Power laws, Pareto distributions and Zipf's law

When the probability of measuring a particular value of some quantity varies inversely as a power of that value, the quantity is said to follow a power law, also known variously as Zipf's law or the Pareto distribution. Power laws appear widely in physics, biology, earth and planetary sciences, economics and finance, computer science, demography and the social sciences. For instance, the distributions of the sizes of cities, earthquakes, solar flares, moon craters, wars and people's personal fortunes all appear to follow power laws. The origin of power-law behaviour has been a topic of debate in the scientific community for more than a century. Here we review some of the empirical evidence for the existence of power-law forms and the theories proposed to explain them.Comment: 28 pages, 16 figures, minor corrections and additions in this versio

Deep Random based Key Exchange protocol resisting unlimited MITM

We present a protocol enabling two legitimate partners sharing an initial secret to mutually authenticate and to exchange an encryption session key. The opponent is an active Man In The Middle (MITM) with unlimited computation and storage capacities. The resistance to unlimited MITM is obtained through the combined use of Deep Random secrecy, formerly introduced and proved as unconditionally secure against passive opponent for key exchange, and universal hashing techniques. We prove the resistance to MITM interception attacks, and show that (i) upon successful completion, the protocol leaks no residual information about the current value of the shared secret to the opponent, and (ii) that any unsuccessful completion is detectable by the legitimate partners. We also discuss implementation techniques.Comment: 14 pages. V2: Updated reminder in the formalism of Deep Random assumption. arXiv admin note: text overlap with arXiv:1611.01683, arXiv:1507.0825

Shannon entropies of atomic structure factors, off-diagonal order and electron correlation

Shannon entropies of one- and two-electron atomic structure factors in the position and momentum representations are used to examine the behavior of the off-diagonal elements of density matrices with respect to the uncertainty principle and to analyze the effects of electron correlation on off-diagonal order. We show that electron correlation induces off-diagonal order in position space which is characterized by larger entropic values. Electron correlation in momentum space is characterized by smaller entropic values as information is forced into regions closer to the diagonal. Related off-diagonal correlation functions are also discussed

Fast Hands-free Writing by Gaze Direction

We describe a method for text entry based on inverse arithmetic coding that relies on gaze direction and which is faster and more accurate than using an on-screen keyboard. These benefits are derived from two innovations: the writing task is matched to the capabilities of the eye, and a language model is used to make predictable words and phrases easier to write.Comment: 3 pages. Final versio

Increasing skeletal muscle carnitine availability does not alter the adaptations to high-intensity interval training

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Accepted manuscript online: 27 March 2017Increasing skeletal muscle carnitine availability alters muscle metabolism during steady-state exercise in healthy humans. We investigated whether elevating muscle carnitine, and thereby the acetyl-group buffering capacity, altered the metabolic and physiological adaptations to 24 weeks of high-intensity interval training (HIIT) at 100% maximal exercise capacity (Wattmax ). Twenty-one healthy male volunteers (age 23±2 years; BMI 24.2±1.1 kg/m(2) ) performed 2x3 minute bouts of cycling exercise at 100% Wattmax , separated by five minutes rest. Fourteen volunteers repeated this protocol following 24 weeks of HIIT and twice-daily consumption of 80g carbohydrate (CON) or 3g L-carnitine+carbohydrate (CARN). Before HIIT, muscle phosphocreatine (PCr) degradation (P<0.0001), glycogenolysis (P<0.0005), PDC activation (P<0.05), and acetylcarnitine (P<0.005) were 2.3, 2.1, 1.5 and 1.5-fold greater, respectively, in exercise bout two compared to bout one, whilst lactate accumulation tended (P<0.07) to be 1.5-fold greater. Following HIIT, muscle free carnitine was 30% greater in CARN vs CON at rest and remained 40% elevated prior to the start of bout two (P<0.05). Following bout two, free carnitine content, PCr degradation, glycogenolysis, lactate accumulation, and PDC activation were all similar between CON and CARN, albeit markedly lower than before HIIT. VO2max , Wattmax and work-output were similarly increased in CON and CARN, by 9, 15 and 23% (P<0.001). In summary, increased reliance on non-mitochondrial ATP resynthesis during a second bout of intense exercise is accompanied by increased carnitine acetylation. Augmenting muscle carnitine during 24 weeks of HIIT did not alter this, nor enhance muscle metabolic adaptations or performance gains beyond those with HIIT alone. This article is protected by copyright. All rights reserved.This research was supported by a BBSRC PhD studentship award for CS

A note on entropic uncertainty relations of position and momentum

We consider two entropic uncertainty relations of position and momentum recently discussed in literature. By a suitable rescaling of one of them, we obtain a smooth interpolation of both for high-resolution and low-resolution measurements respectively. Because our interpolation has never been mentioned in literature before, we propose it as a candidate for an improved entropic uncertainty relation of position and momentum. Up to now, the author has neither been able to falsify nor prove the new inequality. In our opinion it is a challenge to do either one.Comment: 2 pages, 2 figures, 2 references adde

Hybrid MM/SVM structural sensors for stochastic sequential data

In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5' and 3') splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation). We evaluate a variety of kernels and kernel parameters in the classification effort. We present results of the algorithms for splice-site datasets consisting of sequences from a variety of species for comparison

The effect of time constraint on anticipation, decision making, and option generation in complex and dynamic environments

Researchers interested in performance in complex and dynamic situations have focused on how individuals predict their opponent(s) potential courses of action (i.e., during assessment) and generate potential options about how to respond (i.e., during intervention). When generating predictive options, previous research supports the use of cognitive mechanisms that are consistent with long-term working memory (LTWM) theory (Ericsson and Kintsch in Phychol Rev 102(2):211–245, 1995; Ward et al. in J Cogn Eng Decis Mak 7:231–254, 2013). However, when generating options about how to respond, the extant research supports the use of the take-the-first (TTF) heuristic (Johnson and Raab in Organ Behav Hum Decis Process 91:215–229, 2003). While these models provide possible explanations about how options are generated in situ, often under time pressure, few researchers have tested the claims of these models experimentally by explicitly manipulating time pressure. The current research investigates the effect of time constraint on option-generation behavior during the assessment and intervention phases of decision making by employing a modified version of an established option-generation task in soccer. The results provide additional support for the use of LTWM mechanisms during assessment across both time conditions. During the intervention phase, option-generation behavior appeared consistent with TTF, but only in the non-time-constrained condition. Counter to our expectations, the implementation of time constraint resulted in a shift toward the use of LTWM-type mechanisms during the intervention phase. Modifications to the cognitive-process level descriptions of decision making during intervention are proposed, and implications for training during both phases of decision making are discussed

Active Class Incremental Learning for Imbalanced Datasets

Incremental Learning (IL) allows AI systems to adapt to streamed data. Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1) new data are assumed to be readily annotated when streamed and (2) tests are run with balanced datasets while most real-life datasets are actually imbalanced. These hypotheses are discarded and the resulting challenges are tackled with a combination of active and imbalanced learning. We introduce sample acquisition functions which tackle imbalance and are compatible with IL constraints. We also consider IL as an imbalanced learning problem instead of the established usage of knowledge distillation against catastrophic forgetting. Here, imbalance effects are reduced during inference through class prediction scaling. Evaluation is done with four visual datasets and compares existing and proposed sample acquisition functions. Results indicate that the proposed contributions have a positive effect and reduce the gap between active and standard IL performance.Comment: Accepted in IPCV workshop from ECCV202