Miracle-dz/UniFAR-FTUs huggingface dataset:
Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks

Emerging memory devices such as resistive switching memory (RRAM) and phase change memory (PCM) are gaining interest as future synapses for smart neuromorphic systems, capable of learning and inference similar to the human brain. Developing neuromorphic systems with emerging memory technologies requires accurate co-design of devices, synapses, and neural networks, aiming at the replication of the fundamental learning processes in the human brain, such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP). This work addresses the development of RRAM synapses for unsupervised learning via STDP. This learning scheme is implemented in a simple one-transistor/one-resistor (1T1R) structure capable of long term potentiation and depression with standard memory-grade RRAM devices. 1T1R synapses are implemented in a spiking neural network (SNN) with feedforward architecture, allowing for the hardware demonstration of unsupervised learning. Recurrent SNNs employing the same fundamental STDP rule are then addressed by simulation of associative learning, pattern reconstruction, and recall of spatiotemporal sequences.
Two Missense Variants Detected in Breast Cancer Probands Preventing BRCA2-PALB2 Protein Interaction

PALB2 (partner and localizer of BRCA2) was initially identified as a binding partner of BRCA2. It interacts also with BRCA1 forming a complex promoting DNA repair by homologous recombination. Germline pathogenic variants in BRCA1, BRCA2 and PALB2 DNA repair genes are associated with high risk of developing breast cancer. Mutation screening in these breast cancer predisposition genes is routinely performed and allows the identification of individuals who carry pathogenic variants and are at risk of developing the disease. However, variants of uncertain significance (VUSs) are often detected and establishing their pathogenicity and clinical relevance remains a central challenge for the risk assessment of the carriers and the clinical decision-making process. Many of these VUSs are missense variants leading to single amino acid substitutions, whose impact on protein function is uncertain. Typically, VUSs are rare and due to the limited genetic, clinical, and pathological data the multifactorial approaches used for classification cannot be applied. Thus, these variants can only be characterized through functional analyses comparing their effect with that of normal and mutant gene products used as positive and negative controls. The two missense variants BRCA2:c.91T >G (p.Trp31Gly) and PALB2:c.3262C >T (p.Pro1088Ser) were detected in two breast cancer probands originally ascertained at Breast Cancer Units of Institutes located in Milan and Bergamo (Northern Italy), respectively. These variants were located in the BRCA2-PALB2 interacting domains, were predicted to be deleterious by in silico analyses, and were very rare and clinically not classified. Therefore, we initiate to study their functional effect by exploiting a green fluorescent protein (GFP)-reassembly in vitro assay specifically designed to test the BRCA2-PALB2 interaction. This functional assay proved to be easy to develop, robust and reliable. It also allows testing variants located in different genes. Results from these functional analyses showed that the BRCA2:p.Trp31Gly and the PALB2:p.Pro1088Ser prevented the BRCA2-PALB2 binding. While caution is warranted when the interpretation of the clinical significance of rare VUSs is based on functional studies only, our data provide initial evidences in favor of the possibility that these variants are pathogenic.
An event-driven probabilistic model of sound source localization using cochlea spikes

This work presents a probabilistic model that estimates the location of sound sources using the output spikes of a silicon cochlea such as the Dynamic Audio Sensor. Unlike previous work which estimated the source locations directly from the interaural time differences (ITDs) extracted from the timing of the cochlea spikes, the spikes are used instead to support a distribution model of the ITDs representing possible locations of sound sources. Results on noisy single speaker recordings show average accuracies of approximately 80% on detecting the correct source locations and an estimation lag of <100ms.
A 96-channel ASIC for sEMG Fatigue Monitoring with Compressed Sensing for Data Reduction

A novel 96-channel ASIC for simultaneous recording of sEMG signals for muscle fatigue monitoring is presented. Each channel is fully differential such that information regarding the median frequency (MDF) of the sEMG signal can be extracted which has been shown to correlate with muscle fatigue. The analogue front-end of each channel includes a tunable band pass filter and a programmable variable gain, consumes 8. 4μW and occupies an area of 0.034mm2. Additionally, the ASIC incorporates compressed sensing (CS) to reduce the data transmitted from all channels into rates achievable using Bluetooth technology. Each on-chip CS module occupies 0.0085mm2 and consumes 17μW per channel with a compression ratio of 10. The reconstructed signal achieves a SNRrecon of 10.2dB and a MARD of 1.7% for the MDF of the sEMG signal, validated using real sEMG recordings. The system architecture is designed in a modular way to ensure scalability for 100+ channels. As such, the system comprises 4 independent readout modules of 24-channels each which include a dedicated multiplexer, ADC and a compressed sensing module for each. The full ASIC is designed in a 0.35 μm CMOS process, occupies an area of 5.82mm2 and has a total power consumption of 3.93mW.
An Efficient Hardware Architecture for the Implementation of Multi-Step Look-Ahead Sigma-Delta Modulators

A hardware architecture for the implementation of Multi-Step Look-Ahead Sigma-Delta Modulators (MSLA SDMs) is presented. MSLA SDMs offer superior performance than conventional single-bit SDMs for a multitude of applications relying on single-bit signal representation. However, traditional look-ahead SDMs have very high algorithmic complexity and their hardware implementation does not allow for real-time operation. MSLA SDMs overcome this problem by transforming the minimization problem associated with traditional look-ahead SDMs. A proof-of-concept FPGA implementation of a specific MSLA SDM is discussed and compared to a conventional single-bit SDM in terms of performance and hardware complexity. It is demonstrated that MSLA SDMs are a viable alternative to conventional single-bit SDMs when better performance with moderate additional hardware complexity are required.
Closed-loop electrophysiology: Past, present and future perspectives and applications

The combination of recording and stimulation into closed-loop electrophysiological systems is one of the most challenging topics in neuroscience and neural engineering. The application of such systems can range from basic neuroscientific questions to neurorehabilitation purposes. In this review, we summarize the main results obtained with a hybrid neurorobotic platform that we developed in the past to perform closed-loop experiments. To minimize the complexity of the biological network, we used cultured neuronal networks kept alive over Micro Electrode Arrays. The robot is characterized by proximity sensors and wheels, allowing it to navigate within an arena with obstacles. The neurorobotic architecture can become an experimental environment for studying the interaction between brains and machines. We also discuss other possible applications of similar closed-loop architectures related to neuroprosthetics and neurorehabilitation.
Resolving Citation Links With Neural Networks

This work demonstrates how neural network models (NNs) can be exploited towards resolving citation links in the scientific literature, which involves locating passages in the source paper the author had intended when citing the paper. We look at two kinds of models: triplet and binary. The triplet network model works by ranking potential candidates, using what is generally known as the triplet loss, while the binary model tackles the issue by turning it into a binary decision problem, i.e., by labeling a candidate as true or false, depending on how likely a target it is. Experiments are conducted using three datasets developed by the CL-SciSumm project from a large repository of scientific papers in the Association for Computational Linguistics (ACL) repository. The results find that NNs are extremely susceptible to how the input is represented: they perform better on inputs expressed in binary format than on those encoded using the TFIDF metric or neural embeddings. Furthermore, in response to a difficulty NNs and baselines had in predicting the exact location of a target, we introduce the idea of approximately correct targets (ACTs) where the goal is to find a region which likely contains a true target rather than its exact location. We show that with the ACTs, NNs consistently outperform Ranking SVM and TFIDF on the aforementioned datasets.
Pros and Cons of Schmitt Trigger Inverters to Mitigate PVT Variability on Full Adders

This paper evaluates the benefits and drawbacks of using Schmitt Trigger (ST) inverters to minimize process, voltage and temperature variability effects on full adders. Variability mainly affects the energy outcomes on full adders, and the use of ST techniques can decrease up to 80% the energy deviation. However, it implies in a significant increase on the energy consumption. Considering the pros and cons, Mirror CMOS FA is the most beneficiated with the ST inverters, with a small impact on the average delay and a significant reduction in the energy and delay deviation.
Synchronization in networks of Rössler oscillators with long-range interactions

The interactions of units embedded in a complex network are modeled here taking into account not only the direct links among them, but also the indirect interaction between distant nodes. We propose a new framework combining the features of network topology with these long-range interactions by applying the concept of d-path Laplacian matrices, and study synchronization of a network of chaotic units. The main effect of considering this new type of interactions is an improvement in synchronization as it can be achieved even in networks originally not synchronizable.
On the Benefit of Adopting Saturable Inductors in Switching-Mode Power-Supplies: A Case Study

We follow up with recent proposals of using inductors working in weak and deep-saturation region to improve the features of switching mode power supplies. To this aim, we use a constant on-time (COT) converter including a saturable inductor as a test vehicle. The inductor model takes into account both hysteresis and dependence on the working temperature of the core and windings. The digital controller of the COT converter is considered too. Electro-thermal simulations are performed to analyse the performances of the overall analog mixed-signal circuit. It is shown that using a proper saturable inductor positively impacts on the design of the converter by mitigating its main drawback of having a working frequency largely dependent on the load conditions and input supply voltage. Some weak points of proposed design are also identified.
An AC-DC Wind Energy Harvesting Circuit with Extended Input-Voltage Range and 95% Tracking Efficiency

A novel wind energy harvesting (WEH) system using micro-watt turbines is proposed in this paper. To extend the range of wind velocities the harvesting system can process and enhance the settling at the same time, we proposed to use the parasitic inductor of the wind turbines for local excitations. Two-stage power converter is utilized in this system, including a regulator and a maximum power extraction (MPE) implemented in the same chip. The smallest input voltage can be as low as 0.25 V and the settling time is less than 0.2 s with a 1 mF rectified capacitor. This work is fabricated in TSMC 0.35-μm process, achieving 63% end-to-end efficiency. The total active area is 7.68 mm2.
Study protocol for evaluating the effectiveness of depression management on gylcaemic control in non-communicable diseases clinics in Malawi

Introduction Depression is associated with negative patient outcomes for chronic diseases and likely affects consistent physical non-communicable diseases (NCDs) care management in relation to clinic attendance and medication adherence. We found no published studies on the integration of depression management in physical NCD clinics in Malawi and assessing its effects on patient and service outcomes. Therefore, the aim of this study is to evaluate the effectiveness of integrating depression screening and management in physical NCD routine care on patient and service outcomes in Malawi. We will also determine the sensitivity and specificity of the Patient Health Questionnaire-9 (PHQ-9) in the detection of depression in NCD clinics. Methods and analysis The study will have two phases. Phase I will involve the validation of the PHQ-9 screening tool for depression, using a cross-sectional study design involving 323 participants, in two specialised physical NCD clinics in one of the 28 districts of Malawi. Using a quasi-experimental study design in four districts of Malawi not involved in the phase I study, the phase II study will evaluate the effectiveness of integrating depression screening (using PHQ-9) and management (based on a specially designed toolkit). Outcomes will be measured at 3 months and 6 months among patients with comorbid diabetes (poorly controlled) and depression attending physical NCD clinics in Malawi. Ethics and dissemination Ethical approval was obtained from the University of Malawi, College of Medicine Research and Ethics Committee (COMREC) on 31 August 2017 (reference P.07/17/2218). The findings will be disseminated through presentations at journal clubs, senior management of the Ministry of Health, national and international conferences as well as submission to peer-reviewed publications. Policy briefs will also be created. Trial registration number PACTR201807135104799.
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

Measuring similarities between unlabeled time series trajectories is an important problem in many domains such as medicine, economics, and vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g. different sampling rates or outliers). Experts typically hand-craft or manually select a specific metric, such as Dynamic Time Warping (DTW), to apply on their data. In this paper, we propose an end-to-end framework, autowarp, that optimizes and learns a good metric given unlabeled trajectories. We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Edit Distance, Euclidean, etc. Autowarp then leverages the representation power of sequence autoencoders to optimize for a member of this warping family. The output is an metric which is easy to interpret and can be robustly learned from relatively few trajectories. In systematic experiments across different domains, we show that autowarp often outperforms hand-crafted trajectory similarity metrics.
Entropy in Quantum Information Theory - Communication and Cryptography

Entropies have been immensely useful in information theory. In this Thesis, several results in quantuminformation theory are collected, most of which use entropy as the main mathematical tool.The first one concerns the von Neumann entropy. While a direct generalization of the Shannon entropyto density matrices, the von Neumann entropy behaves differently. The latter does not, for example, havethe monotonicity property that the latter possesses: When adding another quantum system, the entropycan decrease. A long-standing open question is, whether there are quantum analogues of unconstrainednon-Shannon type inequalities. Here, a new constrained non-von-Neumann type inequality is proven, astep towards a conjectured unconstrained inequality by Linden and Winter.Like many other information-theoretic tasks, quantum source coding problems such as coherent statemerging have recently been analyzed in the one-shot setting. While the case of many independent,identically distributed quantum states has been treated using the decoupling technique, the essentiallyoptimal one-shot results in terms of the max-mutual information by Berta et al. and Anshu at al. had tobring in additional mathematical machinery. We introduce a natural generalized decoupling paradigm,catalytic decoupling, that can reproduce the aforementioned results when applied in a manner analogousto the application of standard decoupling in the asymptotic case.Quantum teleportation is one of the most basic building blocks in quantum Shannon theory. Whileimmensely more entanglement-consuming, the variant of port based teleportation is interesting for appli-cations like instantaneous non-local computation and attacks on quantum position-based cryptography.Port based teleportation cannot be implemented perfectly, and the resource requirements diverge forvanishing error. We prove several lower bounds on the necessary number of output portsNto achieveport based teleportation for given dimension and error. One of them shows for the first time thatNdiverges uniformly in the dimension of the teleported quantum system, for vanishing error. As a byprod-uct, a new lower bound for the size of the program register for an approximate universal programmablequantum processor is derived.Finally, the mix is completed with a result in quantum cryptography. While quantum key distributionis the most well-known quantum cryptographic protocol, there has been increased interest in extendingthe framework of symmetric key cryptography to quantum messages. We give a new definition forinformation-theoretic quantum non-malleability, strengthening the previous definition by Ambainis etal. We show that quantum non-malleability implies secrecy, analogous to quantum authentication.Furthermore, non-malleable encryption schemes can be used as a primitive to build authenticatingencryption schemes. We also show that the strong notion of authentication recently proposed by Garget al. can be fulfilled using 2-designs.
Formulation, Characterization, and Pharmacokinetic Studies of 6-Gingerol-Loaded Nanostructured Lipid Carriers

In this study, an optimized nanostructured lipid carriers (NLCs) were developed and investigated for improving the solubility and oral availability of 6-Gingerol (6G), an active and abundant component of ginger with limited applications due to its poor water solubility plus oral biological availability. The NLCs consisted of a solid lipid (glyceryl monostearate), another liquid lipid (decanoyl/octanoyl-glycerides) and mixed surfactants (Tween 80 and Poloxamer 188), and was prepared by high pressure homogenization method. The optimal 6G-NLC formulation was evaluated through physical properties such as appearance, mean particle size, zeta potential, encapsulation efficiency, and in vitro drug release, alongside techniques viz., transmission electron microscopy (TEM), differential scanning calorimetry (DSC), as well as powder X-ray diffraction (XRD). Pharmacokinetics were also evaluated in rats. The 6G-NLCs prepared with optimal formulation exhibited a homogenous spherical shape with mean particle size and zeta potential of 63.59 ± 5.54 nm and − 12.18 ± 1.06 mV. Encapsulation efficiency and drug loading were 76.71 ± 1.11 and 1.17 ± 0.35%, respectively. In vitro release profile of 6G from NLCs was sustained and fitted with Weibull equation. After oral administration of the 6G-NLCs, drug concentrations in serum, MRT, and AUC0-t were significantly higher as compared with the free 6G suspension. All these results indicated that the developed NLC formulation could be effective and promising drug carriers to improve the water solubility of 6G while sustaining the drug release as well as prolonging in vivo acting time of the drug coupled with oral bioavailability enhancement.
A Digital Background Calibration Scheme for Pipelined ADCs Using Multiple-Correlation Estimation

This paper proposes a digital background calibration scheme for calibrating the linear and the third-order nonlinear gain errors of the residue amplifiers (RAs) in pipelined ADCs. It is based on a novel multiple-correlation estimation (MCE) technique. We define two correction parameters relating to the gain errors of the RA under calibration. By alternately injecting two bi-level pseudo-random signals with designated amplitudes to the RA through the sub-DAC, the desired correction parameters are estimated according to the correlations of the backend ADC's outputs and the injected pseudo-random signals. Two least-mean-square (LMS) loops are adopted to find and to track the optimal values of the correction parameters. Simulation results of a 12-bit pipelined ADC show that the SNDR is improved from 46.4 dB to 73.4 dB with the help of the proposed calibration design. The proposed calibration scheme has the advantages of simple implementation, no restriction on the input signal of the ADC, fast settling, and running in background.
An information-theoretic approach to study activity driven networks

In this work, we leverage the information-theoretic notion of transfer entropy theory to study causal information flow in epidemic spreading over temporal networks. An improved understanding of causal information flow may lead to the early detection of population segments that should be monitored or immunized to enhance epidemic containment. We focus on activity driven networks, which constitute a powerful and elegant paradigm to capture the inherent time-varying nature of contacts and population heterogeneity. Our preliminary results confirm the intuition that individuals who have a higher propensity in contacting others are responsible for the largest information transfer. Moreover, we find that epidemic parameters such as the probability of infection and recovery may dominate the spreading phenomenon over heterogeneities in the contact formation.
The Urbanek Black Box Arc Model in Passive Resonance Circuit Breakers for HVDC Applications

The black box model by Josef Urbanek of electric-arc, in air-blast circuit breakers or in circuit breakers characterized by an electro-negative gas (e.g. SF6) as quenching medium, is adopted to simulate DC current interruption in HVDC applications. In particular, it is used to model the electric arc phenomenon in passive resonance circuit breakers. Owing to its formulation, the Urbanek model (UM) allows both to easily verify design rules proposed in the literature and to provide more accurate ones. Numerical bifurcation analysis and basin of attraction calculation are used to this aim.
Fantom: A scalable framework for asynchronous distributed systems

We describe \emph{Fantom}, a framework for asynchronous distributed systems. \emph{Fantom} is based on the Lachesis Protocol~\cite{lachesis01}, which uses asynchronous event transmission for practical Byzantine fault tolerance (pBFT) to create a leaderless, scalable, asynchronous Directed Acyclic Graph (DAG). We further optimize the \emph{Lachesis Protocol} by introducing a permission-less network for dynamic participation. Root selection cost is further optimized by the introduction of an n-row flag table, as well as optimizing path selection by introducing domination relationships. We propose an alternative framework for distributed ledgers, based on asynchronous partially ordered sets with logical time ordering instead of blockchains. This paper builds upon the original proposed family of \emph{Lachesis-class} consensus protocols. We formalize our proofs into a model that can be applied to abstract asynchronous distributed system.