Miracle-dz/UniFAR-FTUs huggingface dataset:
A Regression-Based Entropy Distiller for RO PUFs

Silicon physical unclonable functions (PUF) utilize the variation during silicon fabrication process to extract information that will be unique for each chip. There have been many recent approaches to how PUF can be used to improve security related applications. However, it is well-known that the fabrication variation has very strong spatial correlation and this has been pointed out as a security threat to silicon PUF. In fact, when we apply NIST’s statistical test suite for randomness [1] against the random sequences generated from a population of 125 ring oscillator (RO) PUFs [2] using classic 1-out-of-8 Coding [3], [4] and Neighbor Coding [5], none of them can pass all the test. In the paper, we propose to decouple the unwanted systematic variation from the desired random variation through a regression-based distiller, where the basic idea is to build a model for the systematic variation so we can generate the random sequences only from the true random variation. Applying Neighbor Coding to the same benchmark data [2], our experiment shows that 2 and 3 order polynomials distill random sequences that pass all the NIST randomness test, so does 4 order polynomial in the case of 1-out-of-8 Coding, which demonstrates that our method can bolster the security characteristics of existing PUF schemes.
Blind Video Watermarking Scheme for Mpeg-4 Videos with Parity Sequences in Transform Domain

High quality video broad casting is of high demand both with DVB-2 (Digital Video Broad casting) and Internet services. But these broadcasted data is distributed without protection. Invisible mode of video watermarking is one of the solutions, here in this paper a novel approach of data embedding scheme is proposed for MPEG-4 videos with different parity check codes and processed in transform domain. A subjective and objective analysis is performed to examine the proposed approach. Experimental results on various videos have shown that LDPC (Low density parity check) code with Gold spreading sequence in transform domain outperforms when compared against the other methods.
From local properties to brain‐wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data

Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level‐dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain‐wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain‐wide manner.
Biologically‐coupled bisphosphonate chaperones effectively deliver molecules to the site of soft tissue‐bone healing

Tendon injuries are common and often treated surgically, however, current tendon repair healing results in poorly organized fibrotic tissue. While certain growth factors have been reported to improve both the strength and organization of the repaired enthesis, their clinical applicability is severely limited due to a lack of appropriate delivery strategies. In this study, we evaluated a recently developed fluorescent probe, Osteoadsorptive Fluorogenic Sentinel‐3 that is composed of a bone‐targeting bisphosphonate (BP) moiety linked to fluorochrome and quencher molecules joined via a cathepsin K‐sensitive peptide sequence. Using a murine Achilles tendon‐to‐bone repair model, BP‐based and/or Ctsk‐coupled imaging probes were applied either locally or systemically. Fluorescence imaging was used to quantify the resultant signal in vivo. After tendon‐bone repair, animals that received either local or systemic administration of imaging probes demonstrated significantly higher fluorescence signal at the repair site compared to the sham surgery group at all time points (p < 0.001), with signal peaking at 7–10 days after surgery. Our findings demonstrate the feasibility of using a novel BP‐based targeting and Ctsk‐activated delivery of molecules to the site of tendon‐to‐bone repair and creates a foundation for further development of this platform as an effective strategy to deliver bioactive molecules to sites of musculoskeletal injury.
Performance Analysis of PSO and GA Algorithms in Order to Classifying EEG Data

In this Research, a new method has been proposed in order to classify the mental tasks which represent the Electroencephalogram (EEG) signal as time series. Time series are kind of data format which depict signal voltage varieties in time domain. Different parts of the different signals have different powers, so in first step and in the preprocessing, signal partitioning into several fixed windows is needed. Toward the extracting appropriate features from each EEG signal window, PCA algorithm is used. So for each window, a feature vector is made by PCA, and a general vector is created from these primary vectors. In order to refuse redundancy caused by non-important windows, the best combination of such vectors, that have the best results in classification, should be probed. Toward this goal, two feature extraction methods, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are applied. K-Nearest Neighbor (KNN) was used as fitness function for PSO and GA. These methods select such windows whose combination of feature vectors are best and increase TP (true positive) of the classifier. The results show that GA and PSO improve the power of classification, but GA is more efficient. © 2015 Elixir All rights reserved. ARTICLE INFO Article history: Received: 24 November 2014; Received in revised form: 19 April 2015; Accepted: 30 April 2015;
A diagnostic for determining the quality of single‐reference electron correlation methods

It was recently proposed that the Euclidian norm of the t(sub 1) vector of the coupled cluster wave function (normalized by the number of electrons included in the correlation procedure) could be used to determine whether a single-reference-based electron correlation procedure is appopriate. This diagnostic, T(sub 1) is defined for use with self-consistent-field molecular orbitals and is invariant to the same orbital rotations as the coupled cluster energy. T(sub 1) is investigated for several different chemical systems which exhibit a range of multireference behavior, and is shown to be an excellent measure of the importance of non-dynamical electron correlation and is far superior to C(sub 0) from a singles and doubles configuration interaction wave function. It is further suggested that when the aim is to recover a large fraction of the dynamical electron correlation energy, a large T(sub 1) (i.e., greater than 0.02) probably indicates the need for a multireference electron correlation procedure.
Higher systolic blood pressure in early‐mid adulthood is associated with poorer cognitive performance in those with a dominantly inherited Alzheimer's disease mutation but not in non‐carriers. Results from the DIAN study

The Dominantly Inherited Alzheimer Network (DIAN) is a longitudinal observational study that collects data on cognition, blood pressure (BP), and other variables from autosomal‐dominant Alzheimer's disease mutation carriers (MCs) and non‐carrier (NC) family members in early to mid‐adulthood, providing a unique opportunity to evaluate BP and cognition relationships in these populations.
Identification of starvation-mimetic bioactive phytocomponent from Withania somnifera using in-silico molecular modelling and flow cytometry-based analysis for the management of malaria

Abstract Multidrug resistance episodes in malaria increased from 3.9% to 20% from 2015 to 2019. Synchronizing the clinical manifestation in chronological sequence led to a unique impression on glucose demand (increased up to 100-fold) by the parasite-infected RBCs. Hence, restriction in the glucose uptake to parasite-infected RBCs could be an alternative approach to conquer the global burden of malaria to a greater extent. A C28 steroidal lactone Withaferin A (WS-3) isolated from Withania somnifera leave extract shows better thermodynamically stable interactions with the glucose transporters (GLUT-1 and PfHT) to standard drugs metformin and lopinavir. MD simulations for a trajectory period of 100 ns reflect stable interactions with the interactive amino acid residues such as Pro141, Gln161, Gln282, Gln283, Trp388, Phe389, and Phe40, Asn48, Phe85, His168, Gln169, Asn311 which potentiating inhibitory activity of WS-3 against GLUT-1 and PfHT respectively. WS-3 was non-hemotoxic (%hemolysis <5%) for a high concentration of up to 1 mg/ml in the physiological milieu. However, the %hemolysis significantly increased up to 30.55 ± 0.929% in a parasitophorous simulated environment (pH 5.0). Increased hemolysis of WS-3 could be due to the production of ROS in an acidic environment. Further, the inhibitory activity of WS-3 against both glucose transporters was supported with flow cytometry-based analysis of parasite-infected RBCs. Results show that WS-3 has low mean fluorescence intensities for both target proteins compared to conventional drugs, suggesting a potential sugar transporter inhibitor against GLUT-1 and PfHT for managing malaria. Communicated by Ramaswamy H. Sarma Highlights According to clinical studies, malaria-affected RBCs have a greater glucose need (up to 100 times higher). WS-3 isolated from Withania somnifera leaf extract has more thermodynamically stable interactions with the glucose transporters (GLUT-1 and PfHT) compared to standard drugs metformin and lopinavir. WS-3 was found to be non-hemotoxic (%hemolysis <5%) for a high concentration of up to 1 mg/ml in the physiological milieu. WS-3 exhibits substantial lysis of infected RBC in a parasitophorous simulated environment (pH 5.0) due to the formation of ROS in an acidic environment. WS-3 has low mean fluorescence intensities for both target proteins compared to conventional drugs, suggesting a potential sugar transporter inhibitor against GLUT-1 and PfHT for managing malaria.
Stochastic models of atom -photon dynamics with applications to cooling quantum gases

Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Through the years, stochastic physics has provided important insight into natural phenomena that possess an inherently random nature. From its foundations in the study of Brownian motion up through its myriad present applications, a stochastic description of nature has yielded an elegant theoretical understanding, as well as providing practical and efficient simulation techniques. One particularly important application is to the field of quantum optics, in which the interaction of light and matter is treated in a fundamentally quantum-mechanical manner. The work presented here utilizes the methods of stochastic physics to understand a variety of quantum-optical phenomena involving the dynamics of atoms interacting with photons. A solid theoretical understanding of such phenomena is often necessary to describe laser cooling of atoms, and many such applications are discussed here. A detailed model of atoms with complex internal structure interacting with three-dimensional laser fields is presented, as well as the rich dynamics of three-level atoms interacting with two lasers. Applications to cavity cooling of atoms and molecules are discussed, and a method for describing non-Markovian dynamics using relaxation techniques is presented. A novel cooling scheme utilizing Feshbach resonances in the scattering of two atoms is also treated. Acknowledgements I would like to thank Chris Greene for his many years of guidance and support during my graduate career. His passion for physics is contagious and it inspires those who interact with him strive for a deeper understanding of nature. His curiosity extends over a very broad range, and he is often willing to tackle projects outside of his main expertise in order to expand his understanding of physics. I appreciate his willingness to allow me to pursue a study of quantum optics and stochastic physics even given his limited experience in these fields. I believe that our collaboration has been very fruitful. Combining my day-today learning about quantum optics with Chris's comprehensive understanding of traditional atomic physics has allowed us to develop models of atom-photon dynamics with unprecedented attention to an accurate treatment of atomic internal degrees of freedom. Our collaboration has often led to the realization of parallels between quantum-optical concepts and the many theories developed by Fano, which Chris knows so well. For me, this conceptual bridge …
Predicting the Change Impact of Resolving Defects by Leveraging the Topics of Issue Reports in Open Source Software Systems

Upon receiving a new issue report, practitioners start by investigating the defect type, the potential fixing effort needed to resolve the defect and the change impact. Moreover, issue reports contain valuable information, such as, the title, description and severity, and researchers leverage the topics of issue reports as a collective metric portraying similar characteristics of a defect. Nonetheless, none of the existing studies leverage the defect topic, i.e., a semantic cluster of defects of the same nature, such as Performance, GUI, and Database, to estimate the change impact that represents the amount of change needed in terms of code churn and the number of files changed. To this end, in this article, we conduct an empirical study on 298,548 issue reports belonging to three large-scale open-source systems, i.e., Mozilla, Apache, and Eclipse, to estimate the change impact in terms of code churn or the number of files changed while leveraging the topics of issue reports. First, we adopt the Embedded Topic Model (ETM), a state-of-the-art topic modelling algorithm, to identify the topics. Second, we investigate the feasibility of predicting the change impact using the identified topics and other information extracted from the issue reports by building eight prediction models that classify issue reports requiring small or large change impact along two dimensions, i.e., the code churn size and the number of files changed. Our results suggest that XGBoost is the best-performing algorithm for predicting the change impact, with an AUC of 0.84, 0.76, and 0.73 for the code churn and 0.82, 0.71, and 0.73 for the number of files changed metric for Mozilla, Apache, and Eclipse, respectively. Our results also demonstrate that the topics of issue reports improve the recall of the prediction model by up to 45%.
Faster Supervised Average Consensus in Adversarial and Stochastic Anonymous Dynamic Networks

How do we reach consensus on an average value in a dynamic crowd without revealing identity? In this work, we study the problem of average network consensus in Anonymous Dynamic Networks (ADN). Network dynamicity is specified by the sequence of topology-graph isoperimetric numbers occurring over time, which we call the isoperimetric dynamicity of the network. The consensus variable is the average of values initially held by nodes, which is customary in the network-consensus literature. Given that having an algorithm to compute the average one can compute the network size (i.e., the counting problem) and vice versa, we focus on the latter. We present a deterministic distributed average network consensus algorithm for ADNs that we call isoperimetric Scalable Coordinated Anonymous Local Aggregation, and we analyze its performance for different scenarios, including worst-case (adversarial) and stochastic dynamic topologies. Our solution utilizes supervisor nodes, which have been shown to be necessary for computations in ADNs. The algorithm uses the isoperimetric dynamicity of the network as an input, meaning that only the isoperimetric number parameters (or their lower bound) must be given, but topologies may occur arbitrarily or stochastically as long as they comply with those parameters. Previous work for adversarial ADNs overestimates the running time to deal with worst-case scenarios. For ADNs with given isoperimetric dynamicity, our analysis shows improved performance for some practical dynamic topologies, with cubic time or better for stochastic ADNs, and our experimental evaluation indicates that our theoretical bounds could not be substantially improved for some models of dynamic networks.
Open data as an anticorruption tool? Using distributed cognition to understand breakdowns in the creation of transparency data

Abstract One of the drivers for pushing for open data as a form of corruption control stems from the belief that in making government operations more transparent, it would be possible to hold public officials accountable for how public resources are spent. These large datasets would then be open to the public for scrutiny and analysis, resulting in lower levels of corruption. Though data quality has been largely studied and many advancements have been made, it has not been extensively applied to open data, with some aspects of data quality receiving more attention than others. One key aspect however—accuracy—seems to have been overlooked. This gap resulted in our inquiry: how is accurate open data produced and how might breakdowns in this process introduce opportunities for corruption? We study a government agency situated within the Brazilian Federal Government in order to understand in what ways is accuracy compromised. Adopting a distributed cognition (DCog) theoretical framework, we found that the production of open data is not a neutral activity, instead it is a distributed process performed by individuals and artifacts. This distributed cognitive process creates opportunities for data to be concealed and misrepresented. Two models mapping data production were generated, the combination of which provided an insight into how cognitive processes are distributed, how data flow, are transformed, stored, and processed, and what instances provide opportunities for data inaccuracies and misrepresentations to occur. The results obtained have the potential to aid policymakers in improving data accuracy.
Book Review: Geometric Mechanics, Part II: Rotating, Translating and Rolling

See also GEOMETRIC MECHANICS — Part I: Dynamics and Symmetry (2nd Edition) This textbook introduces modern geometric mechanics to advanced undergraduates and beginning graduate students in mathematics, physics and engineering. In particular, it explains the dynamics of rotating, spinning and rolling rigid bodies from a geometric viewpoint by formulating their solutions as coadjoint motions generated by Lie groups. The only prerequisites are linear algebra, multivariable calculus and some familiarity with Euler-Lagrange variational principles and canonical Poisson brackets in classical mechanics at the beginning undergraduate level.The book uses familiar concrete examples to explain variational calculus on tangent spaces of Lie groups. Through these examples, the student develops skills in performing computational manipulations, starting from vectors and matrices, working through the theory of quaternions to understand rotations, then transferring these skills to the computation of more abstract adjoint and coadjoint motions, Lie-Poisson Hamiltonian formulations, momentum maps and finally dynamics with nonholonomic constraints.The organisation of the first edition has been preserved in the second edition. However, the substance of the text has been rewritten throughout to improve the flow and to enrich the development of the material. Many worked examples of adjoint and coadjoint actions of Lie groups on smooth manifolds have also been added and the enhanced coursework examples have been expanded. The second edition is ideal for classroom use, student projects and self-study. /a
Autogenous and drying shrinkage of structural concretes incorporating recycled concrete aggregates from different sources

The main relevant difference between recycled concrete aggregates (RCAs) in comparison with the companion natural ones, is the presence of the Attached Mortar content which confers to the RCAs higher porosity and, consequently, higher water absorption capacity. In the last decades, several studies have been performed worldwide for unveiling the role of the employed RCAs on the resulting concrete mixture performances at both fresh and hardened states. On the other hand, the effects of RCA on the early‐age and/or long‐term concrete deformations (e.g., shrinkage) are still very limited. In this context, this article summarizes the results of a wide experimental campaign aimed at evaluating the influence of RCAs on the autogenous and drying shrinkage of normal (35 MPa) and high (60 MPa) strength recycled aggregate concrete (RAC) mixtures. A total of 10 mixtures were realized by considering the variation of both the original source and size of employed RCAs. The results showed a clear correlation between the intrinsic properties (i.e., Attached Mortar content) of the employed RCAs and the resulting concrete deformation. Moreover, although the drying shrinkage is higher for RAC mixtures, the presence of a more porous aggregates mitigates the observed autogenous shrinkage. Consequently, the overall total shrinkage result to be almost unaffected by the presence of the recycled particles.
Trans/fat: an autoethnographic exploration of becoming at the intersection of trans and fat

ABSTRACT The fat body and the transgender body are expected to always be in a state of becoming. For fat bodies, becoming less fat, for transgender bodies, becoming more “congruent. ” To be fat and/or transgender means coming into constant confrontation with social and cultural expectations about the fat and (trans)gendered body. It means navigating a medical system that considers one a problem to be solved with a careful and pre-determined set of solutions. It means diminished autonomy and little agency. Fat bodies and transgender bodies are often met with solutions based on gatekeeping versus informed consent. Fat bodies and trans bodies are often unwelcome and even made invisible in public spaces. When there are no chairs to fit fat bodies, and no bathrooms to include trans bodies, those bodies are erased, and the gender binary and body ideal reified. When transgender studies excludes discussion and acknowledgment of fat bodies and when fat studies excludes transgender bodies from their analysis or employs gender as a binary characteristic, fat trans bodies are disappeared, raising the question, how might thinking intersectionally reinvigorate both fields? This auto ethnographic exploration of navigating social and medical structures as a fat, trans/non-binary individual seeks to underscore the many commonalities inherent in anti-fat and (trans)gender oppression, and to highlight the ways these oppressions intersect to create unique barriers for fat, trans folks.