Robust nonadiabatic molecular dynamics for metals and insulators

We present a new formulation of the correlated electron-ion dynamics (CEID) scheme, which systematically improves Ehrenfest dynamics by including quantum fluctuations around the mean-field atomic trajectories. We show that the method can simulate models of nonadiabatic electronic transitions and test it against exact integration of the time-dependent Schrodinger equation. Unlike previous formulations of CEID, the accuracy of this scheme depends on a single tunable parameter which sets the level of atomic fluctuations included. The convergence to the exact dynamics by increasing the tunable parameter is demonstrated for a model two level system. This algorithm provides a smooth description of the nonadiabatic electronic transitions which satisfies the kinematic constraints (energy and momentum conservation) and preserves quantum coherence. The applicability of this algorithm to more complex atomic systems is discussed.

Dense Multiperson Tracking with Robust Hierarchical Linear Assignment

We introduce a novel dual-stage algorithm for online multi-target tracking in realistic conditions. In the first stage, the problem of data association between tracklets and detections, given partial occlusion, is addressed using a novel occlusion robust appearance similarity method. This is used to robustly link tracklets with detections without requiring explicit knowledge of the occluded regions. In the second stage, tracklets are linked using a novel method of constraining the linking process that removes the need for ad-hoc tracklet linking rules. In this method, links between tracklets are permitted based on their agreement with optical flow evidence. Tests of this new tracking system have been performed using several public datasets.

Adaptive NormalHedge for robust visual tracking

In this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

Food Labels, Autonomy and the Right (Not) to Know

Food labelling has been overlooked in the emerging body of literature concerning the normative dimensions of food and drink policies. In this paper, I argue that arguments normally advanced in bioethics and medical ethics regarding the “right to know” and the “right not to know” can provide useful normative guidelines for critically assessing existing and proposed food labelling regimes. More specifically, I claim that food labelling ought to respect the legitimate interests and the autonomy of both consumers who seek knowledge about their food in order to make informed dietary choices and consumers who prefer to remain ignorant about the contents and effects of their food in order to avoid the emotional and psychological harm, or more simply the loss of enjoyment, which may result from receiving that information.

Process-variation resilient and voltage-scalable dct architecture for robust low-power computing

In this paper, we present a novel discrete cosine transform (DCT) architecture that allows aggressive voltage scaling for low-power dissipation, even under process parameter variations with minimal overhead as opposed to existing techniques. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors appear only from the long paths that are designed to be less contributive to output quality. The proposed architecture allows a graceful degradation in the peak SNR (PSNR) under aggressive voltage scaling as well as extreme process variations. Results show that even under large process variations (±3σ around mean threshold voltage) and aggressive supply voltage scaling (at 0.88 V, while the nominal voltage is 1.2 V for a 90-nm technology), there is a gradual degradation of image quality with considerable power savings (71% at PSNR of 23.4 dB) for the proposed architecture, when compared to existing implementations in a 90-nm process technology. © 2006 IEEE.

Voltage scalable high-speed robust hybrid arithmetic units using adaptive clocking

In this paper, we explore various arithmetic units for possible use in high-speed, high-yield ALUs operated at scaled supply voltage with adaptive clock stretching. We demonstrate that careful logic optimization of the existing arithmetic units (to create hybrid units) indeed make them further amenable to supply voltage scaling. Such hybrid units result from mixing right amount of fast arithmetic into the slower ones. Simulations on different hybrid adder and multipliers in BPTM 70 nm technology show 18%-50% improvements in power compared to standard adders with only 2%-8% increase in die-area at iso-yield. These optimized datapath units can be used to construct voltage scalable robust ALUs that can operate at high clock frequency with minimal performance degradation due to occasional clock stretching. © 2009 IEEE.

Logic and Memory Design Based on Unequal Error Protection for Voltage-scalable, Robust and Adaptive DSP Systems

In this paper, we propose a system level design approach considering voltage over-scaling (VOS) that achieves error resiliency using unequal error protection of different computation elements, while incurring minor quality degradation. Depending on user specifications and severity of process variations/channel noise, the degree of VOS in each block of the system is adaptively tuned to ensure minimum system power while providing "just-the-right" amount of quality and robustness. This is achieved, by taking into consideration block level interactions and ensuring that under any change of operating conditions, only the "less-crucial" computations, that contribute less to block/system output quality, are affected. The proposed approach applies unequal error protection to various blocks of a system-logic and memory-and spans multiple layers of design hierarchy-algorithm, architecture and circuit. The design methodology when applied to a multimedia subsystem shows large power benefits ( up to 69% improvement in power consumption) at reasonable image quality while tolerating errors introduced due to VOS, process variations, and channel noise.

Multi-level wordline driver for robust SRAM design in nano-scale CMOS technology

In this paper, a multi-level wordline driver scheme is presented to improve 6T-SRAM read and write stability. The proposed wordline driver generates a shaped pulse during the read mode and a boosted wordline during the write mode. During read, the shaped pulse is tuned at nominal voltage for a short period of time, whereas for the remaining access time, the wordline voltage is reduced to save the power consumption of the cell. This shaped wordline pulse results in improved read noise margin without any degradation in access time for small wordline load. The improvement is explained by examining the dynamic and nonlinear behavior of the SRAM cell. Furthermore, during the hold mode, for a short time (depending on the size of boosting capacitance), wordline voltage becomes negative and charges up to zero after a specific time that results in a lower leakage current compared to conventional SRAM. The proposed technique results in at least 2× improvement in read noise margin while it improves write margin by 3× for lower supply voltages than 0.7 V. The leakage power for the proposed SRAM is reduced by 2% while the total power is improved by 3% in the worst case scenario for an SRAM array. The main advantage of the proposed wordline driver is the improvement of dynamic noise margin with less than 2.5% penalty in area. TSMC 65 nm technology models are used for simulations.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees and with over 20% occlusion.

ECJ Fundamental Rights Jurisprudence between Member States' Prerogatives and Citizens' Autonomy

This chapter provides an analysis of the European Court of Justice's Fundamental Rights Jurisprudence, focused on the potential of Member States to maintain any positive regulatory role in supporting citizens' autonomy on the one hand, and on the impact of the Court's case law on citizens' opportunities to actually enjoy human rights within societies (substantive autonomy). It first sketches the notion of autonomy which is proposed as base of fundamental rights protection and promotion within a social reality characterized by not democratically legitimated dominance based on wealth and economic power. It proceeds to contextualize ECJ case law on fundamental rights. This section starts with a quantitative appetizer, which will formalize some assumptions and test them on a total of 150 cases before the European judiciary. The paper then offers a more conceptual recount around fundamental rights to equality and non-discrimination on the one hand and around fundamental rights of workers to actively shape employment and labor relations on the other hand. In conclusion some suggestions are made of how ECJ fundamental rights doctrine could develop more positively in order to moderate diverging interests of different parts of the citizenry in protecting fundamental rights.

Children, Adults, Autonomy and Well-Being

I explore the implications of a view – that children and adults enjoy a markedly different moral and political status, wherein the latter can and should be permitted to make choices as to how they lead their lives, whereas the former should not be permitted to make such choices – for how we think about the relationship between autonomy and welfare, and in particular, in consequence, for how we evaluate paternalism. I discuss the problem of drawing a line and the ‘threshold problem’, and consider how one might, as the UNCRC requires, give a weighted role to the views of the child on matters affecting its own interests.

A unique and robust single slice FPGA identification generator

In this paper, a new field-programmable gate array (FPGA) identification generator circuit is introduced based on physically unclonable function (PUF) technology. The new identification generator is able to convert flip-flop delay path variations to unique n-bit digital identifiers (IDs), while requiring only a single slice per ID bit by using 1-bit ID cells formed as hard-macros. An exemplary 128-bit identification generator is implemented on ten Xilinx Spartan-6 FPGA devices. Experimental results show an uniqueness of 48.52%, and reliability of 92.41% over a 25°C to 70°C temperature range and 10% fluctuation in supply voltage

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.