The many-body localization phase transition

We use exact diagonalization to explore the many-body localization transition in a random-field spin-1/2 chain. We examine the correlations within each many-body eigenstate, looking at all high-energy states and thus effectively working at infinite temperature. For weak random field the eigenstates are thermal, as expected in this nonlocalized, "ergodic" phase. For strong random field the eigenstates are localized, with only short-range entanglement. We roughly locate the localization transition and examine some of its finite-size scaling, finding that this quantum phase transition at nonzero temperature might be showing infinite-randomness scaling with a dynamic critical exponent $z\rightarrow\infty$.Comment: 7 pages, 8 figures. Extended version of arXiv:1003.2613v

Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation, emotion recognition, and facial key-point or landmark prediction using learned identity invariant representations. Established approaches to these problems can work well if sufficient examples and labels for a particular identity are available and factors of variation are highly controlled. However, labeled examples of facial expressions, emotions and key-points for new individuals are difficult and costly to obtain. In this paper we improve the ability of techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression. We show how probabilistic modeling of these sources of variation allows one to learn identity-invariant representations for expressions which can then be used to identity-normalize various procedures for facial expression analysis and animation control. We also show how to extend the widely used techniques of active appearance models and constrained local models through replacing the underlying point distribution models which are typically constructed using principal component analysis with identity-expression factorized representations. We present a wide variety of experiments in which we consistently improve performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS

Number of fermion generations from a novel Grand Unified model

Electroweak interactions based on a gauge group $\rm SU(3)_L \times U(1)_X$, coupled to the QCD gauge group $\rm SU(3)_c$, can predict the number of generations to be multiples of three. We first try to unify these models within SU(N) groups, using antisymmetric tensor representations only. After examining why these attempts fail, we continue to search for an SU(N) GUT that can explain the number of fermion generations. We show that such a model can be found for $N=9$, with fermions in antisymmetric rank-1 and rank-3 representations only, and examine the constraints on various masses in the model coming from the requirement of unification.Comment: 17 pages, 1 eps figur

Learning to Crawl

Web crawling is the problem of keeping a cache of webpages fresh, i.e., having the most recent copy available when a page is requested. This problem is usually coupled with the natural restriction that the bandwidth available to the web crawler is limited. The corresponding optimization problem was solved optimally by Azar et al. [2018] under the assumption that, for each webpage, both the elapsed time between two changes and the elapsed time between two requests follow a Poisson distribution with known parameters. In this paper, we study the same control problem but under the assumption that the change rates are unknown a priori, and thus we need to estimate them in an online fashion using only partial observations (i.e., single-bit signals indicating whether the page has changed since the last refresh). As a point of departure, we characterise the conditions under which one can solve the problem with such partial observability. Next, we propose a practical estimator and compute confidence intervals for it in terms of the elapsed time between the observations. Finally, we show that the explore-and-commit algorithm achieves an $\mathcal{O}(\sqrt{T})$ regret with a carefully chosen exploration horizon. Our simulation study shows that our online policy scales well and achieves close to optimal performance for a wide range of the parameters.Comment: Published at AAAI 202

Employing endogenous access pricing to enhance incentives for efficient upstream operation

Endogenous access pricing (ENAP) is an alternative to the more traditional form of access pricing that sets the access price to reflect the regulator’s estimate of the supplier’s average cost of providing access. Under ENAP, the access price reflects the supplier’s actual average cost of providing access, which varies with realized industry output. We show that in addition to eliminating the need to estimate industry output accurately and avoiding a divergence between upstream revenues and costs, ENAP can enhance the incentive of a vertically integrated producer to minimize its upstream operating cost

Energy transport in disordered classical spin chains

We present a numerical study of the diffusion of energy at high temperature in strongly disordered chains of interacting classical spins evolving deterministically. We find that quenched randomness strongly suppresses transport, with the diffusion constant becoming reduced by several orders of magnitude upon the introduction of moderate disorder. We have also looked for but not found signs of a classical many-body localization transition at any nonzero strength of the spin-spin interactions