Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Network Slicing Using Utility Algorithms and Stochastic Optimization

Network slicing is a key enabler for next-generation 5G services. Slices are designed to offer different services by conjoining virtual network functions (VNFs) through a logical network. We propose a dynamic slicing algorithm, which is based on utility theory and facilitates the growth, provisioning, dimensioning and deletion of network slices. The proposed algorithm is based on a utility model that regulates the virtual topology of VNFs in data-centers. The algorithm optimizes the number of slices as well as instantiated VNFs, thereby creating/modifying and destroying network slices of appropriate granularity. In addition, we also propose a stochastic optimization formulation, which handles uncertainty in service requests. We simulate the algorithm for a multi-data-center model with a large set of slices. Our results indicate dynamism, robustness and scalability of the proposed algorithm.
Robust Algorithm for Denoising of Photon-Limited Dual-Energy Cone Beam CT Projections

Dual-Energy CT offers significant advantages over traditional CT imaging because it offers energy-based awareness of the image content and facilitates material discrimination in the projection domain. The Dual-Energy CT concept has intrinsic redundancy that can be used for improving image quality, by jointly exploiting the high- and low-energy projections. In this paper we focus on noise reduction. This work presents the novel noise-reduction algorithm Dual Energy Shifted Wavelet Denoising (DESWD), which renders high-quality Dual-Energy CBCT projections out of noisy ones. To do so, we first apply a Generalized Anscombe Transform, enabling us to use denoising methods proposed for Gaussian noise statistics. Second, we use a 3D transformation to denoise all the projections at once. Finally we exploit the inter-channel redundancy of the projections to create sparsity in the signal for better denoising with a channel-decorrelation step. Our simulation experiments show that DESWD performs better than a state-of-the-art denoising method (BM4D) in limited photon-count imaging, while BM4D achieves excellent results for less noisy conditions.
BAENET: A Brain Age Estimation Network with 3D Skipping and Outlier Constraint Loss

The potential pattern changes in brain micro-structure can be used for the brain development assessment in children and adolescents by MRI scans. In this paper, we propose a highly accurate and efficient end-to-end brain age estimation network (BAENET) on T1-weighted MRI images. On the network, 3D skipping and outlier constraint loss are designed to accommodate deeper network and increase the robustness. Besides, we incorporate the neuroimaging domain knowledge into stratified sampling for better generalization ability for datasets with different age distributions, and gender learning for more gender-specific features during modeling. We verify the effectiveness of the proposed method on the public ABIDE2 and ADHD200 benchmark, consisting of 382 and 378 normal children scans respectively. Our BAENET achieves MAE of 1.11 and 1.16, significantly outperforming the best reported methods by 5.1 % and 9.4%.
Effects of colonisation by different strains of Coniothyrium minitans on the viability of sclerotia of Sclerotinia sclerotiorum

White mold is a major disease in commercial soybean production. An effective measure to reduce the negative effects of Sclerotinia sclerotiorum is the use of bio-fungicides. Strains of Coniothyrium minitans were isolated and efficacy tests against S. sclerotiorum was studied. The efficacy of pycnidiospores sprays of strain N09 (GenBank Accession No HQ908274) from Iowa, USA and strain CON/M/91-08 of Contans® WG were compared in a series of experiments. Sclerotia viability was significantly (P < 0.05) lower in both sclerotia-infested-sterilized-soils (SISS) and sclerotia-infested-unsterilized-soils (SIUS) sprayed with N09 compared with CON/M/91-08 and control at 3°C for 75d and 90d sampling. Similarly, sclerotia viability was significantly (P < 0.05) lower at 23°C for 45, 60 and 75d sampling in SISS and 45, 75 and 90 d sampling in SIUS compared with CON/M/91-08 and control. In contrast, viability of N09 colonies were significantly (P < 0.05) higher than that of CON/M/91-08 both at 3°C and 23°C in SISS across sampling periods. While in SIUS, N09 colonies were significantly higher at 3°C for 15, 30, 45, 75 and 90 d sampling, and at 23°C for 30, 60 and 75 d sampling. Also, (1) N09 had a faster growth rate and produced 1.5 times more pycnidiospores than CON/M/91-08; (2) mycoparasitism by N09 was faster than CON/M/91-08; and (3) co-inoculation of sclerotia and the strains, N09 showed lower sclerotia reproduction than CON/M/91-08. Our data suggest that the new strain N09 has a greater efficiency than CON/M/91-08 in killing sclerotia.
Differences in the Biology and Ecology of Arboreal Marsupials in Forests of Southeastern Australia

I present information on the biology and ecology of eight species of arboreal marsupials that inhabit montane ash forests of the central highlands of Victoria, southeastern Australia. Major differences occur in diet, body mass, home range, reproductive biology, social structure, mating systems, habitat requirements, and use of nesting sites in large trees with hollows. Differences in the biology and ecology of arboreal marsupials indicate differences may occur in their response to disturbances by humans in montane ash forests, such as clearcutting to produce wood and pulp for the production of paper. An array of different approaches to management of these forests may be needed to conserve the complete assemblage of arboreal marsupials including establishment of large reserves, implementation of networks of corridors, and retention of large trees on sites that are logged.
RLayout: Interior Design System Based on Reinforcement Learning

The advanced artificial intelligence technologies have promoted the development of other industries. As a combination of AI technologies and art, intelligent design has become a research hotspot in recent years. Intelligent design uses computer to simulate human thinking activities with AI technologies, enabling the computers to undertake more complex design tasks in a better way. At the same time, reinforcement learning plays an increasingly important role in many fields, such as system control, game theory, computer network, decision making, etc. In this work, an intelligent design system based on the reinforcement learning algorithm was proposed, and corresponding reward rules were developed according to the design principles. The system can help the designer to generate the optimal scheme automatically, which can be extended and applied to find the best scheme in many problems, including interior design, daily schedule, resource arrangement, etc. Our demo shows that the system can offer excellent design scheme according to pre-set reward rules, and reduce workload of related designers.
Network Energy-Saving Adjustment Routing Under Changing Demands: Models and Algorithms

Routing is key to ensuring the normal operation of a network. The rapid development of networks and the continuous expansion in scale have placed high demands on network energy-saving routing. In this study, we described the problem of network energy-saving adjustment routing under the condition of point-to-point demand changes. The influences of point-to-point demand changes on routing scheme adjustment and network energy consumption are considered, and optimal adjustment of the routing scheme when the demand in the network changes from one moment to another is studied. A 0-1 nonlinear integer programming routing adjustment model is established to adjust the costs and minimize network routing energy consumption. Furthermore, it is proven that the problem is NP-hard, therefore two algorithms are designed: a heuristic algorithm based on the depth-first search and a heuristic algorithm based on k-shortest paths. The validity of the routing adjustment model and the algorithm is verified for both a small network and a medium-to-large size network.
A 0.55V 6.3uW/MHz Arm Cortex-M4 MCU with Adaptive Reverse Body Bias and Single Rail SRAM

An ultra-low-voltage (ULV) Arm® Cortex®-M4 MCU in 22FDX technology is presented, operating at 0.55V. The circuit employs adaptive body bias (ABB) in a reverse bias scheme with independent control of NMOS and PMOS device performance and has been implemented using a fully ABB-aware sign-off approach. It contains 256 KiB SRAM based on an area efficient 6T bitcell with ABB capability, enabling single supply operation at 0.55V with high yield. A testchip is validated at wafer test and achieves robust operation at 40MHz frequency with 6. 6µW leakage power, 6µW ABB generator power and only 6. 3µW/MHz total CoreMark power consumption.
Practical Aspects of Employing Antireverse Engineering

& A TENSION EXISTS between transparency and protecting the confidentiality of the inner workings of electronic systems. The acquirer of the electronic system may desire or be required to perform its own assurance of the system’s fitness for use, requiring access to those innards. For example, the acquirer may need to ensure that the electronic system is interoperable with the acquirer’s other systems (e.g., semantic interoperability among heterogeneous Internet of Things devices). This might require detailed knowledge of the electronic system’s application programming interfaces or system-specific network protocols. As another example, the acquirer may be required by a regulatory authority to determine whether the electronic system can be used safely in particular context of use, necessitating the collection of information about the system for use in hazard analyses. On theotherhand, theprovider of the electronic system must weigh the risk of exposing some or all of its intellectual property associated with the electronic system to scrutiny by external parties; intellectual property is a valuable asset that needs to be protected from misappropriation and misuse such as bills of materials, designs, and proprietary processes. For example, thedesignof thefinite state machine controller for a robotic surgery tool may be left undocumented andhidden from the acquirer because the provider believes that there is no legitimate reason an acquirer would need to know that level of detail about such an integral and commercially valuable part of the system. Operating at either end of the spectrum—full or no transparency—does not serve the acquirer or provider well: Both parties have a vested interest in managing the risk associated with the electronic system. Striking a balance means the acquirer and provider both accept some, but not necessarily the same, level of risk. Finding a balance is not easy given that the cardinality between provider and acquirer is usually one-to-many. It is unlikely that all of the potential acquirers of the electronic system will have the same level of risk aversity. It would also behappenstance that theprovider had the same level of trust in all of the actual and potential acquirers (i.e., its customer base). An additional level of complexity arises when the electronic system consists of components integrated from two or more providers: The system integrators and component providers should come to consensus regarding risk assumption. In addition, there may not be a middle ground. An acquirer may be intent on misappropriating Digital Object Identifier 10.1109/MITP.2019. 2944110
Software Tool to Read, Represent, Manipulate, and Apply N-Dimensional Spatial Transforms

Spatial transforms formalize mappings between coordinates of objects in biomedical images. Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images. Image registration is a prominent task present in nearly all standard image processing and analysis pipelines. The proliferation of software implementations of image registration methodologies has resulted in a spread of data structures and file formats used to preserve and communicate transforms. This segregation of formats hinders the compatibility between tools and endangers the reproducibility of results. We propose a software tool capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM). The proposed software is subject to continuous integration tests to check the compatibility with each supported tool after every change to the code base (https://github.com/poldracklab/nitransforms). Compatibility between software tools and imaging formats is a necessary bridge to ensure the reproducibility of results and enable the optimization and evaluation of current image processing and analysis workflows.
Tooth Segmentation and Labeling from Digital Dental Casts

This paper presents an approach to automatic and accurate segmentation and identification of individual teeth from digital dental casts via deep graph convolutional neural networks. Instead of performing the teeth-gingiva and inter-tooth segmentation in two separate phases, the proposed method enables the simultaneous segmentation and identification of the gingiva and teeth. We perform the vertex-wise feature learning via the feature steered graph convolutional neural network (FeaStNet) [1] that dynamically updates the mapping between convolutional filters and local patches from digital dental casts. The proposed framework handles the tightly intertwined segmentation and labeling tasks with a novel constraint on crown shape distribution and concave contours to remove ambiguous labeling of neighboring teeth. We further enforce the smooth segmentation using the pairwise relationship in local patches to penalize rough and inaccurate region boundaries and regularize the vertex-wise labeling in the training process. The qualitative and quantitative evaluations on the digital dental casts obtained in the clinical orthodontics demonstrate that the proposed method achieves efficient and accurate tooth segmentation and produces performance improvements to the state-of-the-art.
L-Carnitine Reduces Myocardial Oxidative Stress and Alleviates Myocardial Ischemia-Reperfusion Injury by Activating Nuclear Transcription-Related Factor 2 (Nrf2)/Heme Oxygenase-1 (HO-1) Signaling Pathway

Background Myocardial ischemia-reperfusion injury (IRI) is an important injury mechanism of myocardial infarction. The purpose of this study was to explore the effects of L-carnitine (LC) on myocardial IRI and its mechanism. Material/Methods The IRI model was made by ligating the left anterior descending coronary artery. Then, we injected LC intraperitoneally into the rats of the experimental group to assess the effect of LC on IRI rats by use of serum markers, Western blot, and qRT-PCR. H9c2 cells were cultured and then treated with hypoxia-reoxygenation. The effect of LC on oxidative stress, apoptosis, and nuclear transcription-related factor 2/heme oxygenase-1 (Nrf2/HO-1) signaling pathway of H9c2 cells were detected by Western blot, RT-PCR, and flow cytometry. Results LC significantly reduced malondialdehyde (MDA), creatine kinase (CK), and lactate dehydrogenase (LDH) levels in rat myocardial tissue and increased superoxide dismutase (SOD) expression. LC also increased the expression of SOD1/2 and Bcl-2 in rat myocardial tissue and H9c2 cells and decreased the expression of caspase3/8 and Bax. In addition, LC increased the expression of Nrf2/HO-1 signaling pathway-related molecules in H9c2 cells and increased the activity of the Nrf2/HO-1 signaling pathway. Moreover, inhibition of the Nrf2/HO-1 signaling pathway attenuated the protective effect of LC on H9c2 cells. Conclusions LC can activate the Nrf2/HO-1 signaling pathway and reduce oxidative stress and apoptosis in cardiomyocytes, thereby reducing myocardial IRI.
Experimental Study of 1/f1+α Noise in Transient Leakage Current of Metal–Insulator–Metal With Stacked High-k Polycrystalline Films

We have observed and analyzed the <inline-formula> <tex-math notation="LaTeX">${1}/{f}^{{1}+\alpha }$ </tex-math></inline-formula> noise in transient leakage current through a metal–insulator–metal stacked high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> capacitor of TiN–ZrO<sub>2</sub>–TiO<sub>2</sub>–TiN. The ZrO<sub>2</sub> and TiO<sub>2</sub> films, formed by atomic layer deposition, are polycrystalline and show geometrical variety at interfaces (i.e., grain boundaries). Two types of transient leakage current are observed: 1) the monotonically decreasing component with power law dependence and 2) the uneven component having power law dependence. To analyze the uneven component in time domain, we assumed that the power law decay occurs due to a gradual change in the redistribution of electrons between interfaces of ZrO<sub>2</sub>–TiN and ZrO<sub>2</sub>–TiO<sub>2</sub>. The frequency-domain analysis shows that the <inline-formula> <tex-math notation="LaTeX">${1}/{f}^{{1}+\alpha }$ </tex-math></inline-formula> noise comes from the transient leakage of direct tunneling and trap-assisted tunneling (<inline-formula> <tex-math notation="LaTeX">$\alpha > {0}$ </tex-math></inline-formula>). In particular, the noise in the uneven component, the random telegraph noise part (<inline-formula> <tex-math notation="LaTeX">${\alpha \sim {1}}{)}$ </tex-math></inline-formula>, relates to local trap states in a grain boundary affected by phonon scattering. In addition, the analytical method we developed in this article shows an excellent agreement with various measurements of the transient gate leakage current.
High-performance non-enzymatic glucose detection: using a conductive Ni-MOF as an electrocatalyst.

Conductive metal-organic frameworks (MOFs) have been studied extensively in applications like water electrolysis, gas storage, and supercapacitors due to their high conductivity and large pore volume. In this communication, we report the first use of a conductive Ni-MOF as a non-noble-metal catalyst for efficient electro-oxidation of glucose in alkaline electrolyte. As an electrochemical sensor for glucose detection, this Ni-MOF shows a fast response time of less than 3 s, a low detection limit of 0.66 μM (S/N = 3), and a high sensitivity of 21 744 μA mM-1 cm-2. This glucose sensor also displays excellent selectivity, stability and reproducibility, and its application for the detection of glucose in real samples is also demonstrated successfully.
ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl. ch.
Automatic Depression Detection via Facial Expressions Using Multiple Instance Learning

Depression affects more than 300 million people around the world and is the leading cause of disability in USA for individuals ages from 15 to 44. The damage of it compares to most common diseases like cancer, diabetes, or heart disease according to the WHO report. However, people with depression symptoms sometimes do not receive proper treatment due to access barriers. In this paper, we propose a method that automatically detects depression using only landmarks of facial expressions, which are easy to collect with less privacy exposure. We deal with the coarse-grained labels i.e. one final label for the long-time series video clips, which is the common cases in applications, through the integration of feature manipulation and multiple instance learning. The effectiveness of our method is compared to other visual based methods, and our method even outperforms multi-modal methods that use multiple modalities.
Digital Microfluidics: Automation Microscale Liquid Handling

Digital Microfluidics (DMF) is a new technology that enables the manipulation of liquid droplets on open surfaces. The main power of DMF is its ability to automate sample processing for many chemical and biological assays. On-chip droplets can act as microvessels to transport and manipulate reagents and samples to perform any required assay. DMF-compatible processes include the aliquoting, transporting, mixing, heating, purification, and extraction of reagents and samples. A wide range of biological samples can be manipulated inside the droplets, including cells, bacteria, and algae, in addition to different biological molecules, from amino acids to DNA and proteins. Nearly all physiological fluids, such as blood, serum, plasma, urine, saliva, and tears, can be manipulated on DMF devices. These capabilities motivated researchers to implement DMF in many applications.
Angular Velocity Analysis Boosted by Machine Learning for Helping in the Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2 ± 3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8 ± 9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor.