Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in neutrophil/lymphocyte and platelet/lymphocyte ratios after chemotherapy correlate with chemotherapy response and prediction of prognosis in patients with unresectable gastric cancer

The aim of the study was to investigate the application value of neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) in the prediction of chemotherapy response and prognosis in patients with advanced gastric cancer. In total, 120 patients with unresectable gastric cancer were included and separated into two groups according to the median values of NLR or PLR (NLR low: <4.62 or NLR high: ≥4.62 and PLR low: <235 or PLR high: ≥235, respectively). Low baseline NLR level correlated with improved clinicopathological characteristics, including smaller tumor size, well differentiation and less metastasis. Low baseline PLR level also associated with less metastasis. Patients with a low baseline level of NLR or PLR had an improved response to chemotherapy. Patients with a higher baseline NLR and PLR had decreased progression-free survival (PFS) and overall survival (OS) ratios. Alterations in the NLR and PLR levels were associated with therapeutic efficacy and prognosis. The patients who remained in or switched to the low NLR level subgroup subsequent to first-line chemotherapy had an improved response and improved OS ratios, compared to the patients remaining in or switching to the high NLR level group. Similar results were observed when the PLR level was investigated. In conclusion, baseline NLR and PLR measurements, as well as changes of NLR and PLR following chemotherapy can predict the prognostic results in patients with unresectable gastric cancer.
The Scavenger Approach to Data Reclamation and Acquisition from Mixed Media Or iginal Sources

This paper describes the Scavenger approach for the reclamation of data from unstructured “mixed media” original sources. This approach provides semi-automated tool support to assist human operators in populating highly flexible data models using information derived from large sets of pre-existing source materials. The data models generated by this process are stored in such a way that they become accessible for manual investigation, browsing and importation into existing tools and processes. Scavenger is a generic approach which can be applied to many different domains. The approach addresses the difficult problems associated with the structuring and manipulation of large sets of unstructured original source material, derived from potentially many different sources.
Global Metabolic Response of Enterococcus faecalis to Oxygen

ABSTRACT Oxygen and oxidative stress have become relevant components in clarifying the mechanism that weakens bacterial cells in parallel to the mode of action of bactericidal antibiotics. Given the importance of oxidative stress in the overall defense mechanism of bacteria and their apparent role in the antimicrobial mode of action, it is important to understand how bacteria respond to this stress at a metabolic level. The aim of this study was to determine the impact of oxygen on the metabolism of the facultative anaerobe Enterococcus faecalis using continuous culture, metabolomics, and 13C enrichment of metabolic intermediates. When E. faecalis was rapidly transitioned from anaerobic to aerobic growth, cellular metabolism was directed toward intracellular glutathione production and glycolysis was upregulated 2-fold, which increased the supply of critical metabolite precursors (e.g., glycine and glutamate) for sulfur metabolism and glutathione biosynthesis as well as reducing power for cellular respiration in the presence of hemin. The ultimate metabolic response of E. faecalis to an aerobic environment was the upregulation of fatty acid metabolism and benzoate degradation, which was linked to important changes in the bacterial membrane composition as evidenced by changes in membrane fatty acid composition and the reduction of membrane-associated demethylmenaquinone. These key metabolic pathways associated with the response of E. faecalis to oxygen may represent potential new targets to increase the susceptibility of this bacterium to bactericidal drugs.
Development of a Shear Horizontal Surface Acoustic Wave Sensor System for Liquids with a Floating Electrode Unidirectional Transducer

The development of sensors using surface acoustic wave (SAW) devices has been attracting attention. A shear horizontal SAW (SH-SAW) sensor can detect the properties and chemical reactions in liquids. To realize practical applications of the SAW sensor, it is necessary to discuss its properties. In this paper, SH-SAW sensors with a floating electrode unidirectional transducer (FEUDT) or an interdigital transducer (IDT) are compared and the insertion loss and phase characteristics are measured. Also, the phase shift between sample and reference liquids is evaluated. The results indicate that the SH-SAW sensor with the FEUDT is suitable for liquid measurements. The SH-SAW sensor with the FEUDT is then mounted in a newly developed SH-SAW sensing system. The system configuration is the same as that of a vector voltmeter measurement system. However, circuits have been developed that reduce its size. Using this system, several liquids are measured. The obtained results agree well with the theoretical values.
Recurrence relations versus succession rules

In this paper we present a method to pass from a recurrence relation having constant coefficients (in short, a C-recurrence) to a finite succession rule defining the same number sequence. We recall that succession rules are a recently studied tool for the enumeration of combinatorial objects related to the ECO method. We also discuss the applicability of our method as a test for the positivity of a number sequence.
Short Paper: Performance evaluation on distributed data process in M2M systems

We show that distributed data processing for M2M applications improves the performance of M2M systems. The important roles of the M2M systems are to associate an application with required data and to execute the application on suitable processing entities for coming ubiquitous environment. First, we show simple rules on deployment of data process to reduce load in underlying M2M network. Next, we clarify cases such that an application should be divided into subroutines for distributed executions. And so, we model centralized and distributed execution as multi-stage queuing system to show the improvement by the distributed execution.
Speciation Dynamics: Generating Selective Pressure Towards Diversity

Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance which often includes task-specific features and hence a priori knowledge. Alternative methods, that do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life such as artificial ecologies (AE). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. We conclude by hypothesizing how knowledge about self-organizing SPTD in AE could be transferred to the domain of ER.
A Method for Designing Crease Patterns for Flat-Foldable Origami with Numerical Optimization

Origami is the art and science of making various shapes by simply folding a sheet of paper. When origami is studied as a geometrical problem, it is often assumed that the origami is folded flat. This flat foldability is an important property when we consider applying the techniques of origami to increase the portability and storage space of industrial products. We propose a method to generate new crease patterns that are flat-foldable by using a computer with the algorithm of numerical optimization. In this paper, we describe the five steps of our method. Although the numerical optimization sometimes fails to converge and we have to go back to the first step, we can generate many flat-foldable crease patterns containing randomly placed vertices. Our method can generate patterns that can be used not only as new origami designs but also as problems for the origami puzzle "Fold Me Up".
Unitary Beamformer Designs for MIMO Interference Broadcast Channels

Interference broadcast channel (IBC) is a generalized scenario for interference channel (IC) where each base station (BS) transmits to multiple users in its own cell while sharing same time-frequency resources with other adjacent BSs. In this case, it has been shown that by using interference alignment (IA) and under the availability of perfect channel state information (CSI), the achievable degrees of freedom (DoFs) can be linearly scaled up with the number of users. However, in the presence of imperfect CSI, the sum rate becomes degraded, and the achievability of full DoF can not be guaranteed. In this paper, considering a multiple-input-multiple-output (MIMO) IBC with constant channel coefficients, we propose IA algorithms that result in unitary beamformers. First, we consider two improved beamformer designs based on minimizing the weighted-leakage interference (Min-WLI) and maximizing the signal-to-interference-plus-noise ratio (Max-SINR). Second, we propose a novel minimum-mean-square-error (MMSE)-based IA algorithm. Both the improved Max-SINR and the MMSE-based IA schemes are optimized upon the knowledge of the channel estimation error variance so as to achieve better performance under CSI mismatch. We then demonstrate that these two schemes outperform Min-WLI and the conventional Max-SINR IA under perfect and imperfect CSI. Furthermore, it is shown that the MMSE-based IA achieves the same performance trend as the improved Max-SINR IA does; however, the former needs less CSI to be available and has less computational complexity compared to the latter. Moreover, it is established that the MMSE-based IA can result in diagonalized, decoupled subchannels under perfect CSI. The superiority of the proposed algorithms over the conventional schemes is corroborated by numerical simulations.
Orienting Kinesthetically: A Haptic Handheld Wayfinder for People with Visual Impairments

Orientation and position information are vital for people with visual impairments if they are to avoid obstacles and hazards while walking around. We develop and evaluate a haptic direction indicator that delivers directional information in real time through kinesthetic cues. The indicator uses a novel kinesthetic perception method called the pseudo-attraction force technique, which employs the nonlinear relationship between perceived and physical acceleration to generate a force sensation. In an experiment, we find that the haptic direction indicator allowed people with visual impairments to walk safely along a predefined route at their usual walking pace without any previous training, independent of the existence of auditory information. The findings indicate that the haptic direction indicator is effective at delivering simple navigational information, and is a suitable substitute for and/or enhancement to conventional wayfinding methods.
Black-Scholes Model Under Arithmetic Brownian Motion

Usually, in the Black-Scholes world, it is assumed that a stock follows a Geometric Brownian motion. The aim of our research is to present Black-Scholes model in a world where the stock is attributed an Arithmetic Brownian motion. Although Arithmetic Brownian motion is simpler due to lack of the geometric terms, as it is shown the option model is eventually analytically less tractable than under Geometric Brownian motion.
Deviant Behavior in School Setting

Deviant behavior has become a daunting problem lately. More pupils are displaying abnormal behavior and thus destabilizing learning processes within school settings. It is a well-known fact that teenagers usually learn how to adapt to society and that aberrant behavior may lead to problems with conformity to societal values. Although the main causes of deviant behavior have been elucidated, the problem, despite numerous studies, deviant behavior has not been fully understood by scholars. The notion of deviance is closely related to the definition of the social norm which was defined almost a hundred years ago. The present study aims to examine causes and effects of deviant behavior, and identify main strategies to combat the issue. The research methods employed included a theoretical framework analysis, literature review, and qualitative research. A set of recommendations for the teenagers with deviant behaviour and their parents were identified to help them adjust to society.
Coordination of multiple agents via combination algorithms

Multi-agent rendezvous is of great importance in study of networked agent systems. In this paper, we propose a novel combination rendezvous algorithm framework and the corresponding algorithms. The convergence of the algorithms is proved based on the novel tools developed in recent research on graph theory and consensus study. It is noted that the widely studied circumcenter algorithm can be considered as a special case of the combination framework. Moreover, analysis on the framework provides a new viewpoint on designing coordination algorithms for multiple agents with limited sensing ranges. Numerical examples have illustrated effectiveness of the proposed algorithms and also made a comparison between the proposed algorithms and the existing algorithms.
Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives

In this paper, we propose a stand-alone mobile visual search system based on binary features and the bag-of-visual words framework. The contribution of this study is three-fold: (1) We propose an adaptive substring extraction method that adaptively extracts informative bits from the original binary vector and stores them in the inverted index. These substrings are used to refine visual word-based matching. (2) A modified local NBNN scoring method is proposed in the context of image retrieval, which considers the density of binary features in scoring each feature matching. (3) In order to suppress false positives, we introduce a convexity check step that imposes a convexity constraint on the configuration of a transformed reference image. The proposed system improves retrieval accuracy by 11% compared with a conventional method without increasing the database size. Furthermore, our system with the convexity check does not lead to false positive results.
Futurology in the College Classroom.

There is little research on teaching futurology, which is surprising, given that instructors with a future-oriented perspective can encourage students to express constructive hope about controversial problems (e.g., climate change) rather than denying problems (Ojala, 2015). This study evaluates what learning outcomes can be accomplished through three different future-oriented in-class group activities: a future-oriented discussion, a future-oriented roleplay activity, and a backcasting activity. Analysis of student feedback suggests that these three activities encourage similar levels of student interest, understanding, and productive discussion while helping students practice both general college skills and skills specifically related to futurology. The main strength of future-oriented discussion is general understanding of both a topic and one's own perspective on it; of future-oriented roleplay, debate and emotional engagement; and of backcasting, evaluation of different potential futures and a sense of ownership over the future.
Inductive and example-based learning for text classification

Text classification has been widely applied to many practical tasks. Inductive models trained from labeled data are the most commonly used technique. The basic assumption underlying an inductive model is that the training data are drawn from the same distribution as the test data. However, labeling such a training set is often expensive for practical applications. On the other hand, a large amount of labeled data, which have been drawn from a different distribution, is often available in the same application domain. It is thus very desirable to take advantage of these data even though there is a discrepancy between their underlying distribution and that of the test set. This paper compares three text classification algorithms applied in this scenario, including two inductive Maximum Entropy (MaxEnt) models, one flatly initialized and the other initialized with a term-frequency/inverse document frequency (Tf*Idf) weighted vector space model, and an example-based learning algorithm, which assigns a class label to a text by learning from the labels assigned to the training data that are similar to the text. Experiment results show that example-based learning has achieved more than 5% improvement in precisions across almost all coverage levels.
Building a Dialogue Between Feminist, Post- (De)Colonial And Bourdeusian Studies to Analyse the Production And Negotiation of Difference in A World on the Move: The Case of Brazilians in London

Drawing on research with Brazilian in London, which combined 18-month ethnography in places of leisure with 33 in-depth interviews, this articles builds a theoretical framework, through a dialogue between feminist, post -(de)colonial and Bourdeusian studies, in order to analyse the production and negotiation of difference in a globalised world. e article argues that a dialogue between (black) feminist, post and de-colonial theory, and a more exible and multi-varied approach of the work of Pierre Bourdieu, analytically developed in migration studies by Abdelmalek Sayad, proves to be useful to analytically address how people are constantly producing and negotiating difference in a ‘world on the move’. Key words: Post-colonial Bourdieu, Brazilians in London, Black feminism, De-colonial studies
Multilevel refinement based on neighborhood similarity

The multilevel graph partitioning strategy aims to reduce the computational cost of the partitioning algorithm by applying it on a coarsened version of the original graph. This strategy is very useful when large-scale networks are analyzed. To improve the multilevel solution, refinement algorithms have been used in the uncorsening phase. Typical refinement algorithms exploit network properties, for example minimum cut or modularity, but they do not exploit features from domain specific networks. For instance, in social networks partitions with high clustering coefficient or similarity between vertices indicate a better solution. In this paper, we propose a refinement algorithm (RSim) which is based on neighborhood similarity. We compare RSim with: 1. two algorithms from the literature and 2. one baseline strategy, on twelve real networks. Results indicate that RSim is competitive with methods evaluated for general domains, but for social networks it surpasses the competing refinement algorithms.
Outage Capacity Optimization for Free-Space Optical Links With Pointing Errors

We investigate the performance and design of free-space optical (FSO) communication links over slow fading channels from an information theory perspective. A statistical model for the optical intensity fluctuation at the receiver due to the combined effects of atmospheric turbulence and pointing errors is derived. Unlike earlier work, our model considers the effect of beam width, detector size, and jitter variance explicitly. Expressions for the outage probability are derived for a variety of atmospheric conditions. For given weather and misalignment conditions, the beam width is optimized to maximize the channel capacity subject to outage. Large gains in achievable rate are realized versus using a nominal beam width. In light fog, by optimizing the beam width, the achievable rate is increased by 80% over the nominal beam width at an outage probability of 10-5. Well-known error control codes are then applied to the channel and shown to realize much of the achievable gains.
Multiplicative Square Root Algorithms for FPGAs

Most current square root implementations for FPGAs use a digit recurrence algorithm which is well suited to their LUT structure. However, recent computing-oriented FPGAs include embedded multipliers and RAM blocks which can also be used to implement quadratic convergence algorithms, very high radix digit recurrences, or polynomial approximation algorithms. The cost of these solutions is evaluated and compared, and a complete implementation of a polynomial approach is presented within the open-source FloPoCo framework. This polynomial approach allows a shorter latency and higher frequency than the digit recurrence approach, and improves over previous multiplicative approaches. However, the cost of IEEE-compliant correct rounding is shown to be very high.
Biomapping: Privacy trustworthy biometrics using noninvertible and discriminable constructions

Biometric authentication and privacy protection are conflicting issues in a practical system. Since biometrics cannot be revoked or canceled if compromised duo to the permanent association with the user, privacy-preserving biometric recognition is desired. However, the recently proposed template protection schemes are not yet sufficiently mature. Specially, the popular noninvertible transform approach will result in an obvious decrease of GAR for a fixed FAR. In this paper, we put forward a novel anonymous fingerprint recognition scheme, Biomapping, as the first approach to integrate the feature extraction, noninvertible transform, and anonymous query as a whole. Biomapping extracts the fingerprint feature utilizing a minutiae-centered region encoding, then performs anonymous enrollment and verification using the noninvertible and discriminable constructions. Experiments on the public domain database show Biomapping can provide better recognition accuracy along with the ability to protect the biometric template, thus becomes a promising solution for privacy trustworthy biometric applications.
Conjugate analysis of multivariate normal data with incomplete observations

The authors discuss prior distributions that are conjugate to the multivariate normal likelihood when some of the observations are incomplete. They present a general class of priors for incorporating information about unidentified parameters in the covariance matrix. They analyze the special case of monotone patterns of missing data, providing an explicit recursive form for the posterior distribution resulting from a conjugate prior distribution. They develop an importance sampling and a Gibbs sampling approach to sample from a general posterior distribution and compare the two methods.
Simplified addressing scheme for mixed radix FFT algorithms

A mixed radix algorithm for the in-place fast Fourier transform (FFT), which is broadly used in most embedded signal processing fields, can be explicitly expressed by an iterative equation based on the Cooley-Tukey algorithm. The expression can be applied to either decimation-in-time (DIT) or decimation-in-frequency (DIF) FFTs with ordered inputs. For many newly emerging low power portable computing applications, such as mobile high definition video compressing, mobile fast and accurate satellite location, etc., the existing methods perform either resource consuming or non-flexible. In this paper, we propose a new addressing scheme for efficiently implementing mixed radix FFTs. In this scheme, we elaborately design an accumulator that can generate accessing addresses for the operands, as well as the twiddle factors. The analytical results show that the proposed scheme reduces the algorithm complexity meanwhile helps the designer to efficiently choose an arbitrary FFT to design the in-place architecture.