Miracle-dz/UniFAR-FTUs huggingface dataset:
AMWRPN: Ambiguity Measure Weighted Risk Priority Number Model for Failure Mode and Effects Analysis

The relative importance of each risk factor in failure mode and effects analysis (FMEA) should be addressed properly. Intuitively, in the assessments coming from the FEMA experts, there exists a potential judgement on which risk factor has a higher weight for the FMEA item. Based on this cognition and perspective, a new ambiguity measure weighted risk priority number (AMWRPN) for FMEA is proposed. AMWRPN takes into consideration of the relative weight of different risk factors by measuring the ambiguity degree of the experts’ assessments. If the assessment of an expert has a certain belief on the judgement, then the relative importance of the corresponding risk factor will be higher than the uncertain one; and vice versa. The ambiguity measure (AM) in the framework of the Dempster–Shafer evidence theory (DST) has been used to construct the exponential weight of each risk factor in AMWRPN. In comparison with the weight factor basing on fuzzy sets theory or other theories in the DST framework, the AM-based weight factor for uncertainty measure of the subjective assessment ensures the internal coordination of the proposed method. An application of the proposed method in aircraft turbine rotor blade verifies the effectiveness of the new risk priority number model.
Performance and Energy Enhancement through an Online Single/Multi Level Mode Switching Cache Architecture

STT-RAM cells can be considered as an alternative or a hybrid addition to today's SRAM-based cache memories. This is mostly because of their scalability and low leakage power. Moreover, their data storing mechanism (storing the value as resistance) makes them very suitable and applicable for multivalue cache architectures. This feature results in system performance enhancement without any area overhead. On the other hand, the required two-step read/write procedure in multilevel cells results in a non-uniform time access and energy and power overhead on the system. In this paper, we propose a new architecture to dynamically swap data between soft (fast read access) and hard (slow read access) bits in ML cell. Moreover, by reconfiguring cache block size, the proposed architecture can switch between ML and SL modes at runtime. In other words, the swapping method places the hot part of each cache block into soft-bits and the less accessed part into the hard-bits. The SL/ML switching method benefits from the low latency and energy of SL mode and the high storing capacity of ML mode at the same time. Although experimental results show that our proposed method slightly increases the miss rate compared with the conventional ML caches, the performance and energy are improved by 4.9% and 6.5%, respectively. Also, the storage overhead of our method is about 1% that is negligible.
Kinetics of electron cooling in metal films at low temperatures and revision of the two-temperature model

The two-temperature model (2TM) introduced by Kaganov, Lifshitz, and Tanatarov is widely used to describe the energy relaxation in the electron–phonon system of a metallic film. At the same time, the accuracy of the description of the electron–phonon system in terms of the 2TM, i.e. on the basis of the electron and phonon temperatures, has not been considered in detail until now. In this paper we present a microscopic theory of cooling of instantly heated electrons in metallic films. In framework of this theory the main features of electron cooling in thick and thin films were found, and an analysis of the accuracy of the 2TM in the low-temperature region was carried out. We consider a more accurate three-temperature model, which (in contrast to the 2TM) explicitly takes into account phonons with angles of incidence exceeding the angle of total internal reflection. The contribution of these phonons to the kinetics of electron relaxation can be significant if the sound velocities in the film and the substrate are quite different.
Revisiting data normalization for appearance-based gaze estimation

Appearance-based gaze estimation is promising for unconstrained real-world settings, but the significant variability in head pose and user-camera distance poses significant challenges for training generic gaze estimators. Data normalization was proposed to cancel out this geometric variability by mapping input images and gaze labels to a normalized space. Although used successfully in prior works, the role and importance of data normalization remains unclear. To fill this gap, we study data normalization for the first time using principled evaluations on both simulated and real data. We propose a modification to the current data normalization formulation by removing the scaling factor and show that our new formulation performs significantly better (between 9.5% and 32.7%) in the different evaluation settings. Using images synthesized from a 3D face model, we demonstrate the benefit of data normalization for the efficiency of the model training. Experiments on real-world images confirm the advantages of data normalization in terms of gaze estimation performance.
An Empirical Study of Face Recognition under Variations

Face recognition (FR) has recently made remark- able progress given the extraordinary capabilities of modern deep learning(DL) models. Though superior performance of DL based methods over human has been reported on benchmark dataset, it remains an open problem how those systems work in real-world condition with variations such as head pose, lighting, occlusion and image noises, in particular, in comparison to conventional FR approaches. It is hard to answer this question in a quantitative manner as current benchmark datasets are in lack of full range of variations. In this paper we propose a flexible approach to simulate face images under different variations with controllable degrees and based on the simulated dataset we quantitatively study how modern DL based and conventional FR methods perform. Based on the observations on a large number of synthesized face images, we draw several conclusions such as how head pose pitch and yaw variations will influence the FR systems and which part on the face is the most significant region, and in which situation conventional methods still show some advantages. The findings will not only be useful to assess current FR system in a quantitative manner but also shed light on future FR system design and data augmentation.
Honey bees zero in on the empty set

Honey bees join a select number of animals shown to understand the concept of nothing The number zero is central to contemporary mathematics and to our scientifically and technologically advanced culture (1). Yet, it is a difficult number to understand. Children grasp the symbolic number zero long after they start to understand, at around the age of 4 years, that “nothing” can be a numerical quantity—the empty set—that is smaller than one (2). Scientists therefore assumed that the concept of “nothing” as a numerical quantity was beyond the reach of any animal. Recent studies on cognitively advanced vertebrates challenge this view, however. Monkeys and birds can not only distinguish numerical quantities (3) but also grasp the empty set as the smallest quantity on the mental number line (4, 5). On page 1124 of this issue, Howard et al. (6) show that the honey bee, a small insect on a branch very remote from humans on the animal tree of life, also belongs to the elite club of animals that comprehend the empty set as the conceptual precursor of the number zero.
Non-Local Means Algorithm for Image Denoising

Images are often corrupted with noise during acquisition, transmission and retrieval from storage media. So the need for efficient image de-noising methods has grown with the massive and easy production of digital images and movies. Furthermore, de-noising is often necessary as a pre-processing step in image compression, segmentation recognition etc. Therefore, de-noising has been an important and widely studied problem in image processing and computer vision. Basically, the image de-noising methods are divided into two types: local and non-local. The methods that only exploit the spatial redundancy in local neighborhoods are referred as Local methods. The methods that estimate pixel intensity based on information from the whole image and thereby exploiting the presence of similar patterns and features in an image are referred as NonLocal. A non local method called as Non-Local Means[3] estimates a noise-free pixel intensity as a weighted average of all pixel intensities in the image, and the weights are proportional to the similarity between the local neighbourhood of the pixel being processed and local neighbourhoods of surrounding pixels The method is quite spontaneous and powerful that results in comparable PSNR and visual quality to other non-local methods. Key-Words: denoising; local neighbourhood; MSE; Non-local Means; PSNR; visual quality
Storm water simulation: Modeling and analysis of a three-dimensional cloud

A simulation of storm water is presented for developing a physical understanding of storm runoff from urban basins. A new three- dimensional cloud model simulates storm rainfall. The model contains nine prognostic equations: three momentum equations, pressure and thermodynamic equations, three moisture and water equations, and a subgrid kinetic energy equation. A par ticular coordinate transformation is used. It maps a simulation domain with an irregular lower boundary onto a rectangular domain. A finite difference scheme which conserves heat, mois ture, water substances, and subgrid-scale motion solves the trans formed equations. The rainfall is averaged in space and time for a basin to obtain the hyetograph. We use the Road Research Lab oratory Hydrograph method (the RRL method) to compute the storm runoff hydrograph for the basin from the hyetograph. A simulation result for a simplified atmospheric condition is presented.
High-Performance Biomass-Based Flexible Solid-State Supercapacitor Constructed of Pressure-Sensitive Lignin-Based and Cellulose Hydrogels.

Employing renewable, earth-abundant, environmentally friendly, low-cost natural materials to design flexible supercapacitors (FSCs) as energy storage devices in wearable/portable electronics represents the global perspective to build sustainable and green society. Chemically stable and flexible cellulose and electroactive lignin have been employed to construct a biomass-based FSC for the first time. The FSC was assembled using lignosulfonate/single-walled carbon nanotubeHNO3 (Lig/SWCNTHNO3) pressure-sensitive hydrogels as electrodes and cellulose hydrogels as an electrolyte separator. The assembled biomass-based FSC shows high specific capacitance (292 F g-1 at a current density of 0.5 A g-1), excellent rate capability, and an outstanding energy density of 17.1 W h kg-1 at a power density of 324 W kg-1. Remarkably, the FSC presents outstanding electrochemical stability even suffering 1000 bending cycles. Such excellent flexibility, stability, and electrochemical performance enable the designed biomass-based FSCs as prominent candidates in applications of wearable electronic devices.
Breathlessness and Restrictive Lung Disease: An Important Diabetes-Related Feature in Patients with Type 2 Diabetes

Background: Diabetes mellitus is a significant comorbidity of interstitial lung disease (ILD). Objectives: The aim of this study was to investigate the incidence of restrictive lung disease (RLD) and ILD in patients with prediabetes and type 2 diabetes (T2D). Methods: Forty-eight nondiabetics, 68 patients with prediabetes, 29 newly diagnosed T2D, and 110 patients with long-term T2D were examined for metabolic control, diabetes-related complications, breathlessness, and lung function. Five participants with T2D, breathlessness, and RLD underwent multidetector computed tomography (MDCT) and a Six-Minute Walk Test (6MWT). Lung tissue from 4 patients without diabetes and from 3 patients with T2D was histologically examined for presence of pulmonary fibrosis. Results: Breathlessness in combination with RLD was significantly increased in patients with prediabetes and T2D (p < 0.01). RLD was found in 9% of patients with prediabetes, in 20% of patients with newly diagnosed T2D, and in 27% of patients with long-term T2D. Thus, patients with long-term T2D had an increased risk of RLD (OR 5.82 [95% CI 1.71–20.5], p < 0.01). RLD was significantly associated with glucose metabolism and albuminuria (p < 0.01); furthermore, presence of nephropathy increased the risk of RLD (OR 8.57 [95% CI 3.4–21.9], p < 0.01) compared to nondiabetics. MDCT revealed ILD in 4 patients, the 6MWT correlated with the extent of ILD, and histological analysis showed fibrosing ILD in patients with T2D. Conclusions: This study demonstrates increased breathlessness and a high prevalence of RLD in patients with T2D, indicating an association between diabetes and fibrosing ILD.
On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation

Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed-loop control. In particular, optogenetics—the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface application-specified integrated circuit (ASIC) for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08 <italic> μ</italic>V<inline-formula><tex-math notation="LaTeX">$_{\text{rms}}$</tex-math></inline-formula>) recording channels optimized for recording local field potentials (LFPs) (0.1–300 Hz bandwidth, <inline-formula> <tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 5 mV range, sampled 10-bit@4 kHz), which are more stable for chronic applications. For stimulation, it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1 mA range) and pulse-width modulation for high-radiance light emitting diodes (LEDs). The system includes a fully digital interface using a serial peripheral interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a finite state machine (FSM), which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially available 0.35 <italic> μ</italic>m CMOS technology occupying a 1.95 mm <inline-formula><tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 1.10 mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, <italic>in vitro</italic> and <italic>in vivo</italic> experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.
Scheduling and adaptive resource allocation on ICIC with testbed implementation

Inter-Cell Interference Coordination (ICIC) is introduced to mitigate interference at cell-edge User Equipments (UE). A variety of ICIC approaches were proposed in previous literature. However, most of them focused only on resource allocation methods, which were not practical enough for implementation. In our ICIC design, we propose not only a resource allocation scheme but also a scheduling method. The proposed resource optimizer can find an ICIC solution efficiently. The proposed scheduler can maintain the performance of the solution in each Transmission Time Interval (TTI). Both of the resource optimizer and the scheduler are compatible with the testbed. Simulation results shows our proposed schemes outperformed other literature findings in edge throughput and edge UE fairness. Testbed results also shows real gain and the feasibility of our algorithms.
Hardware design of disparity computation for stereo vision using guided image filtering

Disparity estimation is the key operation in stereo matching. In general, there are two categories of stereo matching methods: global and local. Local stereo matching methods are fast due to less computation while global methods can generate more accurate depth information at the cost of more computation complexity. This paper presents a low-cost and high-quality local stereo matching design which utilizes mean filtering and guided image filtering to improve the quality of depth computation. Mean filtering is implemented using a moving-sum method, instead of the conventional integral image method, in order to reduce bit-width of internal memory buffers. Vertical-striped-based guided image filtering is applied to the weighted median filtering for disparity refinement with low memory cost. Experimental results show that the proposed design can achieve satisfactory disparity estimation with much smaller hardware cost compared with prior similar designs.
Sensitivity to natural 3D image transformations during eye movements

The saccadic suppression effect, in which visual sensitivity is reduced significantly during saccades, has been suggested as a mechanism for masking graphic updates in a 3D virtual environment. In this study, we investigate whether the degree of saccadic suppression depends on the type of image change, particularly between different natural 3D scene transformations. The user observed 3D scenes and made a horizontal saccade in response to the displacement of a target object in the scene. During this saccade the entire scene translated or rotated. We studied six directions of transformation corresponding to the canonical directions for the six degrees of freedom. Following each trial, the user made a forced-choice indication of direction of the scene change. Results show that during horizontal saccades, the most recognizable changes were rotations along the roll axis.
A formal approach to multimedia information systems development

As multimedia data penetrates many information systems the need for database support for the multimedia data evolves and so does the need for a formal development methodology. Audio and video media items represent data streams with an inherent temporal property. To adequately and correctly model multimedia transactions, it is vital to guarantee both temporal and functional correctness by using mechanisms that ensure adherence to, and preservation of, media item timing constraints. Consequently such systems require more rigorous treatment to ensure the systems function correctly. This paper describes the specification of synchronization among media items using the Timed CSP specification language in a declarative fashion and shows how temporal constraints among media items may be specified. It also presents a real-time approach that provides constructs to specify temporal constraints in both dynamic and static forms of the different media item types. The paper focuses on issues in the context of the design and development of multimedia systems such as: scheduling of time-constrained triggered transactions; deadline assignment; and concurrency control and commitment of transactions. In particular, the paper focuses on the innovative approach through which advanced multimedia systems can be developed within a distinct framework. This methodology is very useful when composing complex multimedia presentations. It may also lead to the development of a sophisticated authoring tool.
Reactive Oxygen Species-Mediated Neurodegeneration is Independent of the Ryanodine Receptor in Caenorhabditis elegans

Despite the significant impacts on human health caused by neurodegeneration, our understanding of the degeneration process is incomplete. The nematode Caenorhabditis elegans is emerging as a genetic model organism well suited for identification of conserved cellular mechanisms and molecular pathways of neurodegeneration. Studies in the worm have identified factors that contribute to neurodegeneration, including excitotoxicity and stress due to reactive oxygen species (ROS). Disruption of the gene unc-68, which encodes the ryanodine receptor, abolishes excitotoxic cell death, indicating a role for calcium (Ca2+) signaling in neurodegeneration. We tested the requirement for unc-68 in ROS-mediated neurodegeneration using the genetically encoded photosensitizer KillerRed. Upon illumination of KillerRed expressing animals to produce ROS, we observed similar levels of degeneration in wild-type and unc-68 mutant strains. Our results indicate that ROS-mediated cell death is independent of unc-68 and suggest multiple molecular pathways of neurodegeneration.
Pilot study to examine the effects of indoor daylight exposure on depression and other neuropsychiatric symptoms in people living with dementia in long-term care communities

A 12-week study was conducted in eight dementia care communities involving 77 participants addressing the hypothesis that an intervention of increasing indoor exposure to daylight will reduce depression and other neuropsychiatric symptoms. At four communities, staff were enlisted to increase daylight exposure by taking participants to a perimeter room with daylight exposure for socialization in the morning (8:00–10:00 AM) each day. At the other four communities, a control group were taken to a similar sized area without daylight for socialization under typical electrical lighting conditions. Participants in the daylight intervention experienced an average decrease over the trial in the Neuropsychiatric Inventory Nursing Home Version (NPI-NH) scores (p=0.33) and the Cornell Scale for Depression in Dementia (CSDD) scores (p=0.025), while the control participants showed average but nonsignificant increases in both NPI-NH (p=0.33) and CSDD (p=0.13). Difference in outcome changes of the intervention group achieved statistical significance for CSDD (p=0.01) but not for NPI-NH (p=0.17). Our results suggest that increased exposure to daylight can reduce depression in people living with dementia.
A New Weighted Algorithm Based on the Uneven Spatial Resolution of RSSI for Indoor Localization

The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm weights the reference points’ coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of the Wi-Fi RSSI. Therefore, in order to improve the positioning accuracy of traditional location algorithms, this paper proposes a new weighted algorithm based on the physical distance of the RSSI. Experiments were conducted in an office building and the results demonstrate that the proposed method considerably outperforms the KNN, Euclidian-W-KNN, Manhattan-W-KNN, EWKNN, LiFS, and GPR in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.