Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Data Science in Circulating Tumor DNA Detection: A Promising Avenue Towards Liquid Biopsy

The circulating tumor DNA (ctDNA), as a promising biomarker of liquid biopsy, has potential clinical relevance on the molecular diagnosis and monitoring of cancer. However, the trace concentration level of ctDNA in the peripheral blood restricts its extensive clinical application. Recently, high-throughput-based methodologies have been leveraged to improve the sensitivity and specificity of ctDNA detection, showing a promising avenue towards liquid biopsy. This review briefly summarizes the high-throughput data features concerned by current ctDNA detection strategies and the technical obstacles, potential solutions, and clinical relevance of current ctDNA profiling technologies. We also highlight future directions improving the limit of detection of ctDNA for better clinical application. This review may serve as a reference for the crosslinks between data science and ctDNA-based liquid biopsy, benefiting clinical translation in advanced cancer diagnosis.
The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

Seismic activity in volcanic settings could be the signature of processes that include magma dynamics, hydrothermal activity and geodynamics. The main goal of this study is to analyze the seismicity of Lipari Island (Southern Tyrrhenian Sea) to characterize the dynamic processes such as the interaction between pre-existing structures and hydrothermal processes affecting the Aeolian Islands. We deployed a dense seismic array of 48 autonomous 3-component nodes. For the first time, Lipari and its hydrothermal field are investigated by a seismic array recording continuously for about a month in late 2018 with a 0.1–1.5 km station spacing. We investigate the distribution and evolution of the seismicity over the full time of the experiment using self-organized maps and automatic algorithms. We show that the sea wave motion strongly influences the background seismic noise. Using an automatic template matching approach, we detect and locate a seismic swarm offshore the western coast of Lipari. This swarm, made of transient-like signals also recognized by array and polarization analyses in the time and frequency domains, is possibly associated with the activation of a NE-SW fault. We also found the occurrence of hybrid events close to the onshore Lipari hydrothermal system. These events suggest the involvement of hot hydrothermal fluids moving along pre-existing fractures. Seismological analyses of one month of data detect signals related to the regional tectonics, hydrothermal system and sea dynamics in Lipari Island.
Commentary: Two Ways of Thinking About Self-Control

The basic premise of Vosgerau et al.’s (2020; henceforth VSH) article is one that few consumer behavior researchers and psychologists would debate: “self-control conflicts are subjective” and “not all consumers pursue the same superordinate long-term goals” (p. 187). While the dominant paradigm defines self-control as a consumer’s choice to desist from hedonic consumption, VSH outline self-control failures as choices violating superordinate long-term goals (whether hedonic or utilitarian) that entail the anticipation of regret. Therefore, the central message of VSH’s framework is that self-control does not require abstinence from pleasure (i.e., exerting self-control 6= sacrificing pleasure). VSH also argue that this conceptualization is vital for the construct validity of self-control studies in consumer research. This commentary agrees with the idea that the choice of hedonic consumption (e.g., chocolate cake) is not always equated with failures in self-control. In this regard, Moayery et al. (2019b) showed that if consumers do not endorse any standards regarding their diet, they might buy more unhealthy snacks even when enough self-regulatory resources are available. Similarly, it has been suggested that the desire for unhealthy food cannot be translated into temptation, unless the person follows a healthy diet (Hofmann and Kotabe, 2012). At the same time, in this paper, I wish to draw attention to three issues that help in making the distinction between the perspective presented by VSH and my perspective on self-control. These issues are self-monitoring failures, ego-depletion effects, and reflective and impulsive determinants of self-control. Therefore, building on VSH’s framework, this commentary proposes an expanded, more inclusive perspective on self-control problems. In order to flesh out this idea, I attempt to (1) clarify that people often fail to detect a conflict of self-control because they fail to self-monitor, (2) challenge the idea that ego-depletion is not a real phenomenon, and (3) highlight the role of reflective and impulsive aspects of self-control. This commentary also discusses a direction for future studies with emphasis on non-conscious forms of self-control and some methodological considerations for measuring impulses.
Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

For safe and reliable use of the battery for electric vehicles, diagnosis of its state-of-health (SOH) is essential. This is achieved by battery management systems (BMSs) that can monitor changes in the present capacity of the battery. Considering their limited computational resources, an efficient scheme is necessary. The data-driven metamodel is therefore used instead of complex battery models, which can simply capture changes in the shape of the charge curve as a battery ages. In consequence of the model reformulation, the charge curve refers to the time elapsed for charging against voltage. Under constant current charging, using time instead of capacity is favorable for computationally inexpensive BMSs. The aging-relevant parameter in the metamodel is estimated in the least-squares sense. In practice, this is often difficult as the shape of the charge curve, mostly its early part, is distorted by varying battery conditions before charging. For tolerating this distortion, a robust scheme is also required. The weighted least-squares is thus used such that the early part is given less weights whereas the later part is given more weights. The BMS-integrated metamodel and its parameter estimator are validated by using batteries with different SOH, which concludes an estimation error less than 3%. © The Author(s) 2016. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. [DOI: 10.1149/2.0841606jes] All rights reserved.
The rational development of improved in vitro maturation of bovine oocytes

In vitro embryo production has vastly improved over the past decade through the study of the in vivo environment and the metabolic requirements of embryos. In contrast, in vitro oocyte maturation (IVM) culture conditions have remained relatively unchanged and are suboptimal. The aim of this thesis was to create improved systems for bovine IVM by studying the metabolic profiles and requirements of intact cumulus oocyte complexes (COCs) during IVM and determining the ion and energy substrate composition of bovine follicular fluid (FF). Glucose, pyruvate and oxygen consumption of bovine COCs increased 2-fold over the 24 h IVM period, with glucose being the preferred energy substrate. While initially the majority of glucose consumed by COCs is metabolised via glycolysis (Llactate production), a considerable proportion of glucose is used as a substrate for extracellular matrix (ECM) synthesis towards the end of IVM. Glucosamine (an intermediate substrate of hyaluronic acid) supplementation of IVM media lead to decreased glucose consumption and incorporation into ECM during FSH-stimulated expansion. Biochemical analyses of bovine FF demonstrated that the concentration of some ions and energy substrates varied with follicle size. Although follicular glucose concentrations increased with follicle size, levels were ~2-fold lower than that found in Tissue Culture Medium (TCM199), the most commonly employed medium for bovine IVM. Synthetic Follicular Fluid Medium (SFFM) was created, based on the FF data and also contained glucosamine. Two different glucose concentrations were examined, 2.3 mM glucose to represent physiological concentrations and 5.6 mM glucose, the same concentration as is in TCM199. Culturing COCs in different glucose concentrations manipulated the completion of nuclear maturation and this was dependant on concentration, gonadotrophin supplementation and the timing of media changes, demonstrating the importance of this substrate to meiotic competence.
Smart Building Energy Management: Load Profile Prediction using Machine Learning

Smart buildings are gaining popularity with the surfacing trend of smart grid and smart city. Effective energy management is a major aspect of the smart building management system that demands accurate prediction of building electrical energy consumption profile. The paper focuses on a data-driven approach to load profile prediction with the highlighted benefit of a model-free environment. The electricity consumption profile of a commercial smart building is predicted using Gaussian Process Regression (GPR), and a comparative study is carried out to highlight the issues associated with Polynomial Regression, Artificial Neural Network (ANN), Dynamic Mode Decomposition (DMD), and Hankeled DMD (HDMD). For testing the effectiveness of the proposed methodology, various test scenarios were conducted and from the result, it is observed that the HDMD and GPR are preferred techniques to provide reliable prediction, which is beneficial for arranging a specific demand response schedule to earn benefits like financial rewards and carbon footprint curtailment.
A Generalized CUR decomposition for matrix pairs

We propose a generalized CUR (GCUR) decomposition for matrix pairs $(A, B)$. Given matrices $A$ and $B$ with the same number of columns, such a decomposition provides low-rank approximations of both matrices simultaneously, in terms of some of their rows and columns. We obtain the indices for selecting the subset of rows and columns of the original matrices using the discrete empirical interpolation method (DEIM) on the generalized singular vectors. When $B$ is square and nonsingular, there are close connections between the GCUR of $(A, B)$ and the DEIM-induced CUR of $AB^{-1}$. When $B$ is the identity, the GCUR decomposition of $A$ coincides with the DEIM-induced CUR decomposition of $A$. We also show a similar connection between the GCUR of $(A, B)$ and the CUR of $AB^+$ for a nonsquare but full-rank matrix $B$, where $B^+$ denotes the Moore--Penrose pseudoinverse of $B$. While a CUR decomposition acts on one data set, a GCUR factorization jointly decomposes two data sets. The algorithm may be suitable for applications where one is interested in extracting the most discriminative features from one data set relative to another data set. In numerical experiments, we demonstrate the advantages of the new method over the standard CUR approximation; for recovering data perturbed with colored noise and subgroup discovery.
Digital Offset for RRAM-based Neuromorphic Computing: A Novel Solution to Conquer Cycle-to-cycle Variation

Resistance variation in memristor device hinders the practical use of resistive random access memory (RRAM) crossbars as neural network (NN) accelerators. Previous fault-tolerant methods cannot effectively handle cycle-to-cycle variation (CCV). Many of them also use a pair of positive-weight and negative-weight crossbars to store a weight matrix, which implicitly enhances the fault tolerance but doubles the hardware cost. This paper proposes a novel solution that dramatically reduces the NN accuracy loss under CCV, while still using a single crossbar to store a weight matrix. The key idea is to introduce digital offsets into the crossbar, which further enables two techniques to conquer CCV. The first is a variation-aware weight optimization method that determines the optimal target weights to be written into the crossbar; the second is a post-writing tuning method that optimally sets the digital offsets to recover the accuracy loss due to variation. Simulation results show that the accuracy maintains the ideal value for LeNet with MNIST and only drops by 2.77% over the ideal value for ResNet-18 with CIFAR-10 under a large resistance variation. Moreover, compared to state-of-the-art fault-tolerant methods, our method achieves a better NN accuracy with at least 50% fewer crossbars.
A user-friendly smart charging algorithm based on energy-awareness for different PEV parking scenarios

Many studies show that collaborations between renewable energy sources especially PV arrays and plug-in electric vehicles can have a positive impact on the utilization of both emerging technologies. In fact, the intermittent nature of renewable sources and the high-demand for PEV charging will address by this partnership. This paper proposes a user-friendly algorithm for the control of PEV charging and discharging, aiming to satisfy all involved parties including user, parking owner and grid operator by considering the health of batteries and costs. First, the predicted input data are used to obtain the optimal position of PEVs through an extended linear optimization method in different scenarios. In this regard, three scenarios including uncontrolled, unidirectional and bidirectional are used and their impact on the battery health and charging cost is specified. Then, based on energy-awareness, the user selects one of those scenarios arbitrary by comparing their results. Finally, the real-time controller compensates the errors based on a traditional rule-based method. This procedure is repeated upon on arrival of each PEV, and the results are considered as input for the next step.
Compensation of Transmitter IQ Imbalance in Multi-User Hybrid Beamforming Systems

In this paper, we consider the uplink (UL) communication of a massive multi-user (MU) multiple-input multiple-output (MIMO) hybrid beamforming system (HBFS) in which the transmitting user-equipments are affected by IQ imbalance (IQI). We first show that if the transmitter IQI is not compensated, it will result in a finite ceiling of the UL achievable sum-rate at high signal-to-noise ratio, which only depends on the transmitter IQI matrices and is independent of the propagation channel and the choice of the hybrid combining matrices. This justifies the need for transmitter IQI compensation in massive MU-MIMO HBFS. Therefore, we propose a novel zero-forcing based transmitter IQI compensation algorithm to be implemented at the base station which effectively mitigates the undesired effects of transmitter IQI and is applicable for any channel model and any choice of the number of RF chains. For uncorrelated Rayleigh fading channel, we derive an approximate closed-form expression of the UL sum-rate achieved by the massive MU-MIMO HBFS with the proposed transmitter IQI compensation. Finally, numerical results are presented which confirm the effectiveness of the proposed transmitter IQI compensation algorithm in mitigating the undesired effects of transmitter IQI in different channel environments.
Comparison of GPU Computing Methodologies for Safety-Critical Systems: An Avionics Case Study

Introducing advanced functionalities in safety-critical systems requires using more powerful architectures such as GPUs. However software in safety-critical industries is subject to functional certification, which cannot be achieved using standard GPU programming languages such as CUDA and OpenCL. Fortunately, GPUs are already used in certified critical systems for display tasks, using safety-certified solutions such as OpenGL SC 2.0. In this paper, we compare two state-of-the art graphics-based methodologies, OpenGL SC 2.0 and Brook Auto/BRASIL for the implementation of a prototype avionics case study. We evaluate both methods on a realistic industrial setup, composed by an avionics-grade GPU and a safety-certified GPU driver in terms of development metrics and performance, showing their feasibility.
RoBoa: Construction and Evaluation of a Steerable Vine Robot for Search and Rescue Applications

RoBoa is a vine-like search and rescue robot that can explore narrow and cluttered environments such as destroyed buildings. The robot assists rescue teams in finding and communicating with trapped people. It employs the principle of vine robots for locomotion, everting the tip of its tube to move forward. Inside the tube, pneumatic actuators enable lateral movement. The head carries sensors and is mounted outside at the tip of the tube. At the back, a supply box contains the rolled up tube and provides pressurized air, power, computation, as well as an interface for the user to interact with the system. A decentralized control scheme was implemented that reduces the required number of cables and takes care of the low-level control of the pneumatic actuators. The design, characterization, and experimental evaluation of the system and its crucial components is shown. The complete prototype is fully functional and was evaluated in a realistic environment of a collapsed building where the remote-controlled robot was able to repeatedly locate a trapped person after a travel distance of about 10m.
Electronic properties of dislocations

Dislocations exhibit a number of exceptional electronic properties resulting in a significant increase in the drain current of MOSFETs if defined numbers of these defects are placed in the channel. Measurements on individual dislocations in Si refer to a supermetallic conductivity. A model of the electronic structure of dislocations is proposed based on experimental measurements and tight-binding simulations. It is shown that the high strain level on the dislocation core—exceeding 10 % or more—causes locally dramatic changes in the band structure and results in the formation of a quantum well along the dislocation line. This explains experimental findings (two-dimensional electron gas, single-electron transitions). The energy quantization within the quantum well is most important for supermetallic conductivity.
Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications.
Application of an Output Tracking Technique to Formation Control

Recently we have developed a tracking-control technique that is based on a Newton-Raphson flow, systemoutput prediction, and controller speedup. This paper investigates an application of the technique to a swarm of mobile agents which have to maintain a given formation while in motion. The control is essentially distributed, where each agent, except for the leading one, collects information only from the agents immediately ahead of it. The motion dynamics of the agents are two dimensional and linear, but the tracking control technique can be extended to nonlinear systems as well. We investigate a specific example by simulation, and the results indicate convergence and several additional salient features of the control technique.
Static Timing Analysis of OPC UA PubSub

Industrial automation is changing towards higher integration and seamless communication. A stepping stone is end-to-end real-time machine-to-machine communication, now becoming feasible with technologies such as time-sensitive networking (TSN) and OPC Unified Architecture (OPC UA) publish-subscribe. While TSN takes care of communication, the OPC UA stack’s execution time behavior remains unknown. This paper highlights experiences made while adjusting the OPC UA subscriber of the open62541 stack for worst-case execution time (WCET) analysis. Two directly connected time-predictable T-CREST platforms hosting the publisher and subscriber delivered end-to-end timing measures validating the WCET estimates. The paper concludes by outlining further research with several time-predictable publishers and subscribers.
Efficient AUTOSAR-Compliant CAN-FD Frame Packing with Observed Optimality

With the trend towards automated driving, Controller Area Network (CAN) is migrating to CAN with Flexible Data-Rate (CAN-FD), where frame packing (i.e., packing signals of various periods, deadlines, and payloads into frames following the standard CAN- FD format) is critical to address the high bandwidth demand with limited resources. Existing works have applied Integer Linear Programming (ILP), which easily gets intractable as the number of signals to be packed increases, or proposed heuristics, which are not able to obtain the optimal solution. In addition, the security model employed does not meet the AUTOSAR SecOC specification. This paper reports a novel frame packing approach for CAN-FD with an AUTOSAR-compliant security model. We establish the theory that extending the existing frame to pack signals with the same period leads to shorter WCTT (worst-case transmission time) and thus lower bus utilization compared to creating a new frame. Following this principle, the design space is tremendously pruned. As shown in the comprehensive experiments, only 10−9 of the original size or even a smaller portion needs to be explored, while the optimality is kept. The computational time is correspondingly reduced, generating solutions within 15 minutes to large-scale problems that are otherwise intractable with ILP.