Open-source lab hardware: Driver and temperature controller for high compliance voltage, fiber-coupled butterfly lasers

This article describes the development of a compact, relatively low-cost, high compliance voltage laser driver that can provide the constant optical laser output required for a range of applications. The system contains an integrated, high-precision temperature controller that can be implemented with butterfly-style lasers containing an internal thermoelectric cooler. The laser parameters can be controlled manually or via an onboard microcontroller. Additionally, an adjustable over-current protection circuit safeguards the laser diode from potential damage

Electrical and galvanomagnetic properties of AuAl2+6%Cu intermetallic compounds at low temperatures

The AuAl2 intermetallic compounds are of substantial interest in view of their application potential. The investigated intermetallics AuAl 2+6%Cu were prepared from fine powders of AuAl2 and Cu by vacuum sputtering on a glass substrate and consisted of films with a thickness of about one micrometer. The films were annealed. The temperature and field dependence of the electroresistivity, the magnetoresistivity and the Hall effect of AuAl2+6%Cu films were measured in the temperature interval from 4.2 to 100 K and at magnetic fields of up to 15 T. We demonstrate that the temperature dependence of the electroresistivity has a minimum at T = 20 K and a metallic behavior above this temperature. The magnetoresistivity is very small (less then 1%), positive at low temperatures and negative above 12 K. The Hall coefficient is positive, which corresponds to the holes in a one zone model with a charge carrier concentration of about 1.6 1020 cm-3. © Published under licence by IOP Publishing Ltd

QuerySnout: automating the discovery of attribute inference attacks against query-based systems

Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to the dataset is a hard problem. Theoretical solutions relying on differential privacy guarantees are difficult to implement correctly with reasonable accuracy, while ad-hoc solutions might contain unknown vulnerabilities. Evaluating the privacy provided by QBSes must thus be done by evaluating the accuracy of a wide range of privacy attacks. However, existing attacks against QBSes require time and expertise to develop, need to be manually tailored to the specific systems attacked, and are limited in scope. In this paper, we develop QuerySnout, the first method to automatically discover vulnerabilities in query-based systems. QuerySnout takes as input a target record and the QBS as a black box, analyzes its behavior on one or more datasets, and outputs a multiset of queries together with a rule to combine answers to them in order to reveal the sensitive attribute of the target record. QuerySnout uses evolutionary search techniques based on a novel mutation operator to find a multiset of queries susceptible to lead to an attack, and a machine learning classifier to infer the sensitive attribute from answers to the queries selected. We showcase the versatility of QuerySnout by applying it to two attack scenarios (assuming access to either the private dataset or to a different dataset from the same distribution), three real-world datasets, and a variety of protection mechanisms. We show the attacks found by QuerySnout to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature. We finally show how QuerySnout can be extended to QBSes that require a budget, and apply QuerySnout to a simple QBS based on the Laplace mechanism. Taken together, our results show how powerful and accurate attacks against QBSes can already be found by an automated system, allowing for highly complex QBSes to be automatically tested "at the pressing of a button". We believe this line of research to be crucial to improve the robustness of systems providing privacy-preserving access to personal data in theory and in practice

Open-source lab hardware: A versatile microfluidic control and sensor platform

Here we describe a completely integrated and customizable microfluidic control and sensing architecture that can be readily implemented for laboratory or portable chemical or biological control and sensing applications. The compact platform enables control of 32 solenoid valves, a multitude of pumps and motors, a thermo-electric controller, a pressure controller, and a high voltage power supply. It also features two temperature probe interfaces, one relative humidity and ambient temperature sensor, two pressure sensors, and interfaces to an electrical conductivity sensor, flow sensor, and a bubble detector. The platform can be controlled via an onboard microcontroller and requires no proprietary software. Keywords: Capillary electrophoresis; Chemical analysis; Fluidic sensing; Lab automation; Microfluidic sample handling; Valve controller

Quotient probabilistic normed spaces and completeness results

Quotient spaces of probabilistic normed spaces have never been considered. This note is a first attempt to fill this gap: the quotient space of a PN space with respect to one of its subspaces is introduced and its properties are studied. Finally, we investigate the completeness relationship among the PN spaces considered

Reliable Multimodal Heartbeat Classification using Deep Neural Networks

Copyright © 2023 Authors. Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). Heartbeat detection has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate heartbeat classification. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for heartbeat classification, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. Moreover, while many researchers have successfully created methodologies to accurately classify heartbeats including paced beats, none were able to distinguish various sub-classes of paced heartbeats. A more comprehensive distinction is crucial as it not only aids in the identification of pacing settings but also facilitates the detection of inadequate pacing settings, a critical aspect in patient care. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification and for comprehensive paced heartbeats classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on 5 different arrhythmia classes, whereas ResNet34 achieved an accuracy of 93.82% on 12 paced classes. The significant efficiency of utilizing ABP and CVP signals independently for classification, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. For classifying 12 different paced heartbeats, ResNet34 achieved 74.04% accuracy with ABP signals and 74.38% with CVP signals. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.British Heart Foundation for sponsoring this project (No.FS/19/73/34690

Interaction data are identifiable even across long periods of time

Fine-grained records of people’s interactions, both offline and online, are collected at large scale. These data contain sensitive information about whom we meet, talk to, and when. We demonstrate here how people’s interaction behavior is stable over long periods of time and can be used to identify individuals in anonymous datasets. Our attack learns the profile of an individual using geometric deep learning and triplet loss optimization. In a mobile phone metadata dataset of more than 40k people, it correctly identifies 52% of individuals based on their 2-hop interaction graph. We further show that the profiles learned by our method are stable over time and that 24% of people are still identifiable after 20 weeks. Our results suggest that people with well-balanced interaction graphs are more identifiable. Applying our attack to Bluetooth close-proximity networks, we show that even 1-hop interaction graphs are enough to identify people more than 26% of the time. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union’s General Data Protection Regulation