Old Puzzles

In this talk I first briefly review how far we have come in answering old questions about the most fundamental building blocks of matter. I begin with things we know, which is the Standard Model, and then talk about things we can guess, which is superstring theory. After this review I discuss a key point at which our understanding of superstring theory presently stops: the problem of supersymmetry breaking and the cosmological constant. I explain in which direction I imagine a way out. This way out predicts gravitinos and dilatons with masses of order milli-eV

The issue of Dark Energy in String Theory

Recent astrophysical observations, pertaining to either high-redshift supernovae or cosmic microwave background temperature fluctuations, as those measured recently by the WMAP satellite, provide us with data of unprecedented accuracy, pointing towards two (related) facts: (i) our Universe is accelerated at present, and (ii) more than 70 % of its energy content consists of an unknown substance, termed dark energy, which is believed responsible for its current acceleration. Both of these facts are a challenge to String theory. In this review I outline briefly the challenges, the problems and possible avenues for research towards a resolution of the Dark Energy issue in string theory.Comment: Based on Invited lecture at the ``Third Aegean Summer School on: The Invisible Universe: Dark matter and Dark energy'', Karfas, Chios Island (Greece) September 26-October 1 200

Two Moving-Angled 1-Branes with Electric Fields in a Partially Compact Spacetime

In this article we consider two $m1$-branes at angle in the presence of the background electric fields, in a partially compact spacetime. The branes have motions along a common direction that is perpendicular to both of them. Using the boundary state formalism, we calculate their interaction amplitude. Some special cases of this interaction will be studied in detail.Comment: 10 pages, no figure, Late

Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Fuzzy Gravitons From Uncertain Spacetime

The recently proposed remarkable mechanism explaining ``stringy exclusion principle" on an Anti de Sitter space is shown to be another beautiful manifestation of spacetime uncertainty principle in string theory as well as in M theory. Put in another way, once it is realized that the graviton of a given angular momentum is represented by a spherical brane, we deduce the maximal angular momentum directly from either the relation $\Delta t\Delta x^2>l_p^3$ in M theory or \Delta t\Delta x>\ap in string theory. We also show that the result of hep-th/0003075 is similar to results on D2-branes in SU(2) WZW model. Using the dual D2-brane representation of a membrane, we obtain the quantization condition for the size of the membrane.Comment: 10 pages, harvmac. v2: a ref. and a note added; v3: A remark and one more ref. adde

On string cosmology and the RG flow in 2d field theory

Time-dependent solutions of bosonic string theory resemble renormalisation group trajectories in the space of 2d field theories: they often interpolate between repulsive and attractive static solutions. It is shown that the attractive static solutions are those whose spatial sections are minima of |\bar c-25|, where \bar c is the `c-function'. The size of the domain of attraction of such a solution may be a measure of the probability of the corresponding string vacuum. Our discussion has also an implication for the RG flow in theories coupled to dynamical 2d gravity: the flow from models with c>25 to models with c<25 is forbidden.Comment: 16 pages, Latex, BUTP-94/7, Imperial/TP/93-94/29 (some footnotes and references added.

On the Inability of Markov Models to Capture Criticality in Human Mobility

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish a theoretical upper bound on the predictability of human mobility (expressed as a minimum error probability limit), based on temporally correlated entropy. Since its inception, this bound has been widely used and empirically validated using Markov chains. We show that recurrent-neural architectures can achieve significantly higher predictability, surpassing this widely used upper bound. In order to explain this anomaly, we shed light on several underlying assumptions in previous research works that has resulted in this bias. By evaluating the mobility predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-range correlations, bearing similarity to a power-law decay. This is in contrast to the initial assumption that human mobility follows an exponential decay. This assumption of exponential decay coupled with Lempel-Ziv compression in computing Fano's inequality has led to an inaccurate estimation of the predictability upper bound. We show that this approach inflates the entropy, consequently lowering the upper bound on human mobility predictability. We finally highlight that this approach tends to overlook long-range correlations in human mobility. This explains why recurrent-neural architectures that are designed to handle long-range structural correlations surpass the previously computed upper bound on mobility predictability