Miracle-dz/UniFAR-FTUs huggingface dataset:
Information geometric superactivation of classical zero-error capacity of quantum channels

This paper introduces a fundamentally new method of finding the conditions for the superactivation of the zero-error capacity of quantum channels. The zero-error capacity of the quantum channel describes the amount of information which can be transmitted perfectly through a noisy quantum channel. The superactivation of the zero-error capacity of quantum channels makes it possible to use two quantum channels, each with zero zero-error capacity, with a positive joint zero-error capacity. Currently we have no theoretical background for describing all possible combinations of superactive quantum channels, hence there should be many other possible combinations. We give an algorithmic solution to the problem. To analyze the superactivation of the zero-error capacity, we introduce a new geometrical representation, called the quantum superball. Our method can be the first efficient algorithmic solution to discover the still unknown combinations to determine the superactivation of the zero-error capacity of quantum channels, without the extremely high computational costs.
Optimization of Simultaneous Wireless Information and Power Transfer in Cloud Radio Access Networks

In this paper, we focus on optimization of simultaneous wireless information and power transfer (SWIPT) in uplink cloud radio access network (C-RAN). The key idea is to design transceiver architecture and implement the SWIPT strategy at remote radio heads (RRHs). The minimum mean-square-error (MMSE) is considered as the performance metrics with transmit power constraint and energy constraint. Toward the goal of MMSE, the precoders and the detectors are iteratively updated in proposed scheme. However, because of the energy constraint, the precoding optimization problem with a specified detection matrix become non-convex. To solve this problem, a new precoding design based on the Lagrangian dual relaxation (LDR) is developed. Meanwhile, conditions to make sure the optimization problem has its physical interpretation and LDR become convex are determined. Finally, simulation results demonstrate that the proposed transceiver design can significantly improve the system performance.
ANCFIS: A Neurofuzzy Architecture Employing Complex Fuzzy Sets

Complex fuzzy sets (CFSs) are an extension of type-1 fuzzy sets in which the membership of an object to the set is a value from the unit disc of the complex plane. Although there has been considerable progress made in determining the properties of CFSs and complex fuzzy logic, there has yet to be any practical application of this concept. We present the adaptive neurocomplex-fuzzy-inferential system (ANCFIS), which is the first neurofuzzy system architecture to implement complex fuzzy rules (and, in particular, the signature property of rule interference). We have applied this neurofuzzy system to the domain of time-series forecasting, which is an important machine-learning problem. We find that ANCFIS performs well in one synthetic and five real-world forecasting problems and is also very parsimonious. Experimental comparisons show that ANCFIS is comparable with existing approaches on our five datasets. This work demonstrates the utility of complex fuzzy logic on real-world problems.
Selecting the State-Representation in Reinforcement Learning

The problem of selecting the right state-representation in a reinforcement learning problem is considered. Several models (functions mapping past observations to a finite set) of the observations are given, and it is known that for at least one of these models the resulting state dynamics are indeed Markovian. Without knowing neither which of the models is the correct one, nor what are the probabilistic characteristics of the resulting MDP, it is required to obtain as much reward as the optimal policy for the correct model (or for the best of the correct models, if there are several). We propose an algorithm that achieves that, with a regret of order T2/3 where T is the horizon time.
Zombies Walk Among Us: Cross-Platform Data Mining for Event Monitoring

Social networks such as Facebook, Orkut and so on, are repositories of information that are viewed as users' opinions. However, data mining across multiple social websites can reveal valuable factual information for both monitoring and reconstructing events. Crowd sourcing can be used on these sites to monitor `flash mob' group behaviors, loosely or formally planned activities, such as `zombie walks'. In certain contexts, these walks are `marketed' to social site users in order to promote charitable or social engagements, in others, analysis suggests that participation is a form of political engagement. We use multi-platform information extraction to build an atlas of geographic and demographic events, which leads us to compelling, yet imperfect, understanding of why events are successful and regionally relevant factors that encourage people to pick up behaviors or participate in movements. More generally, this case study confirms that `big-data'-led analysis of this kind is reasonably straightforward and rewarding, as well as providing a useful basis for Bayesian reasoning: the use of available evidence to evaluate propositions about this type of events.
Modelling of compound semiconductors: analytical bond-order potential for gallium, nitrogen and gallium nitride

An analytical bond-order potential for GaN is presented that describes a wide range of structural properties of GaN as well as bonding and structure of the pure constituents. For the systematic fit of the potential parameters reference data are taken from total-energy calculations within the density functional theory if not available from experiments. Although long-range interactions are not explicitly included in the potential, the present model provides a good fit to different structural geometries including defects and high-pressure phases of GaN.
Predicting Early Academic Achievement: An Investigation of the Contribution of Executive Function

This study investigated the important question of whether pupils’ executive functions (EF) predict early academic achievement. Current conceptualizations suggest that developmental trends in EF can be measured in young children and that EF may play an important role in predicting academic achievement and school readiness. To date, however, there is little empirical support for this assertion. This study explored EF skills of 3to 5 year-olds using the Dimensional Change Card Sort Task (DCCS). The first objective was to determine if EF indeed predicts math, reading, and writing achievement in 3to 5-year-olds. The second objective was to determine if EF’s prediction of academics occurs independent of the contribution made by general ability (e.g., Battelle motor and language subdomains). The third objective was to determine if the contribution of EF remains uniform across the age span. Consequently, existing data from 969 participants between 54 to 71 months was used. This consisted of scores on the DCCS as well as the Phonological Awareness Literacy Screening (PALS) to measure early reading and writing skills, the Test of Early Math Ability -Third Edition (TEMA-3) to measure early math skills, and sections of the Battelle Developmental Inventory 2 Edition (BDI2) to measure general development. A positive relationship between EF and early math, reading, and writing skills was found. Also EF, as measured by the DCCS, contributed a significant portion of variance in early math, reading, and writing skills after accounting for general development, age, and socioeconomic status. Finally, the contribution of EF to early reading and writing skills remained stable between 3 and 5 years old. In contrast, EF was a stronger predictor of early math skills among 3-year-olds when compared to 5year-olds.
A Method for Extracting Influential Nodes while Considering the Development of Social Networks

In this paper, we discuss the problem of target selection for discovering influential node groups (target set) in a social network. Target set selection has been proven to be NP hard, and a method proposed by Kempe et al. that is based on a greedy algorithm is known as a sophisticated approximate solution method for the target set selection problem. However, since no consideration was given to the dynamic change that occurs in social networks, issues arise with the method proposed by Kempe et al. in networks where the number of nodes and links change. In this paper, we propose an algorithm in which we augment the method of Kempe et al. so that information is diffused while the network is changed over time and influential node groups are discovered from an expected number of diffused nodes.
Terminator Detection by Support Vector Machine Utilizing a Stochastic Context-Free Grammar

A 2-stage detector was designed to find rho-independent transcription terminators in the Escherichia coli genome. The detector includes a stochastic context free grammar (SCFG) component and a support vector machine (SVM) component. To find terminators, the SCFG searches the intergenic regions of nucleotide sequence for local matches to a terminator grammar that was designed and trained utilizing examples of known terminators. The grammar selects sequences that are the best candidates for terminators and assigns them a prefix, stem-loop, suffix structure using the Cocke-Younger-Kasaami (CYK) algorithm, modified to incorporate energy effects of base pairing. The parameters from this inferred structure are passed to the SVM classifier, which distinguishes terminators from non-terminators that score high according to the terminator grammar. The SVM was trained with negative examples drawn from intergenic sequences that include both featureless and RNA gene regions (which were assigned prefix, stem-loop, suffix structure by the SCFG), so that it successfully distinguishes terminators from either of these. The classifier was found to be 96.4% successful during testing
Behavioral Defects in Chaperone-Deficient Alzheimer's Disease Model Mice

Molecular chaperones protect cells from the deleterious effects of protein misfolding and aggregation. Neurotoxicity of amyloid-beta (Aβ) aggregates and their deposition in senile plaques are hallmarks of Alzheimer's disease (AD). We observed that the overall content of αB-crystallin, a small heat shock protein molecular chaperone, decreased in AD model mice in an age-dependent manner. We hypothesized that αB-crystallin protects cells against Aβ toxicity. To test this, we crossed αB-crystallin/HspB2 deficient (CRYAB-/-HSPB2-/-) mice with AD model transgenic mice expressing mutant human amyloid precursor protein. Transgenic and non-transgenic mice in chaperone-sufficient or deficient backgrounds were examined for representative behavioral paradigms for locomotion and memory network functions: (i) spatial orientation and locomotion was monitored by open field test; (ii) sequential organization and associative learning was monitored by fear conditioning; and (iii) evoked behavioral response was tested by hot plate method. Interestingly, αB-crystallin/HspB2 deficient transgenic mice were severely impaired in locomotion compared to each genetic model separately. Our results highlight a synergistic effect of combining chaperone deficiency in a transgenic mouse model for AD underscoring an important role for chaperones in protein misfolding diseases.
Automatic Tuning of Decentralized Controllers by Swarm Intelligence

This study is concerned with an application of particle swarm optimization (PSO) technique for automatic tuning of multi-loop PID controllers for multivariable systems by employing a comprehensive objective function. Controller optimization and tuning of a benchmark multivariable system is addressed to demonstrate merits of the proposed algorithm. Reported results indicate effectiveness of the introduced comprehensive objective function, which fines any violence from desired region defined for the time response of the system in different channels, simultaneously, in addition to the control effort used by the decentralized control system. It is shown that PSO is an effective algorithm in finding the optimal solution for the given problem. The proposed algorithm and PSO method have the ability of being applied to high dimensional industrial applications. The designer of the control system can enjoy optionality provided in the selection of desired region for time response of the system
Implementation of an SMS Spam Control System Based on Trust Management

Communication networks (e.g., the Internet and cellular networks) play an important role in our modern life for information dissemination and communications. However, at the same time when people gather and exchange useful information through networks, they also receive unwanted data and contents, such as malware, spam, spy ware, intrusions and unsolicited commercial advertisements. For example, SMS spam has caused a serious problem to mobile users by intruding user devices, occupying device memories and irritating mobile users. Unwanted traffic control (UTC) has become a crucial issue that impacts the future success of communication networks. This paper presents the design and implementation of an SMS spam control system based on trust management. We evaluate the system performance under a variety of intrusions and attacks. The result shows that the system is effective with regard to accuracy, efficiency and robustness. It can control SMS spam from its source to destinations according to trust evaluation.
Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method

Neighborhood graphs are gaining popularity as a concise data representation in machine learning. However, naive graph construction by pairwise distance calculation takes $O(n^2)$ runtime for $n$ data points and this is prohibitively slow for millions of data points. For strings of equal length, the multiple sorting method (Uno, 2008) can construct an $\epsilon$-neighbor graph in $O(n+m)$ time, where $m$ is the number of $\epsilon$-neighbor pairs in the data. To introduce this remarkably efficient algorithm to continuous domains such as images, signals and texts, we employ a random projection method to convert vectors to strings. Theoretical results are presented to elucidate the trade-off between approximation quality and computation time. Empirical results show the efficiency of our method in comparison to fast nearest neighbor alternatives.
A Systematic Review of Interspinous Dynamic Stabilization

Background A systematic literature review of interspinous dynamic stabilization, including DIAM, Wallis, Coflex, and X-STOP, was conducted to assess its safety and efficacy. Methods The search was done in Korean and English, by using eight domestic databases which included KoreaMed and international databases, such as Ovid Medline, Embase, and the Cochrane Library. A total of 306 articles were identified, but the animal studies, preclinical studies, and studies that reported the same results were excluded. As a result, a total of 286 articles were excluded and the remaining 20 were included in the final assessment. Two assessors independently extracted data from these articles using predetermined selection criteria. Qualities of the articles included were assessed using Scottish Intercollegiate Guidelines Network (SIGN). Results The complication rate of interspinous dynamic stabilization has been reported to be 0% to 32.3% in 3- to 41-month follow-up studies. The complication rate of combined interspinous dynamic stabilization and decompression treatment (32.3%) was greater than that of decompression alone (6.5%), but no complication that significantly affected treatment results was found. Interspinous dynamic stabilization produced slightly better clinical outcomes than conservative treatments for spinal stenosis. Good outcomes were also obtained in single-group studies. No significant difference in treatment outcomes was found, and the studies compared interspinous dynamic stabilization with decompression or fusion alone. Conclusions No particular problem was found regarding the safety of the technique. Its clinical outcomes were similar to those of conventional techniques, and no additional clinical advantage could be attributed to interspinous dynamic stabilization. However, few studies have been conducted on the long-term efficacy of interspinous dynamic stabilization. Thus, the authors suggest further clinical studies be conducted to validate the theoretical advantages and clinical efficacy of this technique.
RTHpower: Accurate fine-grained power models for predicting race-to-halt effect on ultra-low power embedded systems

Ultra-low power (ULP) embedded systems have become popular in the scientific community and industry, especially in media and wearable computing. In order to model ULP systems where energy per instruction can be as low as few pJ, more accurate fine-grained approaches are needed. However, there are no application-general, fine-grained and validated models yet that provide insights into how an application running on an ULP embedded system consumes energy and, particularly, whether the race-to-halt (RTH) strategy that are widely used in high-performance computing (HPC) systems is still applicable to ULP embedded systems. In this study, we propose new RTHpower models which provide insights into how an application consumes energy when running on an ULP embedded system. The models are trained and validated with data from 22 microbenchmarks. The experimental results show that RTH is not always applicable to ULP embedded systems, due to their low static power. RTHpower models support predicting when and when not to use RTH for a given application.
Life-experience passwords (LEPs)

Passwords are widely used for user authentication, but they are often difficult for a user to recall, easily cracked by automated programs and heavily reused. Security questions are also used for secondary authentication. They are more memorable than passwords, but are very easily guessed. We propose a new authentication mechanism, called "life-experience passwords (LEPs)," which outperforms passwords and security questions, both at recall and at security. Each LEP consists of several facts about a user-chosen past experience, such as a trip, a graduation, a wedding, etc. At LEP creation, the system extracts these facts from the user's input and transforms them into questions and answers. At authentication, the system prompts the user with questions and matches her answers with the stored ones. In this paper we propose two LEP designs, and evaluate them via user studies. We further compare LEPs to passwords, and find that: (1) LEPs are 30--47 bits stronger than an ideal, randomized, 8-character password, (2) LEPs are up to 3x more memorable, and (3) LEPs are reused half as often as passwords. While both LEPs and security questions use personal experiences for authentication, LEPs use several questions, which are closely tailored to each user. This increases LEP security against guessing attacks. In our evaluation, only 0.7% of LEPs were guessed by friends, while prior research found that friends could guess 17--25% of security questions. LEPs also contained a very small amount of sensitive or fake information. All these qualities make LEPs a promising, new authentication approach.
Analysis of the Diversity of Substrate Utilisation of Soil Bacteria Exposed to Cd and Earthworm Activity Using Generalised Additive Models

Biolog EcoPlates™ can be used to measure the carbon substrate utilisation patterns of microbial communities. This method results in a community-level physiological profile (CLPP), which yields a very large amount of data that may be difficult to interpret. In this work, we explore a combination of statistical techniques (particularly the use of generalised additive models [GAMs]) to improve the exploitation of CLPP data. The strength of GAMs lies in their ability to address highly non-linear relationships between the response and the set of explanatory variables. We studied the impact of earthworms (Aporrectodea caliginosa Savigny 1826) and cadmium (Cd) on the CLPP of soil bacteria. The results indicated that both Cd and earthworms modified the CLPP. GAMs were used to assess time-course changes in the diversity of substrate utilisation (DSU) using the Shannon-Wiener index. GAMs revealed significant differences for all treatments (compared to control -S-). The Cd exposed microbial community presented very high metabolic capacities on a few substrata, resulting in an initial acute decrease of DSU (i.e. intense utilization of a few carbon substrata). After 54 h, and over the next 43 h the increase of the DSU suggest that other taxa, less dominant, reached high numbers in the wells containing sources that are less suitable for the Cd-tolerant taxa. Earthworms were a much more determining factor in explaining time course changes in DSU than Cd. Accordingly, Ew and EwCd soils presented similar trends, regardless the presence of Cd. Moreover, both treatments presented similar number of bacteria and higher than Cd-treated soils. This experimental approach, based on the use of DSU and GAMs allowed for a global and statistically relevant interpretation of the changes in carbon source utilisation, highlighting the key role of earthworms on the protection of microbial communities against the Cd.
The Impact of Divergence Time on the Nature of Population Structure: An Example from Iceland

The Icelandic population has been sampled in many disease association studies, providing a strong motivation to understand the structure of this population and its ramifications for disease gene mapping. Previous work using 40 microsatellites showed that the Icelandic population is relatively homogeneous, but exhibits subtle population structure that can bias disease association statistics. Here, we show that regional geographic ancestries of individuals from Iceland can be distinguished using 292,289 autosomal single-nucleotide polymorphisms (SNPs). We further show that subpopulation differences are due to genetic drift since the settlement of Iceland 1100 years ago, and not to varying contributions from different ancestral populations. A consequence of the recent origin of Icelandic population structure is that allele frequency differences follow a null distribution devoid of outliers, so that the risk of false positive associations due to stratification is minimal. Our results highlight an important distinction between population differences attributable to recent drift and those arising from more ancient divergence, which has implications both for association studies and for efforts to detect natural selection using population differentiation.
An Airborne Radar Power Supply With Contactless Transfer of Energy—Part II: Converter Design

A magnetic link is established between the stationary and revolving frames of a radar system by means of a rotating transformer. Based on the magnetics analysis of Part I of the series, a design methodology is proposed for integrating a rotating transformer into a power electronic converter using the efficiency and voltage gain plots. It is shown that a phase-shifted full-bridge topology can effectively utilize the parasitic components of the transformer. The increased magnetizing current assists the resonant transition, and this, in turn, compensates for the increased conduction losses that a rotating transformer yields. The proposed design method secures the soft-switching operation of the converter over the entire load range and allows efficient operation and reduced electromagnetic emissions. The methodology is evaluated experimentally, and the resulting prototype demonstrates an average efficiency of 92.6% in the 0.2-1-kW output power range. The proposed topology extends the power capacity of the rotating transformer without compromising its size, cost, or performance. A comparison between the slip rings and rotating transformer solutions highlights the merits and weaknesses of each technology.