Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator

We show that Deep Convolutional Neural Network (CNN) implementations of computational imaging tasks exhibit spatially correlated values. We exploit this correlation to reduce the amount of computation, communication, and storage needed to execute such CNNs by introducing Diffy, a hardware accelerator that performs Differential Convolution. Diffy stores, communicates, and processes the bulk of the activation values as deltas. Experiments show that, over five state-of-the-art CNN models and for HD resolution inputs, Diffy boosts the average performance by 7.1× over a baseline value-agnostic accelerator [1] and by 1.41× over a state-of-the-art accelerator that processes only the effectual content of the raw activation values [2]. Further, Diffy is respectively 1.83× and 1.36× more energy efficient when considering only the on-chip energy. However, Diffy requires 55% less on-chip storage and 2.5× less off-chip bandwidth compared to storing the raw values using profiled per-layer precisions [3]. Compared to using dynamic per group precisions [4], Diffy requires 32% less storage and 1.43× less off-chip memory bandwidth. More importantly, Diffy provides the performance necessary to achieve real-time processing of HD resolution images with practical configurations. Finally, Diffy is robust and can serve as a general CNN accelerator as it improves performance even for image classification models.
Recanalization before Thrombectomy in Tenecteplase vs. Alteplase-Treated Drip-and-Ship Patients

The Extending the time for Thrombolysis in Emergency Neurological Deficits–Intra-Arterial using Tenecteplase (EXTEND-IA TNK) trial recently showed 2-fold higher early recanalization (ER) rate before mechanical thrombectomy (MT) following intravenous thrombolysis (IVT) with tenecteplase 0.25 mg/kg, as compared to alteplase 0.9 mg/kg. However, most included patients were directly admitted to MT-capable centres (‘mothership’ paradigm), implying short IVT-to-MT delays. Tenecteplase may therefore be preferred in the mothership setting. Here, we assessed ER rate before MT following tenecteplase or alteplase in patients transferred for MT from a non-MT-capable centre (‘drip-and-ship’ paradigm), i.e., implying longer IVT-to-MT delays, currently the most frequent situation. Inclusion criteria for the present retrospective study were (1) acute stroke with large vessel occlusion treated with IVT with tenecteplase 0.25 mg/kg or alteplase 0.9 mg/kg; and (2) ER evaluation ≤3 hours from IVT start on pre-MT first angiographic run or non-invasive vascular imaging. Tenecteplase patients were all from one large French non-MT-capable centre, which based on previous trials and for practical convenience opted to use tenecteplase off-label before transfer for MT. Alteplase patients were from 23 other French non-MT-capable centres. ER Recanalization before Thrombectomy in Tenecteplase vs. Alteplase-Treated Drip-and-Ship Patients
ROI-based Fully Automated Liver Registration in Multi-phase CT Images

In this paper, we propose a registration method for fully automated liver tumor detection. Multiple phases CT is used for the detection of the liver tumor because multiple phase CT can give different characteristic features of lesions for each time phases. Registration accuracy is important when obtaining image features from multiple time phases. However, since each time phases have different image density characteristics, therefore registration of multi-phase CT is a challenging task. In this paper, we propose a robust initial alignment method independent of changing image density features in each time phase, and deformable registration method with region of interests (ROI) as liver region extracted by U-Net. Our proposed method is evaluated on 15 patient image sets. This method is applied to the early arterial phase and the equilibrium phase to registries. Experimental results show that segmentation of early arterial phase is 83% and registration is 93% accuracy.
Semi-Automatic Segmentation of Paranasal Sinus from CT images Using Fully Convolutional Networks

In this paper, we present a novel image representation approach for paranasal sinus segmentation in computed tomography (CT) images of the brain. Our proposed method is based on a fully convolutional network (FCN), which is extensively used in a variety of computer vision applications. Experimental results demonstrate that our proposed method is efficient and effective for paranasal sinus segmentation.
Working Memory and Attention Influence Antisaccade Error Rate in Schizophrenia

Abstract Objectives: Antisaccade error rate has been proposed to be one of the most promising endophenotypes for schizophrenia. Increased error rate in patients has been associated with working memory, attention and other executive function impairments. The relationship between antisaccade error rate and other neuropsychological processes in patients compared to healthy controls has not been explored in depth. This study aimed to replicate the finding of heightened antisaccade error rate in patients and determine which cognitive processes were most strongly associated with antisaccade error rate in both patients and controls. In addition, the study investigated whether different antisaccade task paradigms engage different cognitive processes. Methods: One hundred and ninety-one participants (54 patients with schizophrenia/schizoaffective disorder and 137 controls) completed the antisaccade task, which included both gap and step task parameters. Neuropsychological measures were obtained using the MCCB and the Stroop task. Results: The current study replicated a pronounced antisaccade error rate deficit in patients. In patients, working memory variance was most significantly associated with antisaccade errors made during the step condition, while attentional processes were most associated with errors made during the gap condition. In controls, overall global cognitive performance was most associated with antisaccade rates for both gap and step conditions. Conclusions: The current study demonstrates that in schizophrenia patients, but not controls, elevated antisaccade error rate is associated with attention and working memory, but not with global cognitive impairment or psychopathological processes. Our novel findings demonstrate that the gap and step conditions of the antisaccade task engage different cognitive processes. (JINS, 2019, 25, 174–183)
Beacon-Based Localization Middleware for Tracking in Medical and Healthcare Environments

This research paper proposes a Middleware model for a Localization System that may be applied in Healthcare environments such as Hospitals or Nursing Homes to track staff, patients, visitors and equipment. It investigates literature regarding indoor localization methods and limitations to determine a suitable algorithm that may be implemented in an infrastructure oriented software. The methodology used to build and test the software is explained. It then illustrates the concept of the Localization Middleware and how it might be used when deployed indoor premises, inside such rooms as a hospital wards, In terms of the functional responsibilities, it is expected to offer an effective implementation of the distance measurement algorithm for Received Signal Strength and the Linear Least-Squares localization algorithm. The simulations of the localization algorithm with the given simulation results are looking promising. However, the real-time tests demonstrated that the range measurement was insufficiently precise to be reliable. Given a more accurate and reliable distance measurement, a more precise localization result could be attained.
Advancements in Electrical Machines Design Brought by the Modular Construction

In the last years, the developments in the field of electrical machines are speeding up more and more to keep in line with the rapid industrial advancements and fast growing requirements. The most emerging targets of the electrical machines designers are cost reductions, efficiency and reliability improvements, etc. One of the approaches to achieve these is the modular construction of the electrical machines. In the paper a survey is made on the progresses brought by this approach in the field of electrical machines. Also, some typical examples of the modular construction possibilities of some electrical machines are included.
Control of a Ball and Beam System under Intermittent Feedback

The control of a ball and beam system has been a well-known research subject and there have been a large number of related research results by far. To our best knowledge, however, all those existing results have basically assumed that their proposed controllers work under ‘good’ or ‘normal’ feedback conditions, which specifically means in this case that there is not any time period during which feedback is disrupted. In this paper, motivated by a recent open-loop time optimal result of [1], we suggest a new switching control method which can keep the states of a ball and beam system within certain bounds when there occurs a nontrivial feedback disruption during the operation of feedback controllers. Both simulation and experimental results are provided for verification.
Separation and Characterization of Prostate Cancer Cell Subtype according to Their Motility Using a Multi-Layer CiGiP Culture

Cancer cell metastasis has been recognized as one hallmark of malignant tumor progression; thus, measuring the motility of cells, especially tumor cell migration, is important for evaluating the therapeutic effects of anti-tumor drugs. Here, we used a paper-based cell migration platform to separate and isolate cells according to their distinct motility. A multi-layer cells-in-gels-in-paper (CiGiP) stack was assembled. Only a small portion of DU 145 prostate cancer cells seeded in the middle layer could successfully migrate into the top and bottom layers of the stack, showing heterogeneous motility. The cells with distinct migration were isolated for further analysis. Quantitative PCR assay results demonstrated that cells with higher migration potential had increased expression of the ALDH1A1, SRY (sex-determining region Y)-box 2, NANOG, and octamer-binding transcription 4. Increased doxorubicin tolerance was also observed in cells that migrated through the CiGiP layers. In summary, the separation and characterization of prostate cancer cell subtype can be achieved by using the multi-layer CiGiP cell migration platform.
Channel Length Modeling and Experimental Demonstration of Adaptive Threshold Under-Sampled CamCom for Low-SNR Transmitters

Under-sampled camera communication (CamCom) systems realized using fast shutter speeds limit the communication channel length and impose restrictions on the minimum signal-to-noise ratio (SNR) requirement of LED based transmitters. In this paper, we present an empirical model to estimate the effective channel length of a CamCom system. Based upon the model, a transceiver methodology is proposed and demonstrated to support low-SNR transmitters, thereby increasing the effective communication distance of an under-sampled CamCom link. Moreover, an adaptive threshold decoding technique is presented to make the system robust against the variable frame rate phenomenon. The proposed methodology can be utilized to increase the communication distance of under-sampled CamCom links for infrastructure-to-vehicle communication employing low-SNR transmitters.
Design of Wideband Third-Order Bandpass Filters Using Broadside-Coupled Resonators in 0.13- $\mu$ m (Bi)-CMOS Technology

In this paper, two third-order bandpass filters (BPFs) designed for millimeter-wave applications are presented. Unlike previously published ones, the proposed designs use a “cell-based” approach, which utilizes identical broadside-coupled resonators (BCRs) with series and shunt capacitors. The capacitors are mainly used as <inline-formula> <tex-math notation="LaTeX">$J$ </tex-math></inline-formula>-inverters to achieve the desired frequency responses. To fully understand the operational mechanism of the presented approach, both the BCR and BPFs are analyzed using the simplified <italic>LC</italic>-equivalent circuit models. To prove the concept, both BPFs are implemented in a standard 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> silicon–germanium bipolar complementary metal–oxide–semiconductor technology. According to the on-wafer measurement results, the BPFs exhibit the excellent performance including flat in-band responses with relatively large harmonic suppression. The first design has a 1-dB bandwidth from 23.9 to 39.7 GHz with an insertion loss of 3.9 dB at the center frequency of 31 GHz. The stopband attenuation is better than 45 dB at 58 GHz. The 1-dB bandwidth of the second design covers from 26.7 to 44.3 GHz with an insertion loss of 3.1 dB at the center frequency of 35 GHz, and stopband attenuation up to 35 dB is achieved at 59 GHz. Both designs occupy an identical area of 0.073 mm<sup>2</sup> (<inline-formula> <tex-math notation="LaTeX">$0.248\times0.294$ </tex-math></inline-formula> mm<sup>2</sup>), excluding the G–S–G testing pads.
Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data

Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., 2018), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations.
Higher gene expression stability during aging in long-lived giant mole-rats than in short-lived rats

Many aging-associated physiological changes are known to occur in short- and long-lived species with different trajectories. Emerging evidence suggests that numerous life history trait differences between species are based on interspecies variations in gene expression. Little information is available, however, about differences in transcriptome changes during aging between mammals with diverging lifespans. For this reason, we studied the transcriptomes of five tissue types and two age cohorts of two similarly sized rodent species with very different lifespans: laboratory rats (Rattus norvegicus) and giant mole-rats (Fukomys mechowii), with maximum lifespans of 3.8 and more than 20 years, respectively. Our findings show that giant mole-rats exhibit higher gene expression stability during aging than rats. Although well-known aging signatures were detected in all tissue types of rats, they were found in only one tissue type of giant mole-rats. Furthermore, many differentially expressed genes that were found in both species were regulated in opposite directions during aging. This suggests that expression changes which cause aging in short-lived species are counteracted in long-lived species. Taken together, we conclude that expression stability in giant mole rats (and potentially in African mole-rats in general) may be one key factor for their long and healthy life.
Non-Invasive and Flexible Electrodes Based on Multimaterial Fiber for sEMG Signal Detection

A novel sensor made of a multimaterial metal-polymer-glass hollow-core fiber connected to a smart wearable sensing device, for surface electromyography (sEMG) measurement, is proposed. The high flexibility of the fibers allows their easy integration into stretchable garments and textiles, without compromising the comfort of the users. The multimaterial fiber electrodes were first optimized in length and inter-electrode distance by recording different sEMG signals. With the first prototype achieved, sEMG signals from the forearm flexor, and the biceps muscles were recorded. The collected data were compared to a commercial grade sensor and the use of Ag/AgCl electrodes. Frequency and time domain content analysis show that the new flexible sensor enables the recording of comparable sEMG signals, in addition to being suitable for use on muscle zones on which rigid electrode type or wearable sensor cannot be used. This opens up a wide range of applications, in particular for assistive technology devices, and telemedicine.
A case study on risk assessment for personal care robot (Mobile servant robot)

As the use of personal care robots increases, cases of damage have occurred and it is urgent to take countermeasures against them. The most basic method to identify the hazardous risk factors and estimate the likelihood (frequency) and severity (intensity) of the injury or illness caused by them is risk assessment, which is a system that establishes reduction measures (Guidelines for the Assessment of Workplace Hazards, 2017). In this study, risk assessment was conducted according to the safety requirements for robots presented in the international standard. As a result, we analyzed the risk assessment for improving safety by deriving measures and measures for reducing risks. The results of this study can be used as a basic data for the safety evaluation to lower the risk to the level required by the standard and receive the safety certification of the personal care robot in the future.
Effective Products Categorization with Importance Scores and Morphological Analysis of the Titles

During the past few years, the e-commerce platforms and marketplaces have enriched their services with new features to improve their user experience and increase their profitability. Such features include relevant products suggestion, personalized recommendations, query understanding algorithms and numerous others. To effectively implement all these features, a robust products categorization method is required. Due to its importance, the problem of the automatic products classification into a given taxonomy has attracted the attention of multiple researchers. In the current literature, we encounter a broad variety of solutions, ranging from supervised and deep learning algorithms, as well as convolutional and recurrent neural networks. In this paper we introduce a supervised learning method which performs morphological analysis of the product titles by extracting and processing a combination of words and n-grams. In the sequel, each of these tokens receives an importance score according to several criteria which reflect the strength of the correlation of the token with a category. Based on these importance scores, we also propose a dimensionality reduction technique to reduce the size of the feature space without sacrificing much of the performance of the algorithm. The experimental evaluation of our method was conducted by using a real-world dataset, comprised of approximately 320 thousand product titles, which we acquired by crawling a product comparison Web platform. The results of this evaluation indicate that our approach is highly accurate, since it achieves a remarkable classification accuracy of over 95%.
Intensity and Wavelength Division Multiplexing FBG Sensor System Using a Raman Amplifier and Extreme Learning Machine

A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped.
Die Rolle der Inflammation bei Arthroseschmerzen

Osteoarthritis (OA) is one of the major causes of chronic pain. Although OA has long been considered a non-inflammatory “wear and tear” disease leading to loss of articular cartilage, recent findings provide convincing evidence that inflammatory mechanisms play a pivotal role in the pathophysiology of OA. In OA mononuclear cells (e. g. T-cells and macrophages) infiltrate the synovial membrane and the levels of pro-inflammatory cytokines in peripheral blood and synovial fluid samples are elevated. Increased release of inflammatorymediators including interleukin (IL) IL-1β, IL-6, IL8, IL-15 und tumor necrosis factor alpha (TNF-α) induces the expression of proteolytic enzymes such as matrix metalloproteinases resulting in cartilage breakdown. Molecular and cellular interactions between the immune and nervous systemare also involved in the development of OA-related pain. Inflammatory mediators including IL-6 und TNF-α lead to peripheral sensitization of joint nociceptors and growth factors (e. g. NGF) trigger the expression of TRPV1 channels in primary afferents. Moreover, neuropeptides reduce the threshold of nociceptors of OA joints. The current review highlights the role of inflammatory mechanisms in OAinduced joint pain considering clinical signs of inflammation and major inflammatory pathways.