Miracle-dz/UniFAR-FTUs huggingface dataset:
MeLPUF: Memory in Logic PUF

Physical Unclonable Functions (PUFs) are used for securing electronic designs across the implementation spectrum ranging from lightweight FPGA to server-class ASIC designs. However, current PUF implementations are vulnerable to model-building attacks; they often incur significant design overheads and are challenging to configure based on application-specific requirements. These factors limit their application, primarily in the case of the system on chip (SoC) designs used in diverse applications. In this work, we propose MeL-PUF - Memory-in-Logic PUF, a low-overhead, distributed, and synthesizable PUF that takes advantage of existing logic gates in a design and transforms them to create cross-coupled inverters (i.e. memory cells) controlled by a PUF control signal. The power-up states of these memory cells are used as the source of entropy in the proposed PUF architecture. These on-demand memory cells can be distributed across the combinational logic of various intellectual property (IP) blocks in a system on chip (SoC) design. They can also be synthesized with a standard logic synthesis tool to meet the area,power, or performance constraints of a design. By aggregating the power-up states from multiple such memory cells, we can create a PUF signature or digital fingerprint of varying size. We evaluate the MeL-PUF signature quality with both circuit-level simulations as well as with measurements in FPGA devices. We show that MeL-PUF provides high-quality signatures in terms of uniqueness, randomness, and robustness, without incurring large overheads. We also suggest additional optimizations that can be leveraged to improve the performance of MeL-PUF.
Hybrid Chaotic Method for Medical Images Ciphering

Healthcare is an essential application of e-services, where for diagnostic testing, medical imaging acquiring, processing, analysis, storage, and protection are used. Image ciphering during storage and transmission over the networks used has seen implemented using many types of ciphering algorithms for security purpose. Current cyphering algorithms are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, RC5, RSA, .) and chaos cryptography using continuous (Chau, Rossler, Lorenz, .) or discreet (Logistics, Henon, .) algorithms. The traditional algorithms have struggled to combat image data as compared to regular textual data. Whereas, the chaotic algorithms are more efficient for image ciphering. The Significance characteristics of chaos are its extreme sensitivity to initial conditions and algorithm parameters. In this paper, medical image security based on hybrid/mixed chaotic algorithms is proposed. The proposed method is implemented using MATLAB. Where the image of the Retina of the Eye to detect Blood Vessels is ciphered. The Pseudo-Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented, and their statistical properties are evaluated using the National Institute of Standards and Technology NIST and other statistical test-suits. Then, these algorithms are used to secure the data, where the statistical properties of the cipher-text are also tested. We propose two PRNGs to increase the complexity of the PRNGs and to allow many of the NIST statistical tests to be passed: one based on two-hybrid mixed chaotic logistic maps and one based on two-hybrid mixed chaotic Henon maps, where each chaotic algorithm runs side-by-side and starts with random initial conditions and parameters (encryption keys). The resulting hybrid PRNGs passed many of the NIST statistical test suits.
Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale

The aerosol mixing state significantly affects the climate and health impacts of atmospheric aerosol particles. Simplified aerosol mixing state assumptions, common in Earth System models, can introduce errors in the prediction of these aerosol impacts. The aerosol mixing state index, a metric to quantify aerosol mixing state, is a convenient measure for quantifying these errors. Global estimates of aerosol mixing state indices have recently become available via supervised learning models, but require regionalization to ease spatiotemporal analysis. Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices. We used the monthly average of aerosol mixing state indices global distribution as the input data. Grid cells were then clustered into regions by the k-means algorithm without explicit spatial information as input. This approach resulted in eleven regions over the globe with specific spatial aggregation patterns. Each region exhibited a unique distribution of mixing state indices and aerosol compositions, showing the effectiveness of the unsupervised regionalization approach. This study defines "aerosol mixing state zones" that could be useful for atmospheric science research.
Helium bubbles in alpha-irradiated aluminium: positron lifetime studies

The formation of He-stabilised voids in aluminium single crystals injected with helium is monitored by positron lifetime measurements. The bubbles are observed to grow during annealing from 300 to 930K. The bubbles are extremely stable and survive annealing up to the melting point. Positron lifetime data are used to discuss the He density inside the bubbles and their growth mechanism.
High Resolution Mass Spectrometry Using FTICR and Orbitrap Instruments

From the 1950s to the present, mass spectrometry has evolved tremendously. The pioneering mass spectrometrist had a home-built naked instrument, typically a magnetic sector instrument with electron ionisation. Nowadays, highly automated commercial systems, able to produce thousands of spectra per day, are now concealed in a “black box”, a nicely designed and beautifully coloured unit resembling more an espresso machine or a tumble dryer than a mass spectrometer.
Prevalence of Sarcopenia in Older South African Patients Following Surgery for Fragility Fractures of the Hip

Introduction: Geriatric patients with a fragility fracture of the hip (FFH) are especially prone to sarcopenia with poor functional outcomes and quality of life. We assessed the prevalence of sarcopenia in older South African patients with FFH. Risk factors for sarcopenia were also investigated. Materials and Methods: From August 1 to November 30, 2018, all older patients with FFH were invited to participate. Sarcopenia was diagnosed based on the revised criteria of the European Working Group on Sarcopenia in Older People (EWGSOP2). Handgrip strength (HGS) and muscle strength were assessed. Muscle quantity was determined by dual-energy X-ray absorptiometry. Demographic information was collected, and 25-hydroxyvitamin D (25[OH]D) status was determined. Results: Of the 100 hip fracture cases, 65 were enrolled, and 52% (34/65) were sarcopenic (women: 62%; men: 38%). HGS accurately identified sarcopenia (sensitivity and specificity: 100%). Patients >80 years of age had a prevalence of sarcopenia twice (18/21 [83%]) that of younger patients (18/44 [36%]). Women with sarcopenia were smaller than those without (weight: p < 0.001; height: p < 0.001; body mass index: p = 0.018). Low 25(OH)D was almost universally present, with median 25(OH)D levels significantly lower in the patients with sarcopenia (27 nmol/L [interquartile range {IQR}: 20–39] vs. 40 nmol/L [IQR: 29–53]). Several risk factors, including advanced age; female sex; a smaller body size, especially among women; limited physical activity; and low 25(OH)D levels, were identified. Discussion: The accuracy of HGS testing in this cohort underscores EWGSOP2’s recommendation that muscle strength is key to sarcopenia. Further study and follow-up are required to determine the clinical relevance of sarcopenia among FFH patients. Conclusion: The prevalence of sarcopenia in our FFH population is high. Sarcopenia is associated with poor patient outcomes following surgical intervention. Orthopaedic surgeons should therefore be cognizant of the presentation and associated risk of sarcopenia as our patient populations age.
Human-Computer Interaction with Adaptable & Adaptive Motion-based Games for Health

Physical activity plays a major role both in prevention and in the treatment of afflictions linked to a modern sedentary lifestyle and improvements on life expectancy, for example though the application area of physiotherapy. Motion-based games for health (MGH) are being discussed in research and industry for their ability to play a supportive role in health, by offering motivation to engage in treatments, objective insights on status and development, and guidance regarding treatment activities. Difficulty settings in games are typically limited to few discrete tiers. For most serious applications in health, more fine-grained and far-reaching adjustments are required. The need for applying adjustments on complex sets of parameters can be overwhelming for patient-players and even trained professionals. Automatic adaptivity and efficient manual adaptability are thus major concerns for the design and development of MGH. Despite a growing amount of research on specific methods for adaptivity, general considerations on human-computer interaction with adaptable and adaptive MGH are rare. This thesis therefore focuses on establishing and augmenting theory for adaptability and adaptivity in human-computer interaction in the context of MGH. Working with older adults and people with Parkinson's disease as frequent target groups that can benefit from tailored activities, explorations and comparative studies that investigate the design, acceptance, and effectiveness of MGH are presented. The outcomes encourage the application of adaptivity for MGH following iterative human-centred design that considers the respective interests of stakeholders, provided that the users receive adequate information and are empowered to exert control over the automated system when desired or required, and if adaptivity is embedded in such a way that it does not interfere with the users' sense of competence or autonomy.
Extraction of inulin from Burdock root (Arctium lappa) using high intensity ultrasound

Summary Burdock root could be a source of inulin, but scarce studies about its extraction exist. To optimise conventional and ultrasound-assisted extraction of inulin, various combinations were used. The independent variables for ultrasound extraction were sonication amplitude (20–100%), sonication temperature (20–60 °C) and sonication time (5–25 min) while for conventional extraction the following variables were water to solid ratio (5–15 v/w), temperature (40–90 °C) and time (5–40 min). For each response, a second-order polynomial model was developed using multiple linear regression analysis. The use of high intensity ultrasound significantly improves the extraction of inulin contained within the body of Burdock root. It was shown that increasing the amplitude (from 20% to 85%) and extraction time increased the extraction yield; however, the effect of temperature was minor. Optimum extraction condition was found to be sonication time: 25 min, sonication amplitude 83.22% and temperature: 36.76 °C.
THE KINETICS OF LASER PULSE VAPORIZATION OF URANIUM DIOXIDE BY MASS SPECTROMETRY

Safety analyses of nuclear reactors require knowledge of the evaporation behavior of UO/sub 2/ at temperatures well above the melting point of 3140 K. In this study, rapid transient heating of a small spot on a UO/sub 2/ specimen was accomplished by a laser pulse, which generates a surface temperature excursion. This in turn vaporizes the target surface and the gas expands into vacuum. The surface temperature transient was monitored by a fast-response automatic optical pyrometer. The maximum surface temperatures investigated range from approx. 3700 K to approx. 4300 K. A computer program was developed to simulate the laser heating process and calculate the surface temperature evolution. The effect of the uncertainties of the high temperature material properties on the calculation was included in a sensitivity study for UO/sub 2/ vaporization. The measured surface temperatures were in satisfactory agreements.
Supplementary information to: Band gaps of crystalline solids from Wannier-localization based optimal tuning of a screened range-separated hybrid functional

Dahvyd Wing, Guy Ohad, Jonah B. Haber, 3 Marina R. Filip, Stephen E. Gant, 3 Jeffrey B. Neaton, 3, 5 and Leeor Kronik Department of Materials and Interfaces, Weizmann Institute of Science, Rehovoth 76100, Israel Department of Physics, University of California, Berkeley, Berkeley, California 94720, USA Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA Department of Physics, University of Oxford, Oxford OX1 3PJ, United Kingdom Kavli Energy NanoSciences Institute at Berkeley, Berkeley, CA, 94720, USA
PAC-Learning for Strategic Classification

Machine learning (ML) algorithms may be susceptible to being gamed by individuals with knowledge of the algorithm (a.k.a. Goodhart's law). Such concerns have motivated a surge of recent work on strategic classification where each data point is a self-interested agent and may strategically manipulate his features to induce a more desirable classification outcome for himself. Previous works assume agents have homogeneous preferences and all equally prefer the positive label. This paper generalizes strategic classification to settings where different data points may have different preferences over the classification outcomes. Besides a richer model, this generalization allows us to include evasion attacks in adversarial ML also as a special case of our model where positive [resp. negative] data points prefer the negative [resp. positive] label, and thus for the first time allows strategic and adversarial learning to be studied under the same framework. We introduce the strategic VC-dimension (SVC), which captures the PAC-learnability of a hypothesis class in our general strategic setup. SVC generalizes the notion of adversarial VC-dimension (AVC) introduced recently by Cullina et al. arXiv:1806.01471. We then instantiate our framework for arguably the most basic hypothesis class, i.e., linear classifiers. We fully characterize the statistical learnability of linear classifiers by pinning down its SVC and the computational tractability by pinning down the complexity of the empirical risk minimization problem. Our bound of SVC for linear classifiers also strictly generalizes the AVC bound for linear classifiers in arXiv:1806.01471. Finally, we briefly study the power of randomization in our strategic classification setup. We show that randomization may strictly increase the accuracy in general, but will not help in the special case of adversarial classification under evasion attacks.
Sensitivity Analysis of Transient‐MIM HYDRUS‐1D: Case Study Related to Pesticide Fate in Soils

A sensitivity analysis of the HYDRUS‐1D model was performed based on scenarios that represented the fate of a nonvolatile pesticide in three soils. We used the latest dual‐porosity, transient mobile–immobile (MIM) version of HYDRUS‐1D that includes water and solute transfers between mobile and immobile domains. We considered flat bare soils under atmospheric boundary conditions with identical initial pressure head profiles. We targeted instantaneous and mean annual pesticide concentration values at 1‐m depth as relevant output variables. Reference values for most of the parameters were obtained from in situ or laboratory measurements. Automation of parameter set generation and input formatting was achieved using SENSAN. We used a one‐group‐at‐a‐time sensitivity analysis to isolate and identify the effect of single parameter variations whose ranges were potentially different between topsoil layers (Group 1) and deep layers (Group 2). Sensitivity was analyzed by calculating the time‐dependent ratio of variation, i.e., the ratio of relative variation in output concentration to relative variation in parameter input values. As found for other single‐ or dual‐ permeability models, transient‐MIM HYDRUS‐1D was highly sensitive to parameters related to pesticide degradation and sorption, especially the Freundlich exponent associated with nonlinear sorption. Variations in the parameter n of the Mualem–van Genuchten functions had also a very significant impact. Among the MIM parameters, only saturated immobile and mobile water contents were very sensitive parameters, especially those of the topsoil layers. Parameter sensitivities hardly varied among the three soils, but this could be due to their limited contrast.
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction

We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face1. Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoint or headposes is required. In contrast to state-of-the-art approaches that model the geometry and material properties explicitly, or are purely image-based, we introduce an implicit representation of the head based on scene representation networks. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup. In our experiments, we show that this learned volumetric representation allows for photorealistic image generation that surpasses the quality of state-of-the-art video-based reenactment methods.
Effect of High-Pressure Processing on the Microbial Quality throughout the Shelf Life of Vacuum-Packed Sliced Ham and Frankfurters

Microbial spoilage is one of the most important factors threatening the shelf life of meat products. High-pressure processing (HPP) is growing out of its infancy with an increasing number of commercial applications. The aim of this study was to evaluate the efficiency of HPP on the microbial quality of commercial ham and frankfurters over their shelf life period. Commercially manufactured ham and frankfurters were processed with HPP at 600 MPa for 3 min and were then stored in parallel with nontreated samples (control) at 4C for a period of 7 weeks. All samples were weekly analyzed for the following parameters: total aerobic count (TAC), coliforms, Escherichia coli, staphylococci, yeasts and moulds, Salmonella spp. , and Listeria spp. At the end of the tested shelf life period, a significant difference (P < 0.005) was observed on the TAC between HPP-treated and nontreated samples for both products tested, demonstrating the efficiency of the HPP method. Practical Applications This study shows that the HPP treatment at 600 MPa for 3 min is effective at increasing significantly the shelf life of the two products tested. Sliced ham and frankfurters are among the most popular meat products consumed and the majority of the producers apply vacuum packaging to these products. The meat industry is seeking ways to increase the shelf life of these products and it was shown here that this can be achieved by the application of modern commercial HPP systems.
Flocculation in Turbulent Flow: Measurement and Modeling of Particle Size Distributions

Fluid motion in an oscillating-grid flocculator was investigated quantitatively with a laser Doppler velocimeter for two mixing conditions. Batch flocculation experiments were performed for monodisperse, bimodal, and trimodal latex suspensions. The effects of mixing intensity, particle charge, and suspension concentration on the rate of change in particle size distributions were studied. Experimental results indicated that large eddies are relatively unimportant for flocculation and that flocculation is induced by eddies of approximately the same size as the particles. A method for estimating the velocity gradients in eddies of different sizes in the flow was developed and incorporated into an existing mathematical model for flocculation. The predictions of the revised flocculation model were compared with the experimental results and indicated that the velocity gradients of the eddies adequately describe the interparticle contacts resulting from mixing during flocculation.
deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown noise statistics. In this article, we present a DL method, deSpeckNet,1 that estimates the speckle noise distribution and the despeckled image simultaneously. Since it does not depend on a specific noise model, deSpeckNet generalizes well across SAR acquisitions in a variety of landcover conditions. We evaluated the performance of deSpeckNet on single polarized Sentinel-1 images acquired in Indonesia, The Democratic Republic of Congo, and The Netherlands, a single polarized ALOS-2/PALSAR-2 image acquired in Japan and an Iceye X2 image acquired in Germany. In all cases, deSpeckNet was able to effectively reduce speckle and restore the images in high quality with respect to the state of the art.
Two-Timescale Resource Allocation for Cooperative D2D Communication: A Matching Game Approach

In this paper, we consider a cooperative device-to-device (D2D) communication system, where the D2D transmitters (DTs) act as relays to assist the densified cellular network users (CUs) for transmission quality of service (QoS) improvement. The proposed system achieves a win-win situation, i.e. improving the spectrum efficiency of the CUs that cannot meet their rate requirement while providing spectrum access for D2D pairs. Unlike previous works, to reduce the overhead, we design a novel two-timescale resource allocation scheme, in which the pairing between CUs and D2D pairs is decided at a long timescale and transmission time for CU and D2D pair is determined at a short timescale. Specifically, to characterize the long-term payoff of each potential CU-D2D pair, we investigate the optimal cooperation policy to decide the transmission time based on the instantaneous channel state information (CSI). We prove that the optimal policy is a threshold policy which can be achieved via binary search. Since CUs and D2D pairs are self-interested, they are paired only when they agree to cooperate mutually. Therefore, to study the cooperation behaviors of CUs and D2D pairs, we formulate the pairing problem as a matching game, based on the long-term payoff achieved by the optimal cooperation policy of each possible pairing. Furthermore, unlike most previous matching models in D2D networks, we allow transfer between CUs and D2D pairs to improve the performance. To solve the pairing problem, a distributed algorithm is proposed, which converges to an $\epsilon$-stable matching. We show that there is a trade-off between the optimality and the computational complexity of the algorithm. We also analyze the algorithm in terms of the robustness to the unilateral deviation of D2D pairs. Finally, the simulation results verify the efficiency of the proposed matching algorithm.