nils-herrmann/clean_scirepeval huggingface dataset:
New memetic self adaptive firefly algorithm for continuous optimisation

The firefly algorithm is a recent nature inspired algorithm that is receiving increasing attention from the scientific community during the last few years. One of its most promising variants is given by the memetic self adaptive firefly algorithm MSA FFA, recently introduced to solve combinatorial problems. In this paper we propose a modification of the original MSA FFA for continuous optimisation problems. The most important features of our method are: the problem dependent selection of control parameters for self adaptation, a simple population model providing an adequate trade off between exploration and exploitation, and the use of an adaptive size Luus Jaakola random local search. This new method is applied to solve a very difficult real world continuous optimisation problem arising in geometric modelling and manufacturing. The paper also provides the first reliable, standardised benchmark for this optimisation problem. This benchmark is used for a comparative analysis of our method with respect to some of the most popular nature inspired algorithms. Our results show that the proposed method outperforms previous approaches including the standard firefly algorithm for most of the instances in the benchmark.
Speaker and language factorization in DNN based TTS synthesis
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

We have successfully proposed to use multi speaker modelling in DNN based TTS synthesis for improved voice quality with limited available data from a speaker. In this paper, we propose a new speaker and language factorized DNN, where speaker specific layers are used for multi speaker modelling, and shared layers and language specific layers are employed for multi language, linguistic feature transformation. Experimental results on a speech corpus of multiple speakers in both Mandarin and English show that the proposed factorized DNN can not only achieve a similar voice quality as that of a multi speaker DNN, but also perform polyglot synthesis with a monolingual speaker's voice.
Unsupervised speaker adaptation for DNN based TTS synthesis
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Multi speaker TTS trained with a general DNN has outperformed individually modelled baseline [1] Multi speaker DNN takes advantages of larger amount of training data from multiple speakers to find robust transformations in the hidden layers and covers more speaker variability in the output regression layer. In this paper, we propose a new approach to unsupervised speaker adaptation with multi speaker DNN. It takes advantage of shared hidden transformation to search for the labels of unlabelled acoustic frames and the found labels are used for speaker adaption. Experimental results show that the new approach of unsupervised adaptation can achieve comparable performance with supervised adaptation both objectively and subjectively. We further extend it to cross lingual adaptation. It can remove non native accent and improve the naturalness while keep the same speaker's characteristics.
On Hamiltonian Colorings of Block Graphs
Venue: WALCOM

A hamiltonian coloring c of a graph G of order p is an assignment of colors to the vertices of G such that D(u, v) |c(u) c(v) \ge (p 1\ for every two distinct vertices u and v of G, where D(u, v) denotes the detour distance between u and v. The value hc(c) of a hamiltonian coloring c is the maximum color assigned to a vertex of G. The hamiltonian chromatic number, denoted by hc(G) is the min{hc(c) taken over all hamiltonian coloring c of G. In this paper, we present a lower bound for the hamiltonian chromatic number of block graphs and give a sufficient condition to achieve the lower bound. We characterize symmetric block graphs achieving this lower bound. We present two algorithms for optimal hamiltonian coloring of symmetric block graphs.
Private firm pricing and propensity to go public: evidence from mutual funds holdings

The purpose of this study is to focus on mutual funds' valuations of their US private firm holdings. According to extant academic literature, mutual funds' boards of directors should assign prices to their private firm stocks based on their own determination of fair value.  This study investigates fluctuations in valuations of mutual funds holdings of private firms, and whether or not mutual funds managers are able to pick privately held firms that eventually undergo an initial public offer.  The study shows that private firm common stocks' prices fluctuate much more than preferred stocks' however, as expected, preferred stock is the most selected security type. This study also investigates these firms' propensity to undergo an initial public offering (IPO) Results show that mutual funds allocated most of their capital to US private sector firms that underwent an initial public offering. Logit model results reveal that fund managers are able to pick privately held firms that will go public.  Due to data limitations, the authors' analysis does not control for venture capital ownership; an issue the authors plan to address in the future.  Though, research in this area may exist, the authors have not found academic literature related to holdings of private firms by mutual funds, pricing by funds' boards of directors or the motives for such investments.
An Industrial Survey of Safety Evidence Change Impact Analysis Practice
Venue: IEEE Transactions on Software Engineering

In many application domains, critical systems must comply with safety standards. This involves gathering safety evidence in the form of artefacts such as safety analyses, system specifications, and testing results. These artefacts can evolve during a system's lifecycle, creating a need for change impact analysis to guarantee that system safety and compliance are not jeopardised.  We aim to provide new insights into how safety evidence change impact analysis is addressed in practice. The knowledge about this activity is limited despite the extensive research that has been conducted on change impact analysis and on safety evidence management.  We conducted an industrial survey on the circumstances under which safety evidence change impact analysis is addressed, the tool support used, and the challenges faced.  We obtained 97 valid responses representing 16 application domains, 28 countries, and 47 safety standards. The respondents had most often performed safety evidence change impact analysis during system development, from system specifications, and fully manually. No commercial change impact analysis tool was reported as used for all artefact types and insufficient tool support was the most frequent challenge.  The results suggest that the different artefact types used as safety evidence co evolve. In addition, the evolution of safety cases should probably be better managed, the level of automation in safety evidence change impact analysis is low, and the state of the practice can benefit from over 20 improvement areas.
Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis
Venue: 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

Software change impact analysis aims to find artifacts potentially affected by a change. Typical approaches apply language specific static or dynamic dependence analysis, and are thus restricted to homogeneous systems. This restriction is a major drawback given today's increasingly heterogeneous software. Evolutionary coupling has been proposed as a language agnostic alternative that mines relations between source code entities from the system's change history. Unfortunately, existing evolutionary coupling based techniques fall short. For example, using Singular Value Decomposition (SVD) quickly becomes computationally expensive. An efficient alternative applies targeted association rule mining, but the most widely known approach (ROSE) has restricted applicability: experiments on two large industrial systems, and four large open source systems, show that ROSE can only identify dependencies about 25% of the time. To overcome this limitation, we introduce TARMAQ, a new algorithm for mining evolutionary coupling. Empirically evaluated on the same six systems, TARMAQ performs consistently better than ROSE and SVD, is applicable 100% of the time, and runs orders of magnitude faster than SVD. We conclude that the proposed algorithm is a significant step forward towards achieving robust change impact analysis for heterogeneous systems.
Towards autonomous 3D exploration using surface frontiers
Venue: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

Exploring environments to generate 3D maps is a core component of any autonomous robot expected to operate in search and rescue or security related missions. Considering that information about surface voxels in a 3D grid map is sufficient for many tasks, following a mapping mission, such as motion planning for searching or rescue, this paper presents an exploration strategy guided by surface frontiers rather than free space frontiers to actively map the object surfaces in the environment. Surface frontiers in a 3D grid map are surface boundary voxels neighboring unmapped space. Detecting surface frontiers from a 3D occupancy grid is discussed in detail and a strategy to sample view configurations using the detected surface frontiers for exploration is provided. Exploration experiments conducted in both simulated and real indoor environments validate that surface frontiers are a valid and an efficient alternative for freespace frontiers for autonomous 3D exploration.
Geometrically consistent plane extraction for dense indoor 3D maps segmentation
Venue: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Modern SLAM systems with a depth sensor are able to reliably reconstruct dense 3D geometric maps of indoor scenes. Representing these maps in terms of meaningful entities is a step towards building semantic maps for autonomous robots. One approach is to segment the 3D maps into semantic objects using Conditional Random Fields (CRF) which requires large 3D ground truth datasets to train the classification model. Additionally, the CRF inference is often computationally expensive. In this paper, we present an unsupervised geometric based approach for the segmentation of 3D point clouds into objects and meaningful scene structures. We approximate an input point cloud by an adjacency graph over surface patches, whose edges are then classified as being either on or off. We devise an effective classifier which utilises both global planar surfaces and local surface convexities for edge classification. More importantly, we propose a novel global plane extraction algorithm for robustly discovering the underlying planes in the scene. Our algorithm is able to enforce the extracted planes to be mutually orthogonal or parallel which conforms usually with human made indoor environments. We reconstruct 654 3D indoor scenes from NYUv2 sequences to validate the efficiency and effectiveness of our segmentation method.
Comparison of Guidelines on Non Muscle Invasive Bladder Cancer (EAU, CUA, AUA, NCCN, NICE)
Venue: Bladder cancer

Non muscle invasive bladder cancer (NMIBC) represents a considerably diverse patient group and the management of this complex disease is debatable. A number of panels from Europe and North America have convened on the topic and recently released guideline documents.  The purpose was to compare and contrast the NMIBC guideline recommendations from the EAU (Europe) CUA (Canada) NCCN (United States) AUA (United States) and NICE (United Kingdom)  All unabridged guideline documents were reviewed by the authors and comparisons were completed according to major topics in NMIBC.  Despite a paucity of high level evidence regarding the majority of management topics in NMIBC, there was general agreement among the various guideline panels. Differences mainly centered on the categories of evidence synthesized and grades of recommendations. Each document offers a unique presentation of the available literature and guideline recommendation.  The guidelines for NMIBC from the EAU, CUA, AUA, NCCN, and NICE provide considerable consensus regarding the management of this often difficult disease. Clinicians are encouraged to familiarize themselves with all of the guidelines in order to determine which style of presentation would be most useful to their current practice.
Non alcoholic fatty liver disease (NAFLD) summary of NICE guidance
Venue: British Medical Journal

What you need to know There has been a lack of national guidance and care pathways for primary care on when to offer testing for NAFLD. Investigation and referral of suspected NAFLD vary widely. There is currently no licensed treatment for NAFLD, and most people are managed by their general practitioner with a focus on lifestyle advice such as weight loss. Non alcoholic fatty liver disease (NAFLD) is a spectrum of disease from fatty liver to non alcoholic steatohepatitis, fibrosis, and cirrhosis. At the least advanced end of the spectrum, non alcoholic fatty liver (NAFL) is an excess of fat in the liver (steatosis) present in 20 30% of the general population and is largely asymptomatic.1 2 3 4 Currently, diagnosis of NAFLD is most commonly made through incidental findings, such as ultrasonography for investigation of persistently abnormal liver function tests, when other suspected causes have been ruled out. In around 5 6% of patients with NAFL only the condition progresses to non alcoholic steatohepatitis (NASH) fibrosis, or cirrhosis (see fig 1|.5 In this small group there is a risk of death from liver failure or hepatocellular carcinoma, or needing a liver  The number of hospital admissions for liver damage at the more severe end of the NAFLD spectrum, fibrosis and cirrhosis, are increasing every year.6 Fig 1 Spectrum of non alcoholic fatty
(Non adiabatic) string creation on nice slices in Schwarzschild black holes

A bstractNice slices have played a pivotal role in the discussion of the black hole information paradox as they avoid regions of strong spacetime curvature and yet smoothly cut through the infalling matter and the outgoing Hawking radiation, thus, justifying the use of low energy field theory. To avoid information loss it has been argued recently, however, that local effective field theory has to break down at the horizon. To assess the extent of this breakdown in a UV complete framework we study string theoretic effects on nice slices in Schwarzschild black holes. Our purpose is two fold. First, we use nice slices to address various open questions and caveats of  where it was argued that boost enhanced non adiabatic string theoretic effects at the horizon could provide a dynamical mechanism for the firewall. Second, we identify two non adiabatic effects on nice slices in Schwarzschild black holes: pair production of open strings near the horizon enhanced by the presence of the infinite tower of highly excited string states and a late time non adiabatic effect intrinsic to nice slices.
Probing the role of chemical enhancers in facilitating drug release from patches: Mechanistic insights based on FT IR spectroscopy, molecular modeling and thermal analysis.
Venue: Journal of controlled release official journal of the Controlled Release Society

In patches, a drug must release from patches prior to its percutaneous absorption. Chemical enhancers have been used for several decades, but their roles in drug release from patches are poorly understood. In this work, the roles of chemical enhancers in bisoprolol tartrate (BSP T) release from patches were probed in vitro and in vivo. More importantly, an innovative mechanism insight of chemical enhancers in drug release process was provided at molecular level. FT IR spectroscopy and molecular modeling were employed to investigate the influence of chemical enhancers on drug adhesive interaction. The results showed chemical enhancers like Span 80, which had a strong ability forming hydrogen bonds, could decrease drug adhesive interaction leading to the release of drug from adhesive of patches. Thermal analysis was conducted to research the influence of chemical enhancers on the thermodynamic properties of patch system. It showed that chemical enhancers promoted the formation of free volume of adhesive, which facilitated drug release process. By contrast, the influence on the thermodynamic properties of BSP T was less effective in influencing BSP T release process. In conclusion, chemical enhancers played an important role in facilitating BSP T release from the adhesive DURO TAK(r) 87 2287 of patches by decreasing drug adhesive interaction and promoting the formation of free volume of adhesive. This work may be an important step in understanding the important roles of chemical enhancers in drug release process.
Effect of corn grain variety on the bioethanol production efficiency

The impact of corn grain variety on the ethanol production effectiveness was determined. The screening of 258 various corn samples were done. The endosperm of recognized 258 types of corn was semi flint as a consequence of crossing between female parent with dent type seeds and male parent with flint type seeds. The tested cultivars fall into two groups, ie. 133 belong to single cross (SC) and 125 belong to three way cross (TC) hybrids. These hybrids of corn were created with four male parents: 96 with S61328, 61 with S80660A, 20 with S07787A and 8 with S09347. The corn samples were mashed and fermented with distillery yeast Saccharomyces cerevisiae which produced ethanol from corn mashes yielding maximum 81.33% of the theoretical value. It was noted that the corn samples with higher starch content had lower efficiency of starch saccharification 0.35, p 0.05) This fact generates negative correlation between variables ethanol (L/100 kg starch) and starch content 0.43, p 0.05) The highest level of reducing sugars in grains led to higher productivity of ethanol. The comparisons in groups generated with multivariate exploratory techniques (cluster analysis, k means clustering, PCA) showed that it is possible to distinguish a statistically different cluster with the highest ethanol yield (35.6 L of ethanol per 100 kg grains) Moreover, the PCA analysis showed that hybrids of corn with S80660A male parent have the biggest impact on efficiency of ethanol. The presented results clearly prove that not only starch content in corn grains determine the ethanol productivity.
Emerging trends in social media and plastic surgery.
Venue: Annals of translational medicine

Social media has increasingly changed the landscape of medicine and surgery and is rapidly expanding its influence in most peoples' lives. The average person spends nearly 2 hours per day using social media, consuming information about everything from family updates to entertainment news to presidential elections. The concentration of consumers on social media platforms has resulted in direct medicine and medical products marketing to consumers. Similarly, social media is increasingly becoming a platform for interaction between physicians and potential patients. Some physicians have taken this opportunity to better educate patients, while allowing patients to learn more about their surgeons online. These tools can increase internet traffic online to bonafide internet sites, as well as bolster marketing for many hospitals, hospital systems, and individual doctors. It can also serve to increase knowledge about procedures and conditions through direct outreach to patients. Social media is a powerful tool which needs to be utilized wisely to avoid pitfalls.
A multi objective self adaptive differential evolution algorithm for conceptual high rise building design
Venue: 2016 IEEE Congress on Evolutionary Computation (CEC)

This paper presents a multi objective self adaptive differential evolution algorithm to solve the form finding problem of high rise building design in the conceptual phase. The aim of the research is to reach suitable high rise design alternatives for hard and soft objectives, which are construction cost per square meter, structural displacement, and visual perception of the spaces from the inside out subject to several constraints that are related with both high rise construction regulations, and profitability of the spaces. We formulate the problem as a multi objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem, we developed two different optimization algorithms, namely, a Non Dominated Sorting Genetic Algorithm II (NSGA II) and a Self Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA II algorithm.
Design of a piezoelectric harvester fixed under the roof of a high rise building

A theoretical model for a piezoelectric harvester device is developed by an iteration method to determine the maximum energy harvested from vibration of high rise buildings. The piezoelectric harvester device is made of a cantilever fixed under the roof of a building and two groups of series piezoelectric generators connected by a shared shaft. The shaft is driven by a linking rod hinged on a proof mass on the tip of the cantilever. The influences of some practical considerations, such as the length of the cantilever, the radius of the proof mass, and the wall thickness of the main structure, on the root mean square (RMS) of the generated electric power and the energy harvesting efficiency of the piezoelectric harvester device are discussed. The research on the theoretical model provides a new method for an efficient and practical energy harvesting from high rise buildings by a piezoelectric harvester device fixed under their roofs.
Human Factors in Green Office Building Design: The Impact of Workplace Green Features on Health Perceptions in High Rise High Density Asian Cities

There is a growing concern about human factors in green building, which is imperative in high rise high density urban environments. This paper describes our attempts to explore the influence of workplace green features (such as green certification, ventilation mode, and building morphology) on health perceptions (personal sensation, sensorial assumptions, healing performance) based on a survey in Hong Kong and Singapore. The results validated the relationship between green features and health perceptions in the workplace environment. Remarkably, participants from the air conditioned offices revealed significant higher concerns about health issues than those participants from the mixed ventilated offices. The mixed ventilation design performs as a bridge to connect the indoor environment and outdoor space, which enables people to have contact with nature. Additionally, the preferred building morphology of the workplace is the pattern of a building complex instead of a single building. The complex form integrates the configuration of courtyards, podium gardens, green terrace, public plaza, and other types of open spaces with the building clusters, which contributes to better health perceptions. This research contributes to the rationalization and optimization of passive climate adaptive design strategies for green buildings in high density tropical or subtropical cities.