model. In particular, we select the collaborative auto-encoder model as an example and test our method on three public datasets. We show that our approach enhances both overall robustness and performance which outperforms competitive state-of-the-art item recommendation models.
Cache-Aided Millimeter Wave Ad Hoc Networks With Contention-Based Content Delivery

The narrow-beam operation in millimeter wave (mmWave) networks minimizes the network interference leading to noise-limited networks in contrast with interference-limited ones. The medium access control (MAC) layer throughput and interference management strategies heavily depend on the noise-limited or interference-limited regime. Yet, these regimes are not considered in recent mmWave MAC layer designs, which can potentially have disastrous consequences on the communication performance. In this paper, we investigate the performance of cache-enabled MAC-based mmWave ad hoc networks, where randomly distributed nodes are supported by a cache. The ad hoc nodes are modeled as homogenous Poisson point processes. Specifically, we study the optimal content placement (or caching placement) at desirable mmWave nodes using a network model that accounts for uncertainties both in node locations and blockages. We propose a contention-based multimedia delivery protocol to avoid collisions among the concurrent transmissions. Subsequently, only the node with smallest back-off timer among its contenders is allowed to transmit. We then characterize the average success probability of content delivery. We also characterize the cache hit ratio probability, and transmission probability of this system under essential factors, such as blockages, node density, path loss, and caching parameters.
A Model to Classify Manufacturing Archetypes for Distributed Production

Customer demand for more individualization is shifting production models towards decentralized ones. In order to keep up with a heterogeneous class of clients in a turbulent environment, companies are always more inclined towards mass customization. Realizing different kinds of products in line with customer needs without losing the advantages in terms of efficiency of mass production requires new configurations in manufacturing firms as well as a new kind and higher extent of collaboration with customers in the design process. Starting from the analysis of the literature concerning distributed manufacturing, cloud manufacturing and customer involvement, this paper proposes a model aimed at classifying manufacturing archetypes for distributed production.
Testing The Limits of TaN Barrier Scaling

Continued scaling dimensions of interconnects used for Si chip integration reduces the available cross sectional area for conduction. As Cu requires a protective diffusion barrier, TaN and a liner such as Co or Ru is frequently chosen to fulfill the various requirements such as time dependent dielectric breakdown (TDDB). Due to the higher resistivity, scaling the barrier/ liner thickness with the line dimension is desirable, but has proven difficult much below 2nm. One aspect here is the limited conformality when using physical vapor deposition (PVD) as the method of choice. Conformal methods such as atomic layer deposition (ALD) have been studied extensively as well for this application. In this paper, we investigate the limits of PVD TaN thickness scaling while maintaining Cu diffusion barrier properties. For this purpose, a planar metal-insulator capacitor has been employed for TDDB testing which was specifically designed to avoid any process related damage to the low-k dielectric. The TaN range examined here was between 0.2 to 3nm, deposited by a PVD process with low input power in order to control deposition accurately below 1nm thickness. It is found that both Co and Ru can add significantly to the TaN barrier integrity. The results further suggest that TaN can be thinned to less than 0.8nm and yet maintain barrier integrity. In particular the combination of TaN and Ru was found to show excellent barrier properties, with TaN as thin as 0.5nm. The impact on line and via resistance between 12 and 24nm wide is being discussed.
Distributed Planning of Multi-Segment Soft Robotic Arms

This paper presents a distributed kinematic planning algorithm for the end-effector of a multi-segment soft robotic arm to reach the goal position in both 2D and 3D spaces. The planning algorithm runs sequentially from the proximal to the distal segments. For each segment, the planning algorithm only requires the information about the position of itself, the end-effector, and the goal. The 2D planning of each segment is parameterized by a bending angle and an inflation ratio, which are determined by checking the overall geometry of the residual arm and the goal position. The same concept is extended to 3D planning, where parameters include inflation ratio, azimuth angle, and elevation angle for each segment. It is demonstrated in this paper that physical limits of the arm and challenging goal position could lead to failure in goal reaching. To account for this, an iterative learning function is proposed, which allows each segment to learn from past trials and be proactive for goal reaching. The proposed distributed planning algorithm, with iterative learning, demonstrates strong scalability, low computation cost, and robust goal reaching performance. Moreover, it does not need training data to pre-learn the configuration of the arm, which makes the algorithm highly applicable to a wide range of multi-segment soft and continuum robots. Both 2D and 3D simulation results are provided to illustrate the efficacy of the proposed algorithm.
Channel Estimation: Key Factor for LDM Based Local Content Delivery on SFNs

Local Service Insertion (LSI) on Single Frequency Networks (SFN) is an attractive business case for broadcasters. Nevertheless, its practical implementation imposes a technical challenge from the spectral efficiency point of view, which has not been fully overcome yet. Recently, Layered Division Multiplexing (LDM) has been proposed as a solution to face this problem. LDM is one of the tools included in the ATSC 3.0 physical layer that enables an efficient multiple service delivery, making a simultaneous use of the whole time and frequency resources. Under certain configurations, LDM is a potential enabler for the LSI contents delivery, conveying the regional and local services on different layers. In this approach, the complexity and performance of the channel estimation procedures represent a key factor for the commercial success of receivers. Therefore, the main objective is to evaluate the the existing channel estimation procedures in a fully compliant ATSC 3.0 simulation set-up for the LDM-LSI scenario.
Computing Multivariate Effect Sizes and Their Sampling Covariance Matrices With Structural Equation Modeling: Theory, Examples, and Computer Simulations

In the social and behavioral sciences, it is recommended that effect sizes and their sampling variances be reported. Formulas for common effect sizes such as standardized and raw mean differences, correlation coefficients, and odds ratios are well known and have been well studied. However, the statistical properties of multivariate effect sizes have received less attention in the literature. This study shows how structural equation modeling (SEM) can be used to compute multivariate effect sizes and their sampling covariance matrices. We focus on the standardized mean difference (multiple-treatment and multiple-endpoint studies) with or without the assumption of the homogeneity of variances (or covariance matrices) in this study. Empirical examples were used to illustrate the procedures in R. Two computer simulation studies were used to evaluate the empirical performance of the SEM approach. The findings suggest that in multiple-treatment and multiple-endpoint studies, when the assumption of the homogeneity of variances (or covariance matrices) is questionable, it is preferable not to impose this assumption when estimating the effect sizes. Implications and further directions are discussed.
Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.
Atypical social cognitive processing in premotor Huntington’s disease: a single case study

Background We report the case of a 52-year-old male with pre-motor Huntington’s disease (HD) who has undergone detailed clinical and neuropsychological examination. This patient’s negative symptomatology and behavioural change are having a detrimental impact on his social, occupational and interpersonal life, in the absence of motor symptoms. Methods The patient has undergone repeat neuropsychological testing (T1 aged 50; T2 aged 52) with particular focus on executive function and social cognition on repeat testing. Results This case details a specific manifestation of HD relating to behavioural, psychiatric and social affective deficits. Conclusions This case illustrates how social cognitive changes can occur in HD, months and even years prior to the onset of motor features and how such unrecognized deficits can have a deleterious impact on an individual’s functional ability and lifestyle, before the disease is traditionally considered to have become manifest.
Real-Time Optical Methods for Development of Nanoparticle-Based Biosensors for Detection of Hepatitis B Surface Antigen

Label-free spectral interferometric methods were employed for development and optimization of nanolabel-based biosensors. An optical technique has been elaborated for screening of functionalized nanoparticles and reagents for design of immunochromatographic and capacitive biosensors. The first type of sensors use magnetic particles as nanolabels, which are volumetrically detected by non-linear magnetization, while in the capacitive sensors, the nanoparticles serve for signal amplifications. The developed immunosensors were tested for rapid quantitative detection of hepatitis B surface antigen, which is the major marker for diagnostics of hepatitis B.
FPGA-Based System for the Acceleration of Cloud Microservices

Scalability, distributivity, interoperability and modularity introduced in cloud computing have deeply changed the legacy data center's architecture, implementation and processing capabilities. The atomic network services offered by cloud architectures are called microservices. Unlike virtual machines, microservices can be implemented in the form of low resources footprint applications as containers (Docker, LXC etc.) or even smaller as unikernels (IncludeOS, ClickOS, Rumprun, HermitOS etc.). The need to efficiently offload the processing of computation-intensive applications has motivated the introduction of Field Programmable Gate Arrays (FPGA) boards in servers. FPGAs can nowadays be considered as cloud-standard processing resources. However, in today's cloud data centers, FPGAs cannot be accessed to run concurrent microservices. This severely limits the efficient deployment of microservices. This paper aims at introducing an FPGA-based system for the concurrent acceleration of cloud-native microservices onto FPGAs.
An Encoding Algorithm of Triply Extended Reed–Solomon Codes With Asymptotically Optimal Complexities

In this paper, we devise a fast encoding algorithm for triply extended Reed–Solomon codes. The proposed approach requires approximately two XORs per bit, which improves the prior result of three XORs per bit established by certain maximum distance separable (MDS) array codes. We also prove that, for MDS codes with two and three parities, the scheduling algorithms require at least two XORs per bit. To the best of our knowledge, this is the first provable scheduling algorithm for the triple-parity MDS codes to approach the theoretical lower bounds. The implementation with SIMD instructions is provided. The simulations show that the proposed approach is competitive, as compared with other cutting edge implementations.
Toward Affective Handles for Tuning Vibrations

When refining or personalizing a design, we count on being able to modify or move an element by changing its parameters rather than creating it anew in a different form or location—a standard utility in graphic and auditory authoring tools. Similarly, we need to tune vibrotactile sensations to fit new use cases, distinguish members of communicative icon sets, and personalize items. For tactile vibration display, however, we lack knowledge of the human perceptual mappings that must underlie such tools. Based on evidence that affective dimensions are a natural way to tune vibrations for practical purposes, we attempted to manipulate perception along three emotion dimensions (agitation, liveliness, and strangeness) using engineering parameters of hypothesized relevance. Results from two user studies show that an automatable algorithm can increase a vibration’s perceived agitation and liveliness to different degrees via signal energy, while increasing its discontinuity or randomness makes it more strange. These continuous mappings apply across diverse base vibrations; the extent of achievable emotion change varies. These results illustrate the potential for developing vibrotactile emotion controls as efficient tuning for designers and end-users.
Edge Aware Adaptive Filtering Method for Image Denoising

This paper presents an adaptive edge-preserving image denoising technique. The proposed approach adaptively adjusts the filtering mask size and mask content to perform image denoising. The mask size is determined depending on the distance from the nearest edge in the image. Different edge detectors (Prewitt, Sobel, Canny, Perona-Malik) were considered and validated. The filtering mask coefficients were selected using various kernels, such as mean and median. The filtering quality of the proposed solution in its several variants was compared with anisotropic diffusion and frequently used spatial filtering techniques like mean or median that operate globally. The qualitative and quantitative comparison of the proposed method is based on commonly known image quality measures as well as on edge preservation measures. The obtained results show the potential of the method: Quality Index up to 0.84 and edge preservation β measure up to 98.49%.
Reciprocal relationship between herpes zoster and cardiovascular diseases: A nationwide population‐based case–control study in Korea

Recently, herpes zoster (HZ) has been identified as a risk factor for stroke, but there have been few studies on the relationship between HZ and other cardiovascular diseases (CVD), including myocardial infarction (MI) and heart failure (HF). The purpose of this study was to investigate the risk of HZ occurrence after CVD and the occurrence of CVD after HZ. This was a retrospective case–control study based on the National Health Insurance System claims database for 20 311 patients with HZ and 13 980 patients with CVD and 1:5 age‐ and sex‐matched control subjects for each from January 2006 to December 2013. We evaluated the reciprocal relationship between HZ and CVD including MI, ischemic stroke and HF. Severe HZ requiring hospitalization independently increased the risk of subsequent MI (hazard ratio [HR], 1.831; 95% confidence interval [CI], 1.354–2.476)], ischemic stroke (HR, 1.523; 95% CI, 1.212–1.915) and HF (HR, 2.034; 95% CI, 1.615–2.562) during the study period. Likewise, patients with an episode of MI, ischemic stroke or HF were also at increased risk of HZ hospitalization (after MI: HR, 1.625; 95% CI, 1.144–2.308; after stroke: HR, 1.518; 95% CI, 1.177–1.957; and after HF: HR, 1.485, 95% CI 1.041–2.117). Our results suggest that there is a significant reciprocal relationship between severe HZ requiring hospitalization and CVD including MI, ischemic stroke and HF.
Inner-Outer Loop based Robust Active Damping for LCL Resonance in Grid-Connected Inverters using Grid Current Feedback

In this paper, the active damping of LCL filter resonance of a grid-tied inverter is studied. We show the tradeoffs involved in designing a hierarchical inner-outer control system by analyzing the control-relevant properties at multiple loop-breaking points. We demonstrate how a novel inner-outer control design technique can help obtain reasonable properties simultaneously at the error and plant input/controls. Specifically, we show how inner-loop controller affects the robustness margins, and hence affect the sensitivity properties of the system. A new controller structure is proposed that includes a lag network in series with the traditional inner and outer controllers [18], [20], typically used in grid current feedback control, presenting a systematic approach for designing more robust active damping strategies. Further, we present a generalized $\mathcal{H}^{\infty}$ mixed-sensitivity framework for designing hierarchical inner-outer loop control systems, that helps directly address closed-loop properties at multiple loop-breaking points. We show how an “equilibrated” design can be obtained by trading off properties at these multiple loop-breaking points.
Measuring the Temporal Behavior of Real-World Person Re-Identification

Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research. Typically, the probe image or image sequence is matched to a gallery set with a fixed candidate list. On the other hand, in real-world applications of re-id, we would search for a person of interest in a gallery set that is continuously populated by new candidates over time. A key question of interest for the operator of such a system is: how long is a correct match to a probe likely to remain in a rank-k shortlist of candidates? In this paper, we propose to distill this information into what we call a Rank Persistence Curve (RPC), which unlike a conventional cumulative match characteristic (CMC) curve helps directly compare the temporal performance of different re-id algorithms. To carefully illustrate the concept, we collected a new multi-shot person re-id dataset called RPIfield. The RPIfield dataset is constructed using a network of 12 cameras with 112 explicitly time-stamped actor paths among about 4000 distractors. We then evaluate the temporal performance of different re-id algorithms using the proposed RPCs using single and pairwise camera videos from RPIfield, and discuss considerations for future research.
Visualizing linked Jazz: A web-based tool for social network analysis and exploration

This paper intends to demonstrate that recent improvements in web-based technology have made the creation of webbased network visualizations a viable and appealing possibility. We showcase a network visualization tool made for the Linked Jazz Project which utilizes a number of interactive and multimedia features to deliver an engaging experience to a wide audience. We briefly explore the history, benefits, and possible limitations of this web-based approach.