How heavy can dark matter be? Constraining colourful unitarity with SARAH

We describe the automation of the calculation of perturbative unitarity constraints including scalars that have colour charges, and its release in SARAH 4.14.4. We apply this, along with vacuum stability constraints, to a simple dark matter model with colourful mediators and interesting decays, and show how it leads to a bound on a thermal relic dark matter mass well below the classic Griest-Kamionkowski limit.Comment: 21 pages, 4 figure

A Highly-Sensitive Global-Shutter CMOS Image Sensor with On-Chip Memory For Hundreds of Kilo-Frames Per Second Scientific Experiments

In this work, a highly-sensitive global-shutter CMOS image sensor with on-chip memory that can capture up to 16 frames at speeds higher than 200kfps is presented. The sensor fabricated and tested is a 100 x 100 pixel sensor, and was designed to be expandable to a 1000 x 1000 pixel sensor using the same building blocks and similar architecture. The heart of the sensor is the pixel. The pixel consists of 11 transistors (11T) and 2 MOSFET capacitors. A 6T front-end is followed by a Correlated Double Sampling (CDS) circuitry that includes 2 capacitors and a reset switch. The 4T back-end circuitry consists of a source follower, in-pixel current source and 2 switches. The pixel design is unique because of the following. In a relatively small area, 15.1um x 15.1um, it performs CDS that limits the noise stored in the pixel memories to less than 0.33mV rms and allows the stored value to be read in a single readout. Moreover, it has in-pixel current source, which can be turned OFF when not in use, to remove the dependency of its output voltage to its location in the sensor. Furthermore, the in-pixel capacitors are MOSFET capacitors and do not utilize any space in the upper metal layers, therefore, they can be used exclusively for routing. And at the same time it has a fill factor greater than 40%, which important for high sensitivity. Each pixel is connected to a dedicated memory, which is outside the pixel array and consists of 16 MOSFET capacitors and their access switches (1T1C design). Fifty pixels share a line for their connection to their dedicated memory blocks, and, therefore, the transfer of all the stored pixel values to the on-chip memories happens within 50 clock cycles. This allows capturing consecutive frames at speeds higher than 200 kfps. The total rms noise stored in the memories is 0.4 mV

Controlled anisotropic dynamics of tightly bound skyrmions in a synthetic ferrimagnet due to skyrmion-deformation mediated by induced uniaxial in-plane anisotropy

We study speed and skew deflection-angle dependence on skyrmion deformations of a tightly bound two-skyrmion state in a synthetic ferrimagnet. We condsider here, an in-plane uniaxial magnetocrystalline anisotropy-term in order to induce lateral shape distortions and an overall size modulation of the skyrmions due to a reduction of the effective out-of-plane anisotropy, thus affecting the skyrmion speed, skew-deflection and inducing anisotropy in these quantities with respect to the driving current-angle. Because of frustrated dipolar interactions in a synthetic ferrimagnet, sizeable skyrmion deformations can be induced with relatively small induced anisotropy constants and thus a wide range of tuneability can be achieved. We also show analytically, that a consequence of the skyrmion deformation can, under certain conditions cause a skyrmion deflection with respect to driving-current angles, unrelated to the topological charge. Results are analyzed by a combination of micromagnetic simulations and a compound particle description within the Thiele-formalism from which an over-all mobility tensor is constructed. This work offers an additional path towards in-situ tuning of skyrmion dynamics

Membership Inference Attacks: Threat Analysis

Στην σύγχρονη εποχή, οι εταιρίες και οι οργανισμοί σε όλο τον κόσμο, χρησιμοποιούν υπηρεσίες μοντέλων μηχανικής μάθησης, ως ένα εργαλείο για την βελτίωση της ζωής των πελατών τους. Αλγόριθμοι συστάσεων ταινιών, παιχνιδιών και τάσεων, μηχανές αναζήτη- σης, αλλά και ενδοεταιρικές υπηρεσίες σε φαρμακευτικούς, στρατιωτικούς οργανισμούς, λειτουργούν βασιζόμενοι σε μια συνεχή ροή δεδομένων, η οποία συνεχώς αυξάνεται σε όγκο και πλήθος. Οι χρήστες αγνοώντας το πως οι πάροχοι χρησιμοποιούν τα δεδομένα τους , συννενούν να παραδώσουν το δικαίωμα της ιδιωτικότητας των δεδομένων τους, βασιζόμενοι στον εκαστοτε οργανισμό για την διατήρηση της ανωνυμίας και της ιδιοτικότητας των ευαίσθη- των πληροφοριών τους. Απο την άλλη πλευρά, οι πάροχοι κατευνάζουν τις όποιες ανη- συχίες των χρηστών υποσχόμενοι πως χρησιμοποιούν τις τελευταίες τεχνολογίες ιδιοτι- κοποίησης δεδομένων, αγνοόντας το γεγονός ό,τι τα μοντέλα μηχανικής μάθησης, που τοσο πασχίζουν να βγάλουν στην παραγωγή, διαθέτουν ευπάθιες, οι οποίες διακιβεύουν τα ευαίσθητα, προσωπικά δεδομένα των χρηστών τους. Επιθέσεις, τετοιου τύπου ονομάζονται Population Inference attacks και συγκεκριμένα εμείς θα ασχοληθούμε με τα Membership Inference Attacks. Σε αυτές τις επιθέσεις, το μοντέλο θύμα, εκπαιδεύεται πάνω σε ένα κρυφό συνολο δεδομέ- νων, που αποτελεί τον στόχο. Ο κακόβουλος χρήστης από τη δικιά του πλευρά, προσπαθεί να συμπαιράνει αν κάποιοι χρήστες-στόχοι ανήκουν μέσα στο παραπάνω σύνολο εκπαί- δευσης. Ως προς της επίδειξη του κινδύνου μια τέτοιας επίθεσης σκεφτήκε ενα σενάριο όπου, ο επιτιθέμενος γνωρίζει πως τα κλινικά δεδομένα ενός χρήστη, χρησιμοποιήθηκαν για την εκπάιδευση ενός μοντέλου προβλεψεων σχετικών με μία ασθένεια. Ο επιτιθέμενος, γνωρίζει πλέον αν το θύμα έχει την ασθένεια ή όχι, εφόσον το μοντέλο του έχει εκθέσει τις προσωπικές πληροφορίες του θύματος. Η εργασίας αυτή έχει ως στόχο ο αναγνώστης να εξετάσει, αναλύσει και κατανοήσει τον τρόπο λειτουργίας των Membership Inference Attacks, καθώς και τις επιπτώσεις τους και τις διάφορετικές άμυνες που μπορεί κανείς να λάβει για να αποφευχθεί η διαρροή δεδο- μένων κατά την εκπάιδευση μοντέλων μηχανικής μάθησης. Για την καλύτερη κατανόηση και ενίσχυση των επιχειρημάτων που ακολουθούν, παρέ- χουμε διαγράμματα και πίνακες ως οπτίκά βοηθήματα.In the new era of data, companies and organizations around the world offer Machine Learning Services as a tool for enhancing people’s lives. Recommendation algorithms, search engine’s, intra-orgranization usage in medicine, military etc. Αll of the above are working on top of continuous data streams which are getting larger and more rich on user data, day by day. Users, unaware how their data are being used, accept terms and conditions, giving away the right of data privacy, participating in various machine learning experiments with the promise of each vendor’s data anonymity process. The vendors are reassuring users that their data are safe and completely anonymized, ignoring the fact that the machine learning models, they so much strive to incorporate to their product flow, suffers from subtle vulnerabilities, which can be used to expose and identify users, along with their, otherwise, private data. These types of attacks are called Population Inference Attacks and we are, specifically, going to deepen our knowledge and analyze with detail the so-called Membership Infer- ence Attack. In these attacks, the target uses a machine learning model trained on a secret ’target’ dataset. On the other hand the attacker, tries to inference whether some user-victim is a member of this dataset. To display the danger posed by this attack consider the scenario where an attacker knows that the clinical records of a user-victim are part of a disease- related-model’s training set, then the attacker can infer if the person has the disease with high certainty, leading to a serious privacy breach. The goal of this thesis is to further examine, analyze and understand the mechanism, reasoning behind membership inference attacks against machine learning models, as well as the effect and the various ways we could prevent data leakage during training of ML models. Throughout this thesis, a plethora of plots and boards will be provided to the reader, to enhance his/her understanding of this study via experiments

Skyrmion Logic System for Large-Scale Reversible Computation

Computational reversibility is necessary for quantum computation and inspires the development of computing systems in which information carriers are conserved as they flow through a circuit. While conservative logic provides an exciting vision for reversible computing with no energy dissipation, the large dimensions of information carriers in previous realizations detract from the system efficiency, and nanoscale conservative logic remains elusive. We therefore propose a non-volatile reversible computing system in which the information carriers are magnetic skyrmions, topologically-stable magnetic whirls. These nanoscale quasiparticles interact with one another via the spin-Hall and skyrmion-Hall effects as they propagate through ferromagnetic nanowires structured to form cascaded conservative logic gates. These logic gates can be directly cascaded in large-scale systems that perform complex logic functions, with signal integrity provided by clocked synchronization structures. The feasibility of the proposed system is demonstrated through micromagnetic simulations of Boolean logic gates, a Fredkin gate, and a cascaded full adder. As skyrmions can be transported in a pipelined and non-volatile manner at room temperature without the motion of any physical particles, this skyrmion logic system has the potential to deliver scalable high-speed low-power reversible Boolean and quantum computing.Comment: 24 pages, 7 figures, 3 table

O new physics, where art thou? A global search in the top sector

We provide a comprehensive global analysis of Run II top measurements at the LHC in terms of dimension-6 operators. A distinctive feature of the top sector as compared to the Higgs-electroweak sector is the large number of four-quark operators. We discuss in detail how they can be tested and how quadratic terms lead to a stable limit on each individual Wilson coecient. Predictions for all observables are computed at NLO in QCD. Our SFitter analysis framework features a detailed error treatment, including correlations between uncertainties