Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranger: Boosting Error Resilience of Deep Neural Networks through Range Restriction

With the emerging adoption of deep neural networks (DNNs) in the HPC domain, the reliability of DNNs is also growing in importance. As prior studies demonstrate the vulnerability of DNNs to hardware transient faults (i.e., soft errors), there is a compelling need for an efficient technique to protect DNNs from soft errors. While the inherent resilience of DNNs can tolerate some transient faults (which would not affect the system's output), prior work has found there are critical faults that cause safety violations (e.g., misclassification). In this work, we exploit the inherent resilience of DNNs to protect the DNNs from critical faults. In particular, we propose Ranger, an automated technique to selectively restrict the ranges of values in particular DNN layers, which can dampen the large deviations typically caused by critical faults to smaller ones. Such reduced deviations can usually be tolerated by the inherent resilience of DNNs. Ranger can be integrated into existing DNNs without retraining, and with minimal effort. Our evaluation on 8 DNNs (including two used in self-driving car applications) demonstrates that Ranger can achieve significant resilience boosting without degrading the accuracy of the model, and incurring negligible overheads.
A Modified SIR Model for the COVID-19 Contagion in Italy

The purpose of this work is to give a contribution to the understanding of the COVID-19 contagion in Italy. To this end, we developed a modified Susceptible-Infected-Recovered (SIR) model for the contagion, and we used official data of the pandemic up to March 30th, 2020 for identifying the parameters of this model. The non standard part of our approach resides in the fact that we considered as model parameters also the initial number of susceptible individuals, as well as the proportionality factor relating the detected number of positives with the actual (and unknown) number of infected individuals. Identifying the contagion, recovery and death rates as well as the mentioned parameters amounts to a non-convex identification problem that we solved by means of a two-dimensional grid search in the outer loop, with a standard weighted least-squares optimization problem as the inner step.
Robotic Table Tennis with Model-Free Reinforcement Learning

We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies.
Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model

Neural networks are complex algorithms that loosely model the behaviour of the human brain. They play a significant role in computational neuroscience and artificial intelligence. The next generation of neural network models is based on the spike timing activity of neurons: spiking neural networks (SNNs). However, model parameters in SNNs are difficult to search and optimise. Previous studies using genetic algorithm (GA) optimisation of SNNs were focused mainly on simple, feedforward, or oscillatory networks, but not much work has been done on optimising cortex-like recurrent SNNs. In this work, we investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations. We considered a cortical column based SNN comprising 1000 Izhikevich spiking neurons for computational efficiency and biologically realism. The model parameters explored were the neuronal biased input currents. First, we found for this particular SNN, the optimal parameter values for targeted population averaged firing activities, and the convergence of algorithm by ~100 generations. We then showed that the GA optimal population size was within ~16-20 while the crossover rate that returned the best fitness value was ~0.95. Overall, we have successfully demonstrated the feasibility of implementing GA to optimize model parameters in a recurrent cortical based SNN.
Local and $2$-local automorphisms of simple generalized Witt algebras

In this paper, we prove that every invertible $2$-local or local automorphism of a simple generalized Witt algebra over any field of characteristic $0$ is an automorphism. In particular, every $2$-local or local automorphism of Witt algebras $W_n$ is an automorphism for all $n\in \mathbb{N}$. But some simple generalized Witt algebras indeed have $2$-local (and local) automorphisms that are not automorphisms.
Event-Triggered Quantized Average Consensus via Mass Summation

We study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the nodes, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze novel distributed averaging algorithms which operate exclusively on quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and rely on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocols on quantized values and show that their execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value represented as the ratio of two quantized values that is equal to the exact average. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithms.
Detection of FLOSS version release events from Stack Overflow message data

Topic Detection and Tracking (TDT) is a very active research question within the area of text mining, generally applied to news feeds and Twitter datasets, where topics and events are detected. The notion of "event" is broad, but typically it applies to occurrences that can be detected from a single post or a message. Little attention has been drawn to what we call "micro-events", which, due to their nature, cannot be detected from a single piece of textual information. The study investigates micro-event detection on textual data using a sample of messages from the Stack Overflow Q&A platform in order to detect Free/Libre Open Source Software (FLOSS) version releases. Micro-events are detected using logistic regression models with step-wise forward regression feature selection from a set of LDA topics and sentiment analysis features. We perform a detailed statistical analysis of the models, including influential cases, variance inflation factors, validation of the linearity assumption, pseudo R sq. measures and no-information rate. Finally, in order to understand the detection limits and improve the performance of the estimators, we suggest a method for generating micro-event synthetic datasets and use them identify the micro-event detectability thresholds.
bioelectrical domain wall motion using all-optical electrophysiology in an engineered stable cell line and in human iPSC-derived myoblasts. Bioelectrical domain wall migration may occur during embryonic development and during physiological signaling processes in polarized tissues. These results demo

Electrical signaling in biology is typically associated with action potentials, transient spikes in membrane voltage that return to baseline. Here we show theoretically and experimentally that homogeneous or nearly homogeneous tissues can undergo spontaneous symmetry breaking into domains with different resting potentials, separated by stable bioelectrical domain walls. Transitions from one resting potential to another can occur through long-range migration of these domain walls. We map bioelectrical domain wall motion using all-optical electrophysiology in an engineered stable cell line and in human iPSC-derived myoblasts. Bioelectrical domain wall migration may occur during embryonic development and during physiological signaling processes in polarized tissues. These results demonstrate a novel form of bioelectrical pattern formation and long-range signaling.
Electrochromism in Electrolyte-Free and Solution Processed Bragg Stacks

Achieving an active manipulation of colours has huge implications in optoelectronics, as colours engineering can be exploited in a number of applications, ranging from display to lightning. In the last decade, the synergy of the highly pure colours of 1D photonic crystals, also known as Bragg stacks, with electro-tunable materials have been proposed as an interesting route to attain such a technologically relevant effect. However, recent works rely on the use of liquid electrolytes, which can pose issues in terms of chemical and environmental stability. Here, we report on the proof-of-concept of an electrolyte free and solution-processed electrochromic Bragg stack. We integrate an electro-responsive plasmonic metal oxide, namely indium tin oxide, in a 1D photonic crystal structure made of alternating layers of ITO and TiO2 nanoparticles. In such a device we observed 15 nm blue-shift upon application of an external bias (5 V), an effect that we attribute to the increase of ITO charge density arising from the capacitive charging at the metal oxide/dielectric interface and from the current flowing throughout the porous structure. Our data suggest that electrochromism can be attained in all-solid state systems by combining a judicious selection of the constituent materials with device architecture optimisation.
20-MAD - 20 Years of Issues and Commits of Mozilla and Apache Development

Data of long-lived and high profile projects is valuable for research on successful software engineering in the wild. Having a dataset with different linked software repositories of such projects, enables deeper diving investigations. This paper presents 20-MAD, a dataset linking the commit and issue data of Mozilla and Apache projects. It includes over 20 years of information about 765 projects, 3.4M commits, 2.3M issues, and 17.3M issue comments, and its compressed size is over 6 GB. The data contains all the typical information about source code commits (e.g., lines added and removed, message and commit time) and issues (status, severity, votes, and summary). The issue comments have been pre-processed for natural language processing and sentiment analysis. This includes emoticons and valence and arousal scores. Linking code repository and issue tracker information, allows studying individuals in two types of repositories and provide more accurate time zone information for issue trackers as well. To our knowledge, this the largest linked dataset in size and in project lifetime that is not based on GitHub.
HD 196390: A Tight Correlation of Differential Abundances with Condensation Temperature

Bedell et al. (2018) give precision differential abundances for 79 mostly G-dwarf stars. We correct these abundances for Galactic chemical evolution in a manner similar to that used by these authors but with parameters derived from linear fits to plots of [El/H] vs. age in lieu of [El/Fe]. We examine the resulting abundances for correlations with the 50% condensation temperature using values from both Lodders (2003) and Wood et al. (2019), and compare with the results of Bedell et al. HD 196390 is distinct in having the most significant correlation of the 79-star sample. We report statistics for a subset of stars with lower significance, but of some interest.
Community Detection for Gossip Dynamics with Stubborn Agents

We consider a community detection problem for gossip dynamics with stubborn agents in this paper. It is assumed that the communication probability matrix for agent pairs has a block structure. More specifically, we assume that the network can be divided into two communities, and the communication probability of two agents depends on whether they are in the same community. Stability of the model is investigated, and expectation of stationary distribution is char-acterized, indicating under the block assumption, the stationary behaviors of agents in the same community are similar. It is also shown that agents in different communities display distinct behaviors if and only if state averages of stubborn agents in different communities are not identical. A community detection algorithm is then proposed to recover community structure and to estimate communication probability parameters. It is verified that the community detection part converges in finite time, and the parameter estimation part converges almost surely. Simulations are given to illustrate algorithm performance.
A � ective Automotive User Interfaces – Reviewing the State of Emotion Regulation in the Car

A � ective technology o � ers exciting opportunities to improve road safety by catering to human emotions. Modern car interiors enable the contactless detection of user states, paving the way for a systematic promotion of safe driver behavior through emotion regulation. We review the current literature regarding the impact of emotions on driver behavior and analyze the state of emotion regulation approaches in the car. We summarize challenges for a � ective interaction in form of cultural aspects, technological hurdles and methodological considerations, as well as opportunities to improve road safety by reinstating drivers into an emotionally balanced state. The purpose of this review is to outline the community’s combined knowledge for interested researchers, to provide a focussed introduction for practitioners and to identify future directions for a � ective interaction in the car
Landau-Zener transitions in a fermionic dissipative environment

We study Landau-Zener transitions in a fermionic dissipative environment where a two-level (up and down states) system is coupled to two metallic leads kept with different chemical potentials at zero temperature. The dynamics of the system is simulated by an iterative numerically exact influence functional path integral method. In the pure Landau-Zener problem, two kinds of transition (from up to down state and from down to up state) probability are symmetric. However, this symmetry is destroyed by coupling the system to the bath. In addition, in both kinds of transitions, there exists a nonmonotonic dependence of the transition probability on the sweep velocity; meanwhile nonmonotonic dependence of the transition probability on the system-bath coupling strength is only shown in one of them. As in the spin-boson model, these phenomena can be explained by a simple phenomenological model.
Towards dark space stabilization and manipulation in driven dissipative Majorana platforms

An important ingredient in the quest for a quantum information toolbox is to provide protection to elementary quantum bits and arrays thereof. Here, the authors show that combining the topological features of Majorana-based qubits and protected dark states of drive and dissipation protocols gives rise to an exceptionally good performance. For a single qubit device, arbitrary pure states can be stabilized. Two qubit setups support protected dark space manifolds, facilitating stabilization of fault-tolerant Bell states. Once a working Majorana platform becomes available, only standard hardware requirements would be needed to implement these schemes.
Active exploration in adaptive model predictive control *

A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant systems subject to bounded disturbances and parametric uncertainty in the state-space matrices. Online set-membership identification is performed to reduce the uncertainty and thus control affects both the informativity of identification and the system’s performance. The main contribution of the paper is to include this dual effect in the MPC optimization problem using a predicted worst-case cost in the objective function. This allows the controller to perform active exploration, that is, the control input reduces the uncertainty in the regions of the parameter space that have most influence on the performance. Additionally, the MPC algorithm ensures robust constraint satisfaction of state and input constraints. Advantages of the proposed algorithm are shown by comparing it to a passive adaptive MPC algorithm from the literature.
Topological optimization of hybrid quantum key distribution networks

With the growing complexity of quantum key distribution (QKD) network structures, aforehand topology design is of great significance to support a large-number of nodes over a large-spatial area. However, the exclusivity of quantum channels, the limitation of key generation capabilities, the variety of QKD protocols and the necessity of untrusted-relay selection, make the optimal topology design a very complicated task. In this research, a hybrid QKD network is studied for the first time from the perspective of topology, by analyzing the topological differences of various QKD protocols. In addition, to make full use of hybrid networking, an analytical model for optimal topology calculation is proposed, to reach the goal of best secure communication service by optimizing the deployment of various QKD devices and the selection of untrusted-relays under a given cost limit. Plentiful simulation results show that hybrid networking and untrusted-relay selection can bring great performance advantages, and then the universality and effectiveness of the proposed analytical model are verified.
DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction

Accurately predicting drug-target binding affinity (DTA) in silico is a key task in drug discovery. Most of the conventional DTA prediction methods are simulation-based, which rely heavily on domain knowledge or the assumption of having the 3D structure of the targets, which are often difficult to obtain. Meanwhile, traditional machine learning-based methods apply various features and descriptors, and simply depend on the similarities between drug-target pairs. Recently, with the increasing amount of affinity data available and the success of deep representation learning models on various domains, deep learning techniques have been applied to DTA prediction. However, these methods consider either label/one-hot encodings or the topological structure of molecules, without considering the local chemical context of amino acids and SMILES sequences. Motivated by this, we propose a novel end-to-end learning framework, called DeepGS, which uses deep neural networks to extract the local chemical context from amino acids and SMILES sequences, as well as the molecular structure from the drugs. To assist the operations on the symbolic data, we propose to use advanced embedding techniques (i.e., Smi2Vec and Prot2Vec) to encode the amino acids and SMILES sequences to a distributed representation. Meanwhile, we suggest a new molecular structure modeling approach that works well under our framework. We have conducted extensive experiments to compare our proposed method with state-of-the-art models including KronRLS, SimBoost, DeepDTA and DeepCPI. Extensive experimental results demonstrate the superiorities and competitiveness of DeepGS.
MYO1C facilitates arrival at the Golgi apparatus through stabilization of branched actin

We aim at the identification of myosin motor proteins that control trafficking at the Golgi apparatus. In addition to the known Golgi-associated myosins MYO6, MYO18A and MYH9 (myosin IIA), we identify MYO1C as a novel player at the Golgi. We demonstrate that depletion of MYO1C induces Golgi apparatus fragmentation and decompaction. MYO1C accumulates at dynamic structures around the Golgi apparatus that colocalize with Golgi-associated actin dots. Interestingly, MYO1C depletion leads to loss of cellular F-actin, and Golgi apparatus decompaction is also observed after the inhibition or loss of the Arp2/3 complex. We show that the functional consequences of MYO1C depletion is a delay in the arrival of incoming transport carriers, both from the anterograde and retrograde routes. We propose that MYO1C stabilizes branched actin at the Golgi apparatus that facilitates the arrival of incoming transport at the Golgi.
Exploratory re-encoding of Yellow Fever Virus genome: new in- sights for the design of live-attenuated viruses Short title: Exploratory re-encoding of Yellow Fever Virus genome

Virus attenuation by genome re-encoding is a pioneering approach for generating effective live-attenuated vaccine candidates. Its core principle is to introduce a large number of synonymous substitutions into the viral genome to produce stable attenuation of the targeted virus. Introduction of large numbers of mutations has also been shown to maintain stability of the attenuated phenotype by lowering the risk of reversion and recombination of re-encoded genomes. Identifying mutations with low fitness cost is pivotal as this increases the number that can be introduced and generates more stable and attenuated viruses. Here, we sought to identify mutations with low deleterious impact on the in vivo replication and virulence of yellow fever virus (YFV). Following comparative bioinformatic analyses of flaviviral genomes, we categorized synonymous transition mutations according to their impact on CpG/UpA composition and secondary RNA structures. We then designed 17 re-encoded viruses with 100-400 synonymous mutations in the NS2A-to-NS4B coding region of YFV Asibi and Ap7M (hamster-adapted) genomes. Each virus contained a panel of synonymous mutations designed according to the above categorisation criteria. The replication and fitness characteristics of parent and re-encoded viruses were compared in vitro using cell culture competition experiments. In vivo laboratory hamster models were also used to compare relative virulence and immunogenicity characteristics. Most of the re-encoded strains showed no decrease in replicative fitness in vitro. However, they showed reduced virulence and, in some instances, decreased replicative fitness in vivo. Importantly, the most attenuated of the re-encoded strains induced robust, protective immunity in hamsters following challenge with Ap7M, a virulent virus. Overall, the introduction of transitions with no or a marginal increase in the number of CpG/UpA dinucleotides had the mildest impact on YFV replication and virulence in vivo. Thus, this strategy can be incorporated in procedures for the finely tuned creation of substantially re-encoded viral genomes.