Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties

Efficacy of N-acetyl cysteine in traumatic brain injury

In this study, using two different injury models in two different species, we found that early post-injury treatment with NAcetyl Cysteine (NAC) reversed the behavioral deficits associated with the TBI. These data suggest generalization of a protocol similar to our recent clinical trial with NAC in blast-induced mTBI in a battlefield setting [1], to mild concussion from blunt trauma. This study used both weight drop in mice and fluid percussion injury in rats. These were chosen to simulate either mild or moderate traumatic brain injury (TBI). For mice, we used novel object recognition and the Y maze. For rats, we used the Morris water maze. NAC was administered beginning 30-60 minutes after injury. Behavioral deficits due to injury in both species were significantly reversed by NAC treatment. We thus conclude NAC produces significant behavioral recovery after injury. Future preclinical studies are needed to define the mechanism of action, perhaps leading to more effective therapies in man

Stability time of a DT-filled cryogenic ICF target in a high vacuum environment

Following the successful pressure loading with DT of a thin-walled plastic inertial fusion target shell (such as those designed for use at the OMEGA facility at the University of Rochester`s Laboratory for Laser Energetics (UR/LLE)), continual care must be taken to safeguard the shell from being exposed to unacceptable pressure differentials across its wall. In particular, once the DT has been condensed into a liquid or solid phase and the outside pressure has been reduced, the target must be maintained below some upper cutoff temperature such that the vapor pressure of the DT is below the bursting pressure for the shell. Through the process of {beta}-decay the DT self-heats, but while the shell is in a high vacuum environment (P {much_lt} 0.8 Pa (6 mtorr) for the OMEGA layering sphere) there is only a negligible heat loss mechanism. This will cause the temperature to increase. A calculation has been done to estimate the rate of temperature increase of the loaded target under high vacuum conditions. A functional form for calculating the target`s temperature increase given its starting temperature is presented. An overall result is that under high vacuum conditions the DT changes from a solid at 10 K to a liquid at 37 K (T{sub c} = 39.4 K) in about 19 minutes. This holding time is significantly less if the initial temperature is higher, the initial state is liquid, or the upper allowed temperature is lower. Simplifying assumptions which were made and their impact on interpreting the results of this calculation are discussed

Informed pair selection for self-paced metric learning in Siamese neural networks.

Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning in SNNs is not reliant on explicit class knowledge; instead they require knowledge about the relationship between pairs. Though often ignored, we have found that appropriate pair selection is crucial to maximising training efficiency, particularly in scenarios where examples are limited. In this paper, we study the role of informed pair selection and propose a 2-phased strategy of exploration and exploitation. Random sampling provides the needed coverage for exploration, while areas of uncertainty modeled by neighbourhood properties of the pairs drive exploitation. We adopt curriculum learning to organise the ordering of pairs at training time using similarity knowledge as a heuristic for pair sorting. The results of our experimental evaluation show that these strategies are key to optimising training

Analysis of Integrating Sphere Performance for IR Enhanced DT Layering

Absorbed IR energy can supplement the beta decay energy from DT ice to improve the driving force toward uniform layers. A significant problem with this approach has been to deliver the added IR energy with sufficient uniformity to enhance rather than destroy the uniformity of the ice layers. Computer modeling has indicated that one can achieve {approximately}1% uniformity in the angular variation of the absorbed power using an integrating sphere containing holes large enough to allow external inspection of the ice layer uniformity. The power required depends on the integrating sphere size, a 25 mm diameter sphere requires {approximately}35 mW of IR to deposit as much energy in the ice as the 50 mW/cm{sup 3}(35 pW total) received from tritium decay in DT. Power absorbed in the plastic can cause unacceptable ice-layer non-uniformities for the integrating sphere design considered here

Learning to compare with few data for personalised human activity recognition.

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related (but different) new tasks. Unlike task-specific model training, a meta-learner’s training instance - referred to as a meta-instance - is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning-to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN), which learns an embedding for a metric function; 2) relation network (RN) that learns to predict similarity between paired instances; and 3) MAML, a model-agnostic machine-learning algorithm that optimizes the model parameters for rapid adaptation. Results confirm that personalised meta-learning significantly improves performance over non personalised meta-learners

Local modes, phonons, and mass transport in solid 4^4He

We propose a model to treat the local motion of atoms in solid $^{4}$He as a local mode. In this model, the solid is assumed to be described by the Self Consistent Harmonic approximation, combined with an array of local modes. We show that in the bcc phase the atomic local motion is highly directional and correlated, while in the hcp phase there is no such correlation. The correlated motion in the bcc phase leads to a strong hybridization of the local modes with the T$_{1}(110)$ phonon branch, which becomes much softer than that obtained through a Self Consistent Harmonic calculation, in agreement with experiment. In addition we predict a high energy excitation branch which is important for self-diffusion. Both the hybridization and the presence of a high energy branch are a consequence of the correlation, and appear only in the bcc phase. We suggest that the local modes can play the role in mass transport usually attributed to point defects (vacancies). Our approach offers a more overall consistent picture than obtained using vacancies as the predominant point defect. In particular, we show that our approach resolves the long standing controversy regarding the contribution of point defects to the specific heat of solid $^{4}$He.Comment: 10 pages, 10 figure

Femtosecond Laser-Produced Plasma X-Rays from Periodically Modulated Surface Targets

We have studied theoretically and experimentally the x-ray production above 1 keV from femtosecond laser plasmas generated on periodically modulated surface targets. Laser energy coupling to plasma surface waves has been modeled using a numerical differential method. Almost total absorption of incident laser radiation is predicted for optimized interaction conditions. Silicon gratings have been irradiated by a 120fs Ti:sapphire laser at irradiances in excess of 1016 W/cm2. X-ray intensities above 1.5 keV (K-shell lines) have been measured as a function of the incidence angle. Results show a distinct x-ray emission maximum for the first order diffraction angle and are in good qualitative agreement with our theoretical predictions

Targeted over-expression of glutamate transporter 1 (GLT-1) reduces ischemic brain injury in a rat model of stroke

Following the onset of an ischemic brain injury, the excitatory neurotransmitter glutamate is released. The excitotoxic effects of glutamate are a major contributor to the pathogenesis of a stroke. The aim of this study was to examine if overexpression of a glutamate transporter (GLT-1) reduces ischemic brain injury in a rat model of stroke. We generated an adeno-associated viral (AAV) vector expressing the rat GLT-1 cDNA (AAV-GLT1). Functional expression of AAV-GLT1 was confirmed by increased glutamate clearance rate in non-stroke rat brain as measured by in vivo amperometry. AAV-GLT1 was injected into future cortical region of infarction 3 weeks prior to 60 min middle cerebral artery occlusion (MCAo). Tissue damage was assessed at one and two days after MCAo using TUNEL and TTC staining, respectively. Behavioral testing was performed at 2, 8 and 14 days post-stroke. Animals receiving AAV-GLT1, compared to AAV-GFP, showed significant decreases in the duration and magnitude of extracellular glutamate, measured by microdialysis, during the 60 minute MCAo. A significant reduction in brain infarction and DNA fragmentation was observed in the region of AAV-GLT1 injection. Animals that received AAV-GLT1 showed significant improvement in behavioral recovery following stroke compared to the AAV-GFP group. We demonstrate that focal overexpression of the glutamate transporter, GLT-1, significantly reduces ischemia-induced glutamate overflow, decreases cell death and improves behavioral recovery. These data further support the role of glutamate in the pathogenesis of ischemic damage in brain and demonstrate that targeted gene delivery to decrease the ischemia-induced glutamate overflow reduces the cellular and behavioral deficits caused by stroke

Neurosensory symptom complexes after acute mild traumatic brain injury

Mild Traumatic Brain Injury (mTBI) is a prominent public health issue. To date, subjective symptom complaints primarily dictate diagnostic and treatment approaches. As such, the description and qualification of these symptoms in the mTBI patient population is of great value. This manuscript describes the symptoms of mTBI patients as compared to controls in a larger study designed to examine the use of vestibular testing to diagnose mTBI. Five symptom clusters were identified: Post-Traumatic Headache/Migraine, Nausea, Emotional/ Affective, Fatigue/Malaise, and Dizziness/Mild Cognitive Impairment. Our analysis indicates that individuals with mTBI have headache, dizziness, and cognitive dysfunction far out of proportion to those without mTBI. In addition, sleep disorders and emotional issues were significantly more common amongst mTBI patients than non-injured individuals. A simple set of questions inquiring about dizziness, headache, and cognitive issues may provide diagnostic accuracy. The consideration of other symptoms may be critical for providing prognostic value and treatment for best short-term outcomes or prevention of long-term complications