Miracle-dz/UniFAR-FTUs huggingface dataset:
TWO-LINE TECHNIQUE FOR DIELECTRIC MATERIAL CHARACTERIZATION WITH APPLICATION IN 3D-PRINTING FILAMENT ELECTRICAL PARAMETERS EXTRACTION

The literature lacks detailed information about the electrical properties of the plastic filaments used in 3D printing. This opens the way for research on characterizing the types of materials used in these filaments. In this work, a method for the extraction of the dielectric constant and loss tangent of materials is described. This method, which is suitable for characterizing any dielectric material, is then used to characterize 3D-printed samples based on different filament materials and infill densities over a very wide frequency range [0.02–10GHz]. The selected materials are Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS) and a semi-flex filament that combines two important features of flexibility and endurance. These three types are the most commonly used in 3D printing. The two-line technique is applied to extract the complex permittivity of the material under test (MUT) from the propagation constant. This method employs the uncalibrated scattering parameters with different types of transmission line for any characteristic impedance. A rectangular coaxial transmission-line fixture has been used to validate the theoretical work through simulations and measurements involving the 3D filament samples.
Design of Reconfigurable dB-Linear Variable-Gain Amplifier and Switchable-Order $g_{m}$ -C Filter in 65-nm CMOS Technology

A system approach for a power-scalable analog baseband (ABB) design is presented in this article. Using this approach, the energy efficiency of an ABB can be maximized without compromising any other important specifications. To fulfill the feasibility study, a switchable-order $g_{m}$ -C lowpass filter (LPF) along with a voltage-controlled programmable-gain amplifier (VC-PGA) is designed. The selectivity of the LPF can be linearly scaled with power consumption. In addition, the power consumption of VC-PGA has a binary-weighted manner. In contrast to conventional PGAs, the gain step of the designed PGA can be continuously tuned by a control voltage. To prove the concept, the ABB is implemented in 65-nm CMOS technology. The measurements show that the frequency responses of the ABB can be configured as either fifth or seventh order with 16 gain steps. The bandwidth is approximately 50 MHz for all cases, and the gain step can be continuously tuned between 0 and 3 dB. At the high-gain mode, the output third-order intercept point and the input-referred noise of the LPF and PGA are approximate to be 8 dBm and 5 nV/sqrt Hz, respectively. The maximum power consumption of the ABB, excluding the output buffer, is approximately 19.8 mW with a 1.2-V supply voltage. The die area, excluding the pads, is only 0.18 mm2.
Reinforcement learning Lecture 4 : Approximate dynamic programming

Deep Q Networks discussed in the last lecture are an instance of approximate dynamic programming. These are iterative algorithms that try to find fixed point of Bellman equations, while approximating the value-function/Qfunction a parametric function for scalability when the state space is large. Similar to Q-learning, function approximation versions of TD-learning and value-iteration can be obtained. Here, we will examine some versions of these algorithms in terms of their convergence properties.
APPENDIX 1-PROOF OF STREAMING VARIATIONAL

= ∫ +∞ −∞ qθ (z) ln p(cb|z)dz− ∫ +∞ −∞ qθ (z) ln qθ (z) qθb−1(z) dz = E[ln p(cb|z)]−DKL[qθ (z)||qθb−1(z)], Streaming Variational Objective with Proposed Bayesian Updating Let us first rewrite the proposed Bayesian updating with the scaling function Sb to scale the likelihood of batch cb instead of simply using the number of batches. Since Sb ∈R+, we substitute the product of likelihoods term with a likelihood raised to the power of Sb. p(z|< c1 . . .cb >)' p(cb|z)b p(z)∗/p(< c1 . . .cb >) (2) Analogous to the previous proof, consider the KL-divergence between an appropriate family of distribution qθ (.) and posterior p(< z|cb . . .c1 >) estimated using Bayesian updating.
An Initial Examination of Self-compassion as Reported by Patients and Practitioners by Kendra

Approved:______________________________________________ Vicki Ebbeck This study was an initial exploration of the role self-compassion plays in adherence to physical therapy programs. Self-compassion refers to treating oneself with the same kindness and understanding that is usually shown towards loved ones when they are facing difficult life experiences, and has been associated with factors that could facilitate adherence such as self-motivation and coping skills. Fifty-six physical therapy patients (26 male, 30 female) and five physical therapists (4 male, 1 female) participated in the investigation. A practitioner’s observation of self-compassion for each patient was positively related to rehabilitation adherence. Additionally, patients’ stages of rehabilitation moderated this relationship. That is, the correlation was significant if patients had completed more than 70% of their rehabilitation, but no significant correlation was observed for patients who had completed less than 70% of their rehabilitation. This study suggests that practitioner observed self-­‐compassion might play a role in physical therapy compliance.
Data interoperability across IoT domains

— Nowadays the proliferation of IoT (Internet of Things) devices results in heterogeneous and proprietary sensor data formats which makes challenging the processing and interpretation of sensor data across IoT domains. Thus, to achieve syntactic interoperability (the ability to exchange uniformly structured data) and semantic interoperability (the ability to interpret the meaning of data unambiguously) is still an issue under research. In this paper, we introduce and discuss our purpose developed new script language called Language for Sensor Data Description (L4SDD), and the basic principles of our generic, ontology-based approach to achieve cross-domain syntactic and semantic interoperability. Moreover, we illustrate our solutions via a real-life smart parking case study.
Consensus-based statements for the management of mitochondrial stroke-like episodes

Background: Focal-onset seizures and encephalopathy are prominent features of a stroke-like episode, which is a severe neurological manifestation associated with subtypes of mitochondrial disease. Despite more than 30 years of research, the acute treatment of stroke-like episodes remains controversial. Methods: We used the modified Delphi process to harness the clinical expertise of a group of mitochondrial disease specialists from five European countries to produce consensus guidance for the acute management of stroke-like episodes and commonly associated complications. Results: Consensus on a new definition of mitochondrial stroke-like episodes was achieved and enabled the group to develop diagnostic criteria based on clinical features, neuroimaging and/or electroencephalogram findings. Guidelines for the management of strokelike episodes were agreed with aggressive seizure management strongly recommended at the outset of stroke-like episodes. Conclusions: Our consensus statement defines stroke-like episodes in terms of an epileptic encephalopathy and we have used this to revise both diagnostic criteria and guidelines for management. A prospective, multi-centre, randomised controlled trial is required for evaluating the efficacy of any compound on modifying the trajectory of stroke-like episodes.
Authentication , Authorisation & Accountability ( AAA ) Knowledge Area Issue 1 . 0

Access control builds on authorisation and authentication. This KA will present the general foundations of access control and some significant instantiations that have emerged as IT kept spreading into new application areas. It will survey modes of user authentication and the way they are currently deployed, authentication protocols for the web, noting how new use cases have led to a shift from authentication to authorisation protocols, and the formalisation of authentication properties as used in today’s protocol analysis tools. On accountability, the focus is on the management and protection of audit logs. The surveillance of logs to detect attacks or inappropriate behaviour is described in the Security Operations & Incident Management CyBOK Knowledge Area [1] while the examination of evidence following a breach of policy or attack is covered in the Forensics CyBOK Knowledge Area [2]. Throughout the KA, we will flag technical terms that appear in more than one meaning in the academic and the trade literature.
Management through the Lenses of Ancient People Ezgi

“Management” as a science, emerged late in the 19 th century, gained respect and peaked in academic world to the end of 20 th century. Now, it is a fundamental school of concept for all kind of works no matter of its orientation either as business or engineering. In this paper, it is aimed to emphasize popular managerial concepts and buzzwords with ancient people sayings from Egypt, Babylonia, China, and Greece. Accordingly, organization and departmentalization, and human resources management in Ancient Egypt, Hammurabi’s codes and its reflections to bureaucracy in Ancient Babylonia, organizational strategy and ethics in Ancient China, and division of labor, human resources management and leadership in Ancient Greece are explored.
Eliminating turbulent self-interaction through the parallel boundary condition in local gyrokinetic simulations

In this work, we highlight an issue that may reduce the accuracy of many local nonlinear gyrokinetic simulations – turbulent self-interaction through the parallel boundary condition. Given a sufficiently long parallel correlation length, individual turbulent eddies can span the full domain and ‘bite their own tails’, thereby altering their statistical properties. Such self-interaction is only modelled accurately when the simulation domain corresponds to a full flux surface, otherwise it is artificially strong. For Cyclone Base Case parameters and typical domain sizes, we find that this mechanism modifies the heat flux by approximately 40 % and it can be even more important. The effect is largest when using kinetic electrons, low magnetic shear and strong turbulence drive (i.e. steep background gradients). It is found that parallel self-interaction can be eliminated by increasing the parallel length and/or the binormal width of the simulation domain until convergence is achieved.
PointPoseNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds

We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting objects, in some cases with an added refinement step using depth data. Our method consumes unordered point sets with/without RGB information, from initial detection to the final transformation estimation stage. This allows us to achieve accurate pose estimates, in some cases surpassing state of the art methods trained on the same data.
Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)

Quantization of weights of deep neural networks (DNN) has proven to be an effective solution for the purpose of implementing DNNs on edge devices such as mobiles, ASICs and FPGAs, because they have no sufficient resources to support computation involving millions of high precision weights and multiply-accumulate operations. This paper proposes a novel method to compress vectors of high precision weights of DNNs to ternary vectors, namely a cosine similarity based target non-retraining ternary (TNT) compression method. Our method leverages cosine similarity instead of Euclidean distances as commonly used in the literature and succeeds in reducing the size of the search space to find optimal ternary vectors from $3^{N}$ to $N$, where $N$ is the dimension of target vectors. As a result, the computational complexity for TNT to find theoretically optimal ternary vectors is only $O(N\log(N))$. Moreover, our experiments show that, when we ternarize models of DNN with high precision parameters, the obtained quantized models can exhibit sufficiently high accuracy so that re-training models is not necessary.
HOnnotate: A Method for 3D Annotation of Hand and Object Poses

We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our motivation is the current lack of annotated real images for this problem, as estimating the 3D poses is challenging, mostly because of the mutual occlusions between the hand and the object. To tackle this challenge, we capture sequences with one or several RGB-D cameras and jointly optimize the 3D hand and object poses over all the frames simultaneously. This method allows us to automatically annotate each frame with accurate estimates of the poses, despite large mutual occlusions. With this method, we created HO-3D, the first markerless dataset of color images with 3D annotations for both the hand and object. This dataset is currently made of 77,558 frames, 68 sequences, 10 persons, and 10 objects. Using our dataset, we develop a single RGB image-based method to predict the hand pose when interacting with objects under severe occlusions and show it generalizes to objects not seen in the dataset.
Early Detection of Research Trends

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this dissertation, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the 'ancestors' of the new topic. Based on this understanding, we developed Augur, a novel approach to effectively detect the emergence of new research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 timeframe and outperformed four alternative approaches in terms of both precision and recall.
Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection

Motivated by the transformative impact of deep neural networks (DNNs) on different areas (e.g., image and speech recognition), researchers and anti-virus vendors are proposing end-to-end DNNs for malware detection from raw bytes that do not require manual feature engineering. Given the security sensitivity of the task that these DNNs aim to solve, it is important to assess their susceptibility to evasion. In this work, we propose an attack that guides binary-diversification tools via optimization to mislead DNNs for malware detection while preserving the functionality of binaries. Unlike previous attacks on such DNNs, ours manipulates instructions that are a functional part of the binary, which makes it particularly challenging to defend against. We evaluated our attack against three DNNs in white-box and black-box settings, and found that it can often achieve success rates near 100%. Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%. We explored several defenses, both new and old, and identified some that can successfully prevent over 80% of our evasion attempts. However, these defenses may still be susceptible to evasion by adaptive attackers, and so we advocate for augmenting malware-detection systems with methods that do not rely on machine learning.
Recent advances in high-throughput superconductivity research

Applications are being found for superconducting materials in a rapidly growing number of technological areas, and the search for novel superconductors remains a major scientific task. However, the steady increase in the complexity of candidate materials presents a big challenge to researchers. In particular, conventional experimental methods are not well suited to an efficient search for candidates in a compositional space growing exponentially with the number of elements; neither do they permit a quick extraction of reliable multidimensional phase diagrams delineating the physical parameters that control superconductivity. New research paradigms that can boost the speed and the efficiency of research into superconducting materials are urgently needed. High-throughput methods for the rapid screening and optimization of materials have aided the acceleration of research in bioinformatics and the pharmaceutical industry, yet remain rare in quantum materials research. In this paper we briefly review the history of high-throughput research and then focus on some recent applications of this paradigm in superconductivity research. We consider the role these methods can play in all stages of materials development, including high-throughput computation, synthesis, characterization, and the emerging field of machine learning for materials. The high-throughput paradigm will undoubtedly become an indispensable tool in superconductivity research in the near future.
Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice

Static (offline) techniques for mapping applications given by task graphs to MPSoC systems often deliver overly pessimistic and thus suboptimal results w.r. t. exploiting time slack in order to minimize the energy consumption. This holds true in particular in case computation times of tasks may be workload-dependent and becoming known only at runtime or in case of conditionally executed tasks or scenarios. This paper studies and quantitatively evaluates different classes of algorithms for scheduling periodic applications given by task graphs (i.e., DAGs) with precedence constraints and a global deadline on homogeneous MPSoCs purely at runtime on a per-instance base. We present and analyze algorithms providing provably optimal results as well as approximation algorithms with proven guarantees on the achieved energy savings. For problem instances taken from realistic embedded system benchmarks as well as synthetic scalable problems, we provide results on the computation time and quality of each algorithm to perform a) scheduling and b) voltage/speed assignments for each task at runtime. In our portfolio, we distinguish as well continuous and discrete speed (e.g., DVFS-related) assignment problems. In summary, the presented ties between theory (algorithmic complexity and optimality) and execution time analysis deliver important insights on the practical usability of the presented algorithms for runtime optimization of task scheduling and speed assignment on MPSoCs.
Style Transfer with Time Series: Generating Synthetic Financial Data

Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ratio. High dimensionality can be dealt with using robust feature selection or dimensionality reduction, but limited observations often result in a model that overfits due to the large parameter space of most deep neural networks. We propose a generative model for financial time series, which allows us to train deep learning models on millions of simulated paths. We show that our generative model is able to create realistic paths that embed the underlying structure of the markets in a way stochastic processes cannot.
Approximation by juntas in the symmetric group, and forbidden intersection problems

A family of permutations $\mathcal{F} \subset S_{n}$ is said to be $t$-intersecting if any two permutations in $\mathcal{F}$ agree on at least $t$ points. It is said to be $(t-1)$-intersection-free if no two permutations in $\mathcal{F}$ agree on exactly $t-1$ points. If $S,T \subset \{1,2,\ldots,n\}$ with $|S|=|T|$, and $\pi: S \to T$ is a bijection, the $\pi$-star in $S_n$ is the family of all permutations in $S_n$ that agree with $\pi$ on all of $S$. An $s$-star is a $\pi$-star such that $\pi$ is a bijection between sets of size $s$. Friedgut and Pilpel, and independently the first author, showed that if $\mathcal{F} \subset S_n$ is $t$-intersecting, and $n$ is sufficiently large depending on $t$, then $|\mathcal{F}| \leq (n-t)! $; this proved a conjecture of Deza and Frankl from 1977. Equality holds only if $\mathcal{F}$ is a $t$-star. In this paper, we give a more `robust' proof of a strengthening of the Deza-Frankl conjecture, namely that if $n$ is sufficiently large depending on $t$, and $\mathcal{F} \subset S_n$ is $(t-1)$-intersection-free, then $|\mathcal{F} \leq (n-t)! $, with equality only if $\mathcal{F}$ is a $t$-star. The main ingredient of our proof is a `junta approximation' result, namely, that any $(t-1)$-intersection-free family of permutations is essentially contained in a $t$-intersecting {\em junta} (a `junta' being a union of a bounded number of $O(1)$-stars). The proof of our junta approximation result relies, in turn, on a weak regularity lemma for families of permutations, a combinatorial argument that `bootstraps' a weak notion of pseudorandomness into a stronger one, and finally a spectral argument for pairs of highly-pseudorandom fractional families. Our proof employs four different notions of pseudorandomness, three being combinatorial in nature, and one being algebraic.
Conjunctive Regular Path Queries with String Variables

We introduce the class CXRPQ of conjunctive xregex path queries, which are obtained from conjunctive regular path queries (CRPQs) by adding string variables (also called backreferences) as found in practical implementations of regular expressions. CXRPQs can be considered user-friendly, since they combine two concepts that are well-established in practice: pattern-based graph queries and regular expressions with backreferences. Due to the string variables, CXRPQs can express inter-path dependencies, which are not expressible by CRPQs. The evaluation complexity of CXRPQs, if not further restricted, is PSpace-hard in data complexity. We identify three natural fragments with more acceptable evaluation complexity: their data complexity is in NL, while their combined complexity varies between ExpSpace, PSpace and NP. In terms of expressive power, we compare the CXRPQ-fragments with CRPQs and unions of CRPQs, and with extended conjunctive regular path queries (ECRPQs) and unions of ECRPQs.