Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Foundations of Physical Unclonable Functions

We investigate the foundations of Physical Unclonable Functions from several perspectives. Firstly, we discuss formal and conceptual issues in the various current definitions of PUFs. As we argue, they have the effect that many PUF candidates formally meet no existing definition. Next, we present alternative definitions and a new formalism. It avoids asymptotic concepts like polynomial time, but is based on concrete time bounds and on the concept of a security experiment. The formalism splits the notion of a PUF into two new notions, Strong t-PUFs and Obfuscating t-PUFs. Then, we provide a comparative analysis between the existing definitions and our new notions, by classifying existing PUF implementations with respect to them. In this process, we use several new and unpublished machine learning results. The outcome of this comparative classification is that our definitions seem to match the current PUF landscape well, perhaps better than previous definitions. Finally, we analyze the security and practicality features of Strong and Obfuscating t-PUFs in concrete applications, obtaining further justification for the split into two notions.
Maximum Sum Rate of Slotted Aloha With Capture

The sum rate performance of random-access networks crucially depends on the access protocol and receiver structure. Despite extensive studies, how to characterize the maximum sum rate of the simplest version of random access, Aloha, remains an open question. In this paper, a comprehensive study of the sum rate performance of slotted Aloha networks is presented. By extending the unified analytical framework from the classical collision model to the capture model, the network steady-state point in saturated conditions is derived as a function of the signal-to-interference-plus-noise ratio (SINR) threshold, which determines a fundamental tradeoff between the information encoding rate and the network throughput. To maximize the sum rate, both the SINR threshold and backoff parameters of nodes should be properly selected. Explicit expressions of the maximum sum rate and the optimal setting are obtained, which show that similar to the sum capacity of the multiple access channel, the maximum sum rate of slotted Aloha also logarithmically increases with the mean received signal-to-noise ratio (SNR), but the high-SNR slope is only $e^{-1}$. Effects of backoff and power control on the sum rate performance of slotted Aloha networks are further discussed, which shed important light on the practical network design.
Measurement Placement in Distribution System State Estimation

This paper introduces a technique for meter placement for the purpose of improving the quality of voltage and angle estimates across a network. The proposed technique is based on the sequential improvement of a bivariate probability index governing relative errors in voltage and angle at each bus. The meter placement problem is simplified by transforming it into a probability bound reduction problem, with the help of the two sided-Chebyshev inequality. A straightforward solution technique is proposed for the latter problem, based on the consideration of 2-sigma error ellipses. The benefits of the proposed technique are revealed by Monte Carlo simulations on a 95-bus UKGDS network model.
Managing don't cares in Boolean satisfiability

Advances in Boolean satisfiability solvers have popularized their use in many of today's CAD VLSI challenges. Existing satisfiability solvers operate on a circuit representation that does not capture all of the structural circuit characteristics and properties. This work proposes algorithms that take into account the circuit don't care conditions thus enhancing the performance of these tools. Don't care sets are addressed in this work both statically and dynamically to reduce the search space and guide the decision making process. Experiments demonstrate performance gains.
The More the Tubular: Dynamic Bundling of Actin Filaments for Membrane Tube Formation

Tubular protrusions are a common feature of living cells, arising from polymerization of stiff protein filaments against a comparably soft membrane. Although this process involves many accessory proteins in cells, in vitro experiments indicate that similar tube-like structures can emerge without them, through spontaneous bundling of filaments mediated by the membrane. Using theory and simulation of physical models, we have elaborated how nonequilibrium fluctuations in growth kinetics and membrane shape can yield such protrusions. Enabled by a new grand canonical Monte Carlo method for membrane simulation, our work reveals a cascade of dynamical transitions from individually polymerizing filaments to highly cooperatively growing bundles as a dynamical bottleneck to tube formation. Filament network organization as well as adhesion points to the membrane, which bias filament bending and constrain membrane height fluctuations, screen the effective attractive interactions between filaments, significantly delaying bundling and tube formation.
Jammer Excision in Spread Spectrum Communications via Discrete Evolutionary Transform

In this paper, we propose time-frequency jammer excision techniques for direct sequence spread spectrum communications. One method is based on the estimation of the instantaneous frequency (IF) of each of the chirp jam-mer components using a combination of the discrete evolutionary and the Hough transforms. The second method is based in Wiener masking, and reduces the interference in a mean-square fashion. In the ﬁrst method, the jammer is synthesized and subtracted from the baseband received signal, while in the second method a mean-square estimate of the message is obtained. The IF-based method applies equally well to multi-component chirp jammers with constant or time-varying amplitudes, and instantaneous frequencies not necessarily parametrically modeled. A statistical analysis of this method is developed based on the signal to interference and noise ratio. We will show that the Wiener masking method is a general method requiring the spectrum of the spreading function. The two methods are illustrated by simulations.
Optimal user-centric relay assisted device-to-device communications: an auction approach

Device-to-device (D2D) communication has recently attracted much research attention because of its potential to increase the capacity of cellular networks. Most existing works aim to maximise the overall system throughput (system-centric), which ignores the actual traffic demands of D2D users. In this study, the authors consider user-centric relay assisted D2D communications where D2D users have different evaluations for the significance of every unit of increased data rate. By considering the traffic demands of D2D users, the authors propose a Vickrey–Clarke–Groves auction based relay allocation mechanism (ARM) in which every D2D user submits a bid to the basestation (BS). The submitted bids indicate D2D users’ valuation on every unit of the increased data rate. The BS then allocates relays to D2D users by maximising the social welfare of D2D users while maintaining a predefined data rate requirement for cellular users. A payment scheme to charge D2D users for using relays is designed, and the authors show that the auction is truthful. The authors also extend the results to a general case and provide a general ARM accordingly. Extensive simulation results are provided to demonstrate the performance of the proposed mechanisms.
A Scalable and Early Congestion Management Mechanism for MINs

Several packet marking-based mechanisms have been proposed to manage congestion in multistage interconnection networks. One of them, the MVCM mechanism obtains very good results for different network configurations and traffic loads. However, as MVCM applies full virtual output queuing at origin, its memory requirements may jeopardize its scalability. Additionally, the applied packet marking technique introduces certain delay to detect congestion. In this paper, we propose and evaluate the Scalable Early Congestion Management mechanism which eliminates the drawbacks exhibited by MVCM. The new mechanism replaces the full virtual output queuing at origin by either a partial virtual output queuing or a shared buffer, in order to reduce its memory requirements, thus making the mechanism scalable. Also, it applies an improved packet marking technique based on marking packets at output buffers regardless of their marking at input buffers, which simplifies the marking technique, allowing also a sooner detection of the root of a congestion tree.
Neural correlates of learning and trajectory planning in the posterior parietal cortex

The posterior parietal cortex (PPC) is thought to play an important role in the planning of visually-guided reaching movements. However, the relative roles of the various subdivisions of the PPC in this function are still poorly understood. For example, studies of dorsal area 5 point to a representation of reaches in both extrinsic (endpoint) and intrinsic (joint or muscle) coordinates, as evidenced by partial changes in preferred directions and positional discharge with changes in arm posture. In contrast, recent findings suggest that the adjacent medial intraparietal area (MIP) is involved in more abstract representations, e.g., encoding reach target in visual coordinates. Such a representation is suitable for planning reach trajectories involving shortest distance paths to targets straight ahead. However, it is currently unclear how MIP contributes to the planning of other types of trajectories, including those with various degrees of curvature. Such curved trajectories recruit different joint excursions and might help us address whether their representation in the PPC is purely in extrinsic coordinates or in intrinsic ones as well. Here we investigated the role of the PPC in these processes during an obstacle avoidance task for which the animals had not been explicitly trained. We found that PPC planning activity was predictive of both the spatial and temporal aspects of upcoming trajectories. The same PPC neurons predicted the upcoming trajectory in both endpoint and joint coordinates. The predictive power of these neurons remained stable and accurate despite concomitant motor learning across task conditions. These findings suggest the role of the PPC can be extended from specifying abstract movement goals to expressing these plans as corresponding trajectories in both endpoint and joint coordinates. Thus, the PPC appears to contribute to reach planning and approach-avoidance arm motions at multiple levels of representation.
Protein Adducts of Iso[4]levuglandin E2, a Product of the Isoprostane Pathway, in Oxidized Low Density Lipoprotein*

Levuglandin (LG) E2, a cytotoxic seco prostanoic acid co-generated with prostaglandins by nonenzymatic rearrangements of the cyclooxygenase-derived endoperoxide, prostaglandin H2, avidly binds to proteins. That LGE2-protein adducts can also be generated nonenzymatically is demonstrated by their production during free radical-induced oxidation of low density lipoprotein (LDL). Like oxidized LDL, LGE2-LDL, but not native LDL, undergoes receptor-mediated uptake and impaired processing by macrophage cells. Since radical-induced lipid oxidation produces isomers of prostaglandins, isoprostanes (isoPs), via endoperoxide intermediates, we postulated previously that a similar family of LG isomers, isoLGs, is cogenerated with isoPs. Now iso[4]LGE2-protein epitopes produced by radical-induced oxidation of arachidonic acid in the presence of protein were detected with an enzyme-linked immunosorbent assay. Iso[4]LGE2-protein epitopes are also generated during free radical-induced oxidation of LDL. All of the LGE2isomers generated upon oxidation of LDL are efficiently sequestered by covalent adduction with LDL-based amino groups. The potent electrophilic reactivity of iso-LGs can be anticipated to have biological consequences beyond their obvious potential as markers for specific arachidonate-derived protein modifications that may be of value for the quantitative assessment of oxidative injury.
Expression of Epstein‐Barr virus nuclear antigen‐1 induces B cell neoplasia in transgenic mice.

The Epstein‐Barr virus (EBV) nuclear antigen‐1 (EBNA‐1) is a pleiotropic protein which has been characterized extensively both biochemically and functionally. It is the only one of the identified latent protein‐encoding genes to be consistently expressed in viral‐associated endemic Burkitt's lymphoma cells. As such, it is the only candidate viral protein to possibly perform a maintenance function in the tumour pathology. Despite this, no oncogenic activity has been attributed to the protein in tissue culture assays. The experiments described here were initiated to explore the activity of the protein in B cells in vivo. EBNA‐1 transgenic mice were generated with transgene expression directed to the B cell compartment using the mouse Ig heavy chain intron enhancer. Transgene expression was demonstrated in the lymphoid tissues of mice of two independent lines. Transgenic positive mice of both lines succumb to B cell lymphoma. The B cell tumours are monoclonal, frequently of follicular centre cell origin and remarkably similar to those induced by transgenic c‐myc expression. These results demonstrate that EBNA‐1 is oncogenic in vivo and suggest that the gene product may play a direct role in the pathogenesis of Burkitt's lymphoma and possibly other EBV‐associated malignancies.
Low-power acoustic modem for dense underwater sensor networks

Significant progress has been made in terrestrial sensor networks to revolutionize sensing and data collection. To bring the concept of long-lived, dense sensor networks to the underwater environment, there is a compelling need to develop low-cost and low-power acoustic modems for short-range communications. This paper presents our work in designing and developing such a modem. We describe our design rationale followed by details of both hardware and software development. We have performed preliminary tests with transducers for in-air communications.
Lightweight 3D Modeling of Urban Buildings from Range Data

Laser range scanners are widely used to acquire accurate scene measurements. The massive point clouds they generate, however, present challenges to efficient modeling and visualization. State-of-the-art techniques for generating 3D models from voluminous range data is well-known to demand large computational and storage requirements. In this paper, attention is directed to the modeling of urban buildings directly from range data. We present an efficient modeling algorithm that exploits a priori knowledge that buildings can be modeled from cross-sectional contours using extrusion and tapering operations. Inspired by this simple workflow, we identify key cross-sectional slices among the point cloud. These slices capture changes across the building facade along the principal axes. Standard image processing algorithms are used to remove noise, fill missing data, and vectorize the projected points into planar contours. Applying extrusion and tapering operations to these contours permits us to achieve dramatic geometry compression, making the resulting models suitable for web-based applications such as Google Earth or Microsoft Virtual Earth. This work has applications in architecture, urban design, virtual city touring, and online gaming. We present experimental results on synthetic and real urban building datasets to validate the proposed algorithm.
An Entity-Focused Approach to Generating Company Descriptions

Finding quality descriptions on the web, such as those found in Wikipedia articles, of newer companies can be difﬁcult: search engines show many pages with varying relevance, while multi-document summarization algorithms ﬁnd it difﬁcult to distinguish between core facts and other information such as news stories. In this paper, we propose an entity-focused, hybrid generation approach to automatically produce descriptions of previously unseen companies, and show that it outperforms a strong summarization baseline.
The Essential Role of Cholesterol Metabolism in the Intracellular Survival of Mycobacterium leprae Is Not Coupled to Central Carbon Metabolism and Energy Production

ABSTRACT Mycobacterium leprae induces the formation of lipid droplets, which are recruited to pathogen-containing phagosomes in infected macrophages and Schwann cells. Cholesterol is among the lipids with increased abundance in M. leprae-infected cells, and intracellular survival relies on cholesterol accumulation. The present study investigated the capacity of M. leprae to acquire and metabolize cholesterol. In silico analyses showed that oxidation of cholesterol to cholest-4-en-3-one (cholestenone), the first step of cholesterol degradation catalyzed by the enzyme 3β-hydroxysteroid dehydrogenase (3β-HSD), is apparently the only portion of the cholesterol catabolic pathway seen in Mycobacterium tuberculosis preserved by M. leprae. Incubation of bacteria with radiolabeled cholesterol confirmed the in silico predictions. Radiorespirometry and lipid analyses performed after incubating M. leprae with [4-14C]cholesterol or [26-14C]cholesterol showed the inability of this pathogen to metabolize the sterol rings or the side chain of cholesterol as a source of energy and carbon. However, the bacteria avidly incorporated cholesterol and, as expected, converted it to cholestenone both in vitro and in vivo. Our data indicate that M. leprae has lost the capacity to degrade and utilize cholesterol as a nutritional source but retains the enzyme responsible for its oxidation to cholestenone. Thus, the essential role of cholesterol metabolism in the intracellular survival of M. leprae is uncoupled from central carbon metabolism and energy production. Further elucidation of cholesterol metabolism in the host cell during M. leprae infection will establish the mechanism by which this lipid supports M. leprae intracellular survival and will open new avenues for novel leprosy therapies. IMPORTANCE Our study focused on the obligate intracellular pathogen Mycobacterium leprae and its capacity to metabolize cholesterol. The data make an important contribution for those interested in understanding the mechanisms of mycobacterial pathogenesis, since they indicate that the essential role of cholesterol for M. leprae intracellular survival does not rely on its utilization as a nutritional source. Our findings reinforce the complexity of cholesterol's role in sustaining M. leprae infection. Further elucidation of cholesterol metabolism in the host cell during M. leprae infection will establish the mechanism by which this lipid supports M. leprae intracellular survival and will open new avenues for novel leprosy therapies.
Seng Lee Improving Co-integration Trading Rule Profitability with Forecasts from an Artificial Neural Network

Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy. Keywords—Artificial neural networks, co-integration, forecasting, trading rule.
Information fusion for obstacle recognition in visible and infrared images

We propose the information fusion of visible and infrared images for a pedestrian-vehicle SVM-based classification. Different types of fusion methods are presented: data fusion, feature fusion, matching score fusion and decision fusion. Data - level fusion assumes that the raw information is combined at the pixel level. The fusion at the feature level produces a feature vector integrating both visual and infrared information. Matching score fusion and decision fusion combine matching scores or decisions of individual obstacle recognition modules. Comparative results showed that fusion-based obstacle recognition techniques outperformed individual visual and infrared obstacle recognizers. An important advantage of these fusion-based systems is their possibility to adapt to the environmental illumination conditions due to a weighting parameter which also controls the system's final decision. Different feature extraction and feature selection algorithms have been investigated in order to retain the best suited features for the classification process.
The role of the Kramers–Henneberger atom in the higher-order Kerr effect

We discuss the connection between strong-field ionization, saturation of the Kerr response and the formation of the Kramers–Henneberger (KH) atom and long-living excitations in intense infrared (IR) external fields. We present a generalized model for the intensity-dependent response of atoms in strong IR laser fields, describing deviations in the nonlinear response at the frequency of the driving field from the standard model. We show that shaping the driving laser pulse allows one to reveal signatures of the excited KH states in the Kerr response of an individual atom.
Inverse Depth to Depth Conversion for Monocular SLAM

Recently it has been shown that an inverse depth parametrization can improve the performance of real-time monocular EKF SLAM, permitting undelayed initialization of features at all depths. However, the inverse depth parametrization requires the storage of 6 parameters in the state vector for each map point. This implies a noticeable computing overhead when compared with the standard 3 parameter XYZ Euclidean encoding of a 3D point, since the computational complexity of the EKF scales poorly with state vector size. In this work we propose to restrict the inverse depth parametrization only to cases where the standard Euclidean encoding implies a departure from linearity in the measurement equations. Every new map feature is still initialized using the 6 parameter inverse depth method. However, as the estimation evolves, if according to a linearity index the alternative XYZ coding can be considered linear, we show that feature parametrization can be transformed from inverse depth to XYZ for increased computational efficiency with little reduction in accuracy. We present a theoretical development of the necessary linearity indices, along with simulations to analyze the influence of the conversion threshold. Experiments performed with with a 30 frames per second real-time system are reported. An analysis of the increase in the map size that can be successfully managed is included.
Complex Search Task: How to Make a Phone Safe for a Child

There are many factors in task design that might make it ‘complex’: having multiple components, having multiple cross-dependent components, tasks that involve comparison, evaluation, estimation, or learning. In this paper, we discuss a case study of a complex task we may consider to be highly natural, a common concern for many people, and one that ‘should’ have a clear answer, but doesn’t: how do you make a phone safe for a child. For this question, there is a lot of opinion online, many possibilities for actions, many variations in hardware and software, but ultimately no one clear and correct answer for everyday phone users. We found very little objective behaviours that separated people in terms of performance but instead have begun to identify some successful tactics that are not directly linked to domain knowledge.