Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.     

RL \subseteq SC

We show that any randomized logspace algorithm (running in polynomial time with bounded two-sided error) can be simulated deterministically in polynomial time andO(log² n) space. This puts RL in SC, Steve's Class In particular, we get a polynomial time,O(log² n) space algorithm for thest-connectivity problem on undirected graphs.Subject classifications. 68Q10, 68Q15, 68Q25.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

A theoretical framework and empirical examination of the effects of foreign and translated interface language

Given the increased globalization and popularization of computer applications, translating a system's human interface into the local language has become a major consideration for software vendors and distributors. In this paper, we suggest a theoretical framework for the study of user interface translation. The framework includes recognizing vendors' and users' costs of, and benefits from, software translation. An experiment was conducted, based on this framework, to test user performance and preferences regarding interface translations. The experiment manipulated the translation of two interface components: documentation language and manipulation language. The results indicate that users are sensitive to different combinations of interface translation in a way that is commensurate with the instruction-following process (Terwilliger and Polson 1997). Users performed best when a fully translated interface was used and worst when only the manipulation language was translated. Users' preferences were in line with their performance, indicating that a cost benefit approach can serve as a promising starting point to the study of interface translation.     

Competitive analysis of incentive compatible on-line auctions

This paper studies auctions in a setting where the different bidders arrive at different times and the auction mechanism is required to make decisions about each bid as it is received. Such settings occur in computerized auctions of computational resources as well as in other settings. We call such auctions, on-line auctions.

We first characterize exactly on-line auctions that are incentive compatible, i.e. where rational bidders are always motivated to bid their true valuation. We then embark on a competitive worst-case analysis of incentive compatible on-line auctions. We obtain several results, the cleanest of which is an incentive compatible on-line auction for a large number of identical items. This auction has an optimal competitive ratio, both in terms of seller's revenue and in terms of the total social efficiency obtained.     

Superluminal tunnelling times as weak values

Abstract

We consider the tunnelling particle as a pre- and post-selected system and prove that the tunnelling time is the expectation value of the position of a ‘clock’ degree of freedom weakly coupled to it. Such a value, called a ‘weak value’, typically falls outside the eigenvalue spectrum of the operator. The appearance of unusual weak values has been associated with a unique interference structure called ‘superoscillations’ (band-limited functions which on a finite interval, approximate functions with spectra well outside their band). It is proposed that superoscillations play an important role in the interferences which give rise to superluminal effects. To demonstrate that, we consider a certain simple tunnelling barrier which allows a wave packet to travel in zero time and negligible distortion, a distance arbitrarily longer than the width of the wave packet. The peak is shown to result from a superoscillatory superposition at the tail. Similar reasoning applies to the dwell time. For this system, both the Wigner time (related to the group velocity) and a clock time correspond to superluminal velocities.     

What role for microgeneration in a shift to a low carbon domestic energy sector in the UK?

Domestic energy use accounts for more than a quarter of CO₂ emissions in the UK. Traditional approaches to energy reduction look at direct emissions savings, and recommend insulation and efficiency as more cost-effective than microgeneration. However, microgeneration has indirect, ‘soft’ benefits and could play a significant role in emissions reduction. Current uptake of microgeneration in the UK is low, with various barriers—economic, technical, cultural, behavioural and institutional—both to uptake and to maximising energy and emissions savings once installed. Subsidies and spreading information alone do not guarantee maximising uptake, and even if successful, this is not enough to maximise savings. The industry focuses on maximising sales, with no incentives to ensure best installations and use; householders do not have access to the best information, and user behaviour does not maximise energy and emission savings. This is related to a broader state of socio-technical ‘lock-in’ in domestic energy use; there’s a lack of connection between personal behaviour and energy consumption, let alone global climate change. This suggests that a major cultural–behavioural shift is needed to reduce energy/emissions in the home. Transition theory and strategic niche management provide insights into possible systemic change and a suitable framework for future policies, such as supporting a variety of radically innovative niches, both technological and social. Microgeneration, properly employed, has the potential to play a part in such a transition by increasing awareness and energy literacy and empowering people to seriously engage in energy debates as producers, as well as consumers, of energy. This deeper understanding and heightened responsibility are crucial in a shift toward bottom-up emission-reducing behaviour change and better acceptance of top-down energy-saving policy measures, as part of a new domestic energy paradigm. The implications for policy are that, as well as supporting the technologies, it needs to support existing niches and to develop new niche experiments. Policy needs to consider how to promote empowerment and responsibility and support or even develop new energy sector models; this will involve a range of stakeholders and multiple governance levels, not just national incentive schemes.     

Whodunnit – Searching for the most important feature types signalling emotion-related user states in speech

In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states – confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of ‘most important’ features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics.