gebhart/patrepeval huggingface dataset:
Mining Attribute-based Access Control Policies (2013)

Attribute-based access control (ABAC) provides a high level of flexibility that promotes security and information sharing. ABAC policy mining algorithms have potential to significantly reduce the cost of migration to ABAC, by partially automating the development of an ABAC policy from an access control list (ACL) policy or role-based access control (RBAC) policy with accompanying attribute data. This paper presents an ABAC policy mining algorithm. To the best of our knowledge, it is the first ABAC policy mining algorithm. Our algorithm iterates over tuples in the given user-permission relation, uses selected tuples as seeds for constructing candidate rules, and attempts to generalize each candidate rule to cover additional tuples in the user-permission relation by replacing conjuncts in attribute expressions with constraints. Our algorithm attempts to improve the policy by merging and simplifying candidate rules, and then it selects the highest-quality candidate rules for inclusion in the generated policy.
A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment (2015)

The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing.
Stein Variational Policy Gradient (2017)

Policy gradient methods have been successfully applied to many complex reinforcement learning problems. However, policy gradient methods suffer from high variance, slow convergence, and inefficient exploration. In this work, we introduce a maximum entropy policy optimization framework which explicitly encourages parameter exploration, and show that this framework can be reduced to a Bayesian inference problem. We then propose a novel Stein variational policy gradient method (SVPG) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies. SVPG is robust to initialization and can easily be implemented in a parallel manner. On continuous control problems, we find that implementing SVPG on top of REINFORCE and advantage actor-critic algorithms improves both average return and data efficiency.
Context-Based Collaborative Self-Test for Autonomous Wireless Sensor Networks (2011)

Reliability is a major concern in autonomous wireless sensor networks. Current approaches to maintaining high overall system availability concentrate on pseudo-random test scheduling and test vector generation based on a probabilistic approach to failure prediction. In the case of wireless sensor networks though, most of device failures can be directly associated with specific events. Furthermore, these events can often be identified using the sensors already present on the nodes and used to trigger self test of the affected devices with test vectors specifically crafted to match the possible failures. In this paper, we discuss an approach to wireless sensor node self-testing using sensor data gathered by the device itself and by the neighboring nodes. We analyze possible impact of this approach on the Mean Time To Detect (MTTD) and the overall system availability. Also, the proposed approach can help decrease energy consumption of the system through avoiding unnecessary data communication and extensive hardware testing. We also discuss advantages arising from installation of additional dedicated sensors on the nodes that help to more accurately detect and classify an event and thus the possible failure and its severity. Finally, we present a test system that implements the proposed approach.
Efficient surface-emitting cold cathodes based on electroluminescent porous silicon diodes (1997)

The porosity multilayer technique has been applied to improve the previously-reported cold electron emission properties of electroluminescent porous silicon (PS) diodes. It is demonstrated here that by introducing a modified structrure into the PS layer, the emission efficiency is significantly improved. An extremely high efficiency of 12 % has been obtained. A significant decrease in the emission current fluctuation and a consequent increase in the durability of the emission are also observed. These results are due to a regulated electric field distribution near the outer surface ofthe PS layer.
How the headpiece hinge angle is opened: new insights into the dynamics of integrin activation (2006)

How the integrin head transitions to the high-affinity conformation is debated. Although experiments link activation with the opening of the hinge angle between the betaA and hybrid domains in the ligand-binding headpiece, this hinge is closed in the liganded alpha(v)beta3 integrin crystal structure. We replaced the RGD peptide ligand of this structure with the 10th type III fibronectin module (FnIII10) and discovered through molecular dynamics (MD) equilibrations that when the conformational constraints of the leg domains are lifted, the betaA/hybrid hinge opens spontaneously. Together with additional equilibrations on the same nanosecond timescale in which small structural variations impeded hinge-angle opening, these simulations allowed us to identify the allosteric pathway along which ligand-induced strain propagates via elastic distortions of the alpha1 helix to the betaA/hybrid domain hinge. Finally, we show with steered MD how force accelerates hinge-angle opening along the same allosteric pathway. Together with available experimental data, these predictions provide a novel framework for understanding integrin activation.
Expression of endogenous and foreign ribulose 1,5-bisphosphate carboxylase-oxygenase (RubisCO) genes in a RubisCO deletion mutant of Rhodobacter sphaeroides (1991)

A Rhodobacter sphaeroides ribulose 1,5-bisphosphate carboxylase-oxygenase (RubisCO) deletion strain was constructed that was complemented by plasmids containing either the form I or form II CO2 fixation gene cluster. This strain was also complemented by genes encoding foreign RubisCO enzymes expressed from a Rhodospirillum rubrum RubisCO promoter. In R. sphaeroides, the R. rubrum promoter was regulated, resulting in variable levels of disparate RubisCO molecules under different growth conditions. Photosynthetic growth of the R. sphaeroides deletion strain complemented with cyanobacterial RubisCO revealed physiological properties reflective of the unique cellular environment of the cyanobacterial enzyme. The R. sphaeroides RubisCO deletion strain and R. rubrum promoter system may be used to assess the properties of mutagenized proteins in vivo, as well as provide a potential means to select for altered RubisCO molecules after random mutagenesis of entire genes or gene regions encoding RubisCO enzymes.
Sub-20 nm LiErF<sub>4</sub>-Based Upconversion Nanophosphors for Simultaneous Imaging and Photothermal Therapeutics (2020)

This study reports red-emitting LiErF4:Tm-based upconversion nanophosphors (UCNPs) with small size and high brightness for simultaneous upconversion luminescence (UCL) imaging and photothermal therapeutics. Strong red UCL is realized from LiErF4-based UCNPs by doping Tm3+ into LiErF4 and forming multishells on the LiErF4:Tm core. The ultrasmall LiErF4:Tm UCNPs are facilely synthesized by using rare-earth-oleate precursors and a mixed solvent of oleic acid and 1-octadecene. The size of the ultrasmall LiErF4:Tm(0.3%) UCNPs is finely tuned from 2.8 to 10.4 nm, and LiErF4:Tm(0.3%)/LiGdF4 core/shell (C/S) UCNPs show small sizes ranging from 8.6 to 13.2 nm. Notably, 11.4 nm-sized C/S UCNPs show bright red light. Furthermore, LiErF4:Tm-based core/triple-shell (C/T-S) UCNPs are synthesized, and they show dramatic enhancement of red UCL (∼4242-fold enhancement compared with the core under 980 nm excitation and ∼89-fold and ∼14-fold enhancement compared with the C/S UCNPs under 800 and 1532 nm excitation, respectively). Besides UCL enhancement, Nd3+ ions doped in the LiErF4:Tm-based C/T-S UCNPs generate heat under 800 nm irradiation, allowing the C/T-S UCNPs to be applied as photothermal therapeutic agents. The ultrasmall (<20 nm) and bright red-emitting LiErF4:Tm-based C/T-S UCNPs have the potential for simultaneous imaging and photothermal therapeutic applications.
Graphene Nanoribbon Spin Interconnects for Nonlocal Spin-Torque Circuits: Comparison of Performance and Energy Per Bit With CMOS Interconnects (2011)

Solid-state devices based on utilizing the electron spin for storing and manipulating information may pave the way for next-generation computers. In this paper, the performance and the energy dissipation of graphene spin interconnects in a nonlocal spin-torque (NLST) circuit are compared against those of the CMOS circuit at the end of the silicon technology roadmap (technology year 2020). The interconnect-oriented limitations of the NLST circuit are quantified through exhaustive simulations. The impact of the electron scattering at the edges of the graphene nanoribbon (GNR) on the speed and the energy dissipation of the NLST circuit are evaluated. It is found that, if the edges in the GNR scatter electrons with a 100% probability, then the delay of the interconnect in the NLST circuit is <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{10}\times$</tex></formula> higher than its value at perfectly passivated GNR edges for an interconnect with a length of 100 gate pitches and a width of 14 nm. The scattering of electrons at the GNR edges leads to inefficient spin injection in the interconnect, which increases the energy dissipation of the NLST circuit. Energy versus delay landscapes of the NLST circuit and the CMOS circuit are compared. It is found that the energy versus delay of the NLST circuit exhibits a minimum in energy, which is in striking in contrast with the energy-versus-delay behavior of the CMOS circuit.
Fine-grained recognition of abnormal behaviors for early detection of mild cognitive impairment (2015)

According to the World Health Organization, the rate of people aged 60 or more is growing faster than any other age group in almost every country, and this trend is not going to change in a near future. Since senior citizens are at high risk of non communicable diseases requiring long-term care, this trend will challenge the sustainability of the entire health system. Pervasive computing can provide innovative methods and tools for early detecting the onset of health issues. In this paper we propose a novel method relying on medical models, provided by cognitive neuroscience researchers, describing abnormal activity routines that may indicate the onset of early symptoms of mild cognitive impairment. A non-intrusive sensor-based infrastructure acquires low-level data about the interaction of the individual with home appliances and furniture, as well as data from environmental sensors. Based on those data, a novel hybrid statistical-symbolical technique is used to detect first the activities being performed and then the abnormal aspects in carrying out those activities, which are communicated to the medical center. Differently from related works, our method can detect abnormal behaviors at a fine-grained level, thus providing an important tool to support the medical diagnosis. In order to evaluate our method we have developed a prototype of the system and acquired a large dataset of abnormal behaviors carried out in an instrumented smart home. Experimental results show that our technique has a high precision while generating a small number of false positives.
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework (2019)

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as they are competing in nature. In this article, a mechanism for radio resource management using multi-agent deep reinforcement learning (RL) is proposed, which strikes the right trade-off between maximizing the average and the $5^{th}$ percentile user throughput. Each transmitter in the network is equipped with a deep RL agent, receiving partial observations from the network (e.g., channel quality, interference level, etc.) and deciding whether to be active or inactive at each scheduling interval for given radio resources, a process referred to as link scheduling. Based on the actions of all agents, the network emits a reward to the agents, indicating how good their joint decisions were. The proposed framework enables the agents to make decisions in a distributed manner, and the reward is designed in such a way that the agents strive to guarantee a minimum performance, leading to a fair resource allocation among all users across the network. Simulation results demonstrate the superiority of our approach compared to decentralized baselines in terms of average and $5^{th}$ percentile user throughput, while achieving performance close to that of a centralized exhaustive search approach. Moreover, the proposed framework is robust to mismatches between training and testing scenarios. In particular, it is shown that an agent trained on a network with low transmitter density maintains its performance and outperforms the baselines when deployed in a network with a higher transmitter density.
Arc-scanning Very High-frequency Digital Ultrasound for 3D Pachymetric Mapping of the Corneal Epithelium and Stroma in Laser in situ Keratomileusis (2000)

VHF digital ultrasound arc-B scanning provides high-resolution imaging and high-precision three-dimensional thickness mapping of corneal layers, enabling accurate anatomical evaluation of the changes induced in the cornea by LASIK.
Small-footprint Keyword Spotting with Graph Convolutional Network (2019)

Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices. In this study, we propose a novel context-aware and compact architecture for keyword spotting task. Based on residual connection and bottleneck structure, we design a compact and efficient network for KWS task. To leverage the long range dependencies and global context of the convolutional feature maps, the graph convolutional network is introduced to encode the non-local relations. By evaluated on the Google Speech Command Dataset, the proposed method achieves state-of-the-art performance and outperforms the prior works by a large margin with lower computational cost.
Involvement of miRNAs in Placental Alterations Mediated by Oxidative Stress (2014)

Oxidative stress (OS) is known to be strongly involved in a large number of fetal, neonatal, and adult diseases, including placental disorders, leading to pregnancy loss and stillbirths. A growing body of research links OS to preeclampsia, gestational diabetes, obesity, spontaneous abortion, recurrent pregnancy, preterm labor, and intrauterine growth restriction. While a considerable number of miRNAs have been related to physiological functions and pathological conditions of the placenta, a direct link among these miRNAs, placental functions, and OS is still lacking. This review summarizes data describing the role of miRNAs in placental pathophysiological processes and their possible impact on OS damaging responses. As miRNAs can be found in circulation, improving our understanding on their role in the pathogenesis of pregnancy related disorders could have an important impact on the diagnosis and prognosis of these diseases.
Utilisation d’une lame de Savart pour un système de projection de franges interférométriques pour la mesure de forme 3D (2009)

Non-contact optical measurement methods are essential tools in many industrial domain. Hololab developed an original out of plane, in line and almost common path interferometer based on polarization states separation using a coated prism for fringes projection. This setup has proven efficient in the field of arts and archaeology but does not meet the demands of the industrial world. Above the acquisition speed and the post treatment process, one of the disadvantages comes from the sensitivity of the system to the environment. To overcome this disadvantage, the coated prism is substituted by a Savart plate. This device has already been used in another field of optical metrology as a shearing device. Many advantages of the prism substitution are discussed and recorded fringes patterns will be shown.
Evaluation of a High-Throughput Peptide Reactivity Format Assay for Assessment of the Skin Sensitization Potential of Chemicals (2016)

The direct peptide reactivity assay (DPRA) is a validated method for in vitro assessment of the skin sensitization potential of chemicals. In the present work, we describe a peptide reactivity assay using 96-well plate format and systematically identified the optimal assay conditions for accurate and reproducible classification of chemicals with known sensitizing capacity. The aim of the research is to ensure that the analytical component of the peptide reactivity assay is robust, accurate, and reproducible in accordance with criteria that are used for the validation of bioanalytical methods. Analytical performance was evaluated using quality control samples (QCs; heptapeptides at low, medium, and high concentrations) and incubation of control chemicals (chemicals with known sensitization capacity, weak, moderate, strong, extreme, and non-sensitizers) with each of three synthetic heptapeptides, viz Cor1-C420 (Ac-NKKCDLF), cysteine- (Ac-RFAACAA), and lysine- (Ac-RFAAKAA) containing heptapeptides. The optimal incubation temperature for all three heptapeptides was 25°C. Apparent heptapeptide depletion was affected by vial material composition. Incubation of test chemicals with Cor1-C420, showed that peptide depletion was unchanged in polypropylene vials over 3-days storage in an autosampler but this was not the case for borosilicate glass vials. For cysteine-containing heptapeptide, the concentration was not stable by day 3 post-incubation in borosilicate glass vials. Although the lysine-containing heptapeptide concentration was unchanged in both polypropylene and borosilicate glass vials, the apparent extent of lysine-containing heptapeptide depletion by ethyl acrylate, differed between polypropylene (24.7%) and glass (47.3%) vials. Additionally, the peptide-chemical complexes for Cor1-C420-cinnamaldehyde and cysteine-containing heptapeptide-2, 4-dinitrochlorobenzene were partially reversible during 3-days of autosampler storage. These observations further highlight the difficulty in adapting in vitro methods to high-throughput format for screening the skin sensitization potential of large numbers of chemicals whilst ensuring that the data produced are both accurate and reproducible.
Inferotemporal Cortex and Object Vision (1996)

Cells in area TE of the inferotemporal cortex of the monkey brain selectively respond to various moderately complex object features, and those that cluster in a columnar region that runs perpendicular to the cortical surface respond to similar features. Although cells within a column respond to similar features, their selectivity is not necessarily identical. The data of optical imaging in TE have suggested that the borders between neighboring columns are not discrete; a continuous mapping of complex feature space within a larger region contains several partially overlapped columns. This continuous mapping may be used for various computations, such as production of the image of the object at different viewing angles, illumination conditions, and articulation poses.
Loss of NPC1 enhances phagocytic uptake and impairs lipid trafficking in microglia (2021)

Niemann-Pick type C disease is a rare neurodegenerative disorder mainly caused by mutations in NPC1, resulting in abnormal late endosomal/lysosomal lipid storage. Although microgliosis is a prominent pathological feature, direct consequences of NPC1 loss on microglial function remain not fully characterized. We discovered pathological proteomic signatures and phenotypes in NPC1-deficient murine models and demonstrate a cell autonomous function of NPC1 in microglia. Loss of NPC1 triggers enhanced phagocytic uptake and impaired myelin turnover in microglia that precede neuronal death. Npc1<sup>-/-</sup> microglia feature a striking accumulation of multivesicular bodies and impaired trafficking of lipids to lysosomes while lysosomal degradation function remains preserved. Molecular and functional defects were also detected in blood-derived macrophages of NPC patients that provide a potential tool for monitoring disease. Our study underscores an essential cell autonomous role for NPC1 in immune cells and implies microglial therapeutic potential.
On efficient generation of instruction sequences to test for delay defects in a processor (2008)

We present a technique that deals with the problem of efficiently generating instruction sequences to test for delay defects in a processor. These instruction sequences are loaded into the cache of a processor and the processor is run in its normal functional (native) mode to test itself. The methodology that we present avoids the significant increase in search space of a previous method while generating tests. We also present a technique which increases the probability of detecting multiple delay faults with a single instruction sequence. This technique can help immensely in reducing the cost of test. We demonstrate the effectiveness of our technique on an off-the shelf processor.
Analysis of emotion recognition using facial expressions, speech and multimodal information (2004)

The interaction between human beings and computers will be more natural if computers are able to perceive and respond to human non-verbal communication such as emotions. Although several approaches have been proposed to recognize human emotions based on facial expressions or speech, relatively limited work has been done to fuse these two, and other, modalities to improve the accuracy and robustness of the emotion recognition system. This paper analyzes the strengths and the limitations of systems based only on facial expressions or acoustic information. It also discusses two approaches used to fuse these two modalities: decision level and feature level integration. Using a database recorded from an actress, four emotions were classified: sadness, anger, happiness, and neutral state. By the use of markers on her face, detailed facial motions were captured with motion capture, in conjunction with simultaneous speech recordings. The results reveal that the system based on facial expression gave better performance than the system based on just acoustic information for the emotions considered. Results also show the complementarily of the two modalities and that when these two modalities are fused, the performance and the robustness of the emotion recognition system improve measurably.