Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Models for Correlated Demand

Demand forecasting is a crucial component of revenue management, as forecasts are almost always inputs into optimization routines for pricing decisions. The performance of many revenue management systems often depends critically on the quality of demand forecasts. Indeed, industry estimates suggest that a 20% reduction of forecast error may lead to 1% incremental increase in revenue (Talluri and van Ryzin, 2004). Most of current research and many practical revenue management systems rely on univariate stochastic models for demand. These usually consist of popular statistical distributions, such as the normal and gamma distributions (McGill and van Ryzin 1999; Lee 1990). However, in practice the available capacity is allocated dynamically between different products and thus is makes sense to take into account the potential correlations among product demand. At the same time, demand for any single product is recorded over the entire booking horizon at fixed time points (snapshots). In this framework it is natural to investigate whether there is correlation among realizations of demand for any individual product in different booking periods. Inter–product and inter–temporal correlations of demand have been documented empirically (McGill 1995). For efficiency reasons, it is therefore important to take them into account in a demand forecasting model. Correlation between demands for different products may arise in several different ways. For instance, in the travel industry some discount fare passengers may have a tendency to upgrade to the full fare if the discount fare is not available, thus giving rise to correlation between discount and full fare
Spalt4 mediates invagination and otic placode gene expression in cranial ectoderm

Vertebrate placodes are regions of thickened head ectoderm that contribute to paired sensory organs and cranial ganglia. We demonstrate that the transcription factor Spalt4 (also known as Sall4) is broadly expressed in chick preplacodal epiblast and later resolves to otic, lens and olfactory placodes. Ectopic expression of Spalt4 by electroporation is sufficient to induce invagination of non-placodal head ectoderm and prevent neurogenic placodes from contributing to cranial ganglia. Conversely, loss of Spalt4 function in the otic placode results in abnormal otic vesicle development. Intriguingly, Spalt4 appears to initiate a placode program appropriate for the axial level but is not involved in later development of specific placode fates. Fgfs can regulate Spalt4, since implantation of Fgf2 beads into the area opaca induces its expression. The results suggest that Spalt4 is involved in early stages of placode development, initiating cranial ectodermal invagination and region-specific gene regulatory networks.
Supporting orientation during search result examination

Search engines help their users decide which results to visit using captions comprising titles, URLs, and snippets containing the query keywords and proximal text from landing pages (the search results linked from the result page). Although caption content can be a key factor in these decisions, snippets provide only basic support for orienting users with landing page content from the search-engine result page (SERP), and no support during the transition to landing pages or once users reach the page following a selection decision. As a result, many searchers must employ inefficient strategies such as skimming and scanning the content of the landing page. In this paper we propose a novel method, called clickable snippets, to address this shortcoming. Clickable snippets provide searchers with a direct and actionable link between SERP captions and landing-page content. We describe a user study comparing clickable snippets with extant methods of orientation support such as query-term highlighting on the landing page and thumbnail previews on the SERP. We show that clickable snippets are preferred by participants, and lead to more effective and efficient searching. Our findings have implications for the design of the user experience in search systems.
A fault-tolerant and congestion-aware routing algorithm for Networks-on-Chip

This paper presents a fault-tolerant routing algorithm for mesh-based Networks-on-Chip (NoC) with faulty links. It is a distributed, adaptive and congestion-aware routing algorithm where only two virtual channels are used for both adaptiveness and fault-tolerance. The proposed routing method has a multilevel fault-tolerance capability and therefore it is capable to tolerate more faulty links in more complicated faulty situations with additional hardware costs. The network performance, fault-tolerance capability and hardware overhead are evaluated through appropriate simulations. The experimental results show that the overall reliability of a Network-on-Chip is significantly enhanced against multiple link failures or partially faulty routers with only a small hardware overhead.
Design of multilevel inverter based statcom for power quality enhancement

Power quality problems in the power distribution system are not new, but the customer awareness regarding these problems has increased nowadays. Recent development of Flexible Alternating Current Transmission System (FACTS) devices offers reliable solution to power quality problems. This work proposes a solution that implements custom power devices such as Static Synchronous Compensator (STATCOM) in simulation module of power system using multilevel inverter to improve the quality of power. During simulation, the control angle of the converter is varied to vary the power system parameters at the receiving end. The application of these devices provides faster control and enhances the way of using the transmission system at their rated thermal capability. The devices are modeled, simulated using proposed 14 bus system and at the same time control pulse of two different kinds- Sine Wave Pulse Width Modulation (SPWM), Optimized Hybrid Phase Disposition Method (OHPDPWM) are used and the results are compared to measure the effectiveness of control.
THE DEVELOPMENT OF ANTISOCIAL BEHAVIOR: GENES, BRAIN AND ENVIRONMENT AS DIRECT AND INTERACTIVE PREDICTORS OF ANTISOCIAL BEHAVIOR FROM EARLY CHILDHOOD TO YOUNG ADULTHOOD

Antisocial behavior (AB), including physical and sexual aggression, destruction of property, theft, and violation of serious rules, has been of particular interest to researchers and the general public because of its large cost to society and negative impact on perpetrators and victims, its chronic nature and trajectory, and the difficulty in preventing and treating AB. Although recent views of AB have emphasized the complex interplay between biology and the environment (Jaffee et al., 2005; Reiss, 2005; Rutter, 1997), little empirical work has connected genetic variability, neural reactivity and environmental risk in understanding the development of AB in early adulthood. Thus, the current study sought to advance our understanding of AB in an ethnically diverse sample of 310 young men followed prospectively from age 1.5 to age 20 through measurement of amygdala reactivity to threat, variability in genes affecting serotonin signaling, cumulative environmental risk during early childhood and early adolescence, and measures of AB during adolescence and at age 20. Contrary to our hypotheses, we found that AB across adolescence and at age 20 was related to lower amygdala reactivity to threat, regardless of the level of callous traits also present. Also contrary to our hypotheses, we found that variants in serotonin genes previously linked to lower amygdala reactivity were related to callous traits as well as AB in the presence of high callousness. Imaging genetics models that linked variability in specific serotonin genes, amygdala reactivity, and AB were not supported. Similarly, little support was found for Imaging Gene by Environment interactions in which the interactions between genetic variability and environmental risk were linked to AB via their association with neural reactivity. Results highlight the difficulty in testing complex models of the likely interactions between genes, brain and environment in understanding AB and suggest a specific role of amygdala reactivity in AB.
Analyzing ranking data using decision tree

Ranking/preference data arises from many applications in marketing, psychology and politics. We establish a new decision tree model for the analysis of ranking data by adopting the concept of classi-(cid:12)cation and regression tree [2]. We modify the existing splitting criteria, Gini and entropy, which can precisely measure the impurity of a set of ranking data. Two types of impurity measures for ranking data are introduced, namely n -wise and top-k measures. Minimal cost-complexity pruning is used to (cid:12)nd the optimum-sized tree. In model assessment, the area under the ROC curve (AUC) is applied to evaluate the tree performance. The proposed methodology is implemented to analyze a partial ranking dataset of Inglehart’s items collected in the 1993 International Social Science Programme survey. Change in importance of item values with country, age and level of education are identi(cid:12)ed.
Forecasting covariance for optimal carry trade portfolio allocations

Modelling and forecasting of asset volatility and covariance is of prime importance in the construction of portfolios. In this paper, we present a generalised multi-factor model that incorporates heteroskedasticity and dependence in the idiosyncratic error terms. We apply this model to forecasting the time-varying covariances in a basket of high interest rate and a basket of low interest rate carry trade currencies and then utilise these forecasts for portfolio optimisation. We compare traditional Markowitz portfolio optimisation to the more recently popular risk-based portfolio optimisation. Our model is shown to provide superior risk-adjusted returns for a currency carry trade strategy over the period 1999 – 2014.
Efficacy Assessment for Several Natural Products with Potential Cytotoxic Activity Against Breast and Cervix Cancers (THE 1st INTERNATIONAL CONFERENCE ON ARID LAND (ICAL 1) "DESERT TECHNOLOGY X (DT X)")

Over more than five decades, cancer remains a national and international health problem regardless of the discovery of several dozens of novel anticancer drugs (natural and synthetic). Accordingly, screening of natural products for promising anti cancer activity is very initiative field of studies in several countries with diversity of gardenia. Breast and cervix cancers are of the most common gynecological female solid tumors that represent major health problem. Herein, we have assessed the cytotoxic characteristics of several molecules of natural origin (cerulinin, chrysin, honikiol, limonin, mevinolin, resveratrol, salicin, retinol, ascorbic acid and calciferol) against two different gynecological breast (MCF-7) and Cervix (HeLa) solid tumor cell lines. After exposure to serial concentrations of the test compounds, SRB-u assay was undertaken and viability assessment was performed via fitting to Emax model to identify the cytotoxicity parameters such as, IC50 and resistant fraction (R-value). Tested compounds showed cytotoxic efficacy against both gynecological solid tumor cell lines (MCF-7 and HeLa) with IC50's ranged from 0.61 to 131.1 µg/ml. In MCF-7 breast cancer cell line; cerulinin, honikiol, mevinolin and calciferol showed the highest potency with IC50 less than 5 µg/ml. Chrysin showed moderate potency with IC50 of 7.27 µg/ml. Ascorbic acid and resveratrol showed the weakest cytotoxic activity with IC50 more than 10 µg/ml. In HeLa cervix cancer cell line; cerulinin and mevinolin showed the highest potency with IC50 less than 5 µg/ml. Chrysin, honikiol, resveratrol and calciferol showed moderate potency with IC50 of ranging from 5-10 µg/ml. Ascorbic acid was the weakest cytotoxic molecule with IC50 more than 10 µg/ml. Limonine, salicin and retinol failed to exert any cytotoxic effect against MCF-7 or HeLa cancer cell lines in-vitro. RT-PCR analysis revealed that the cytotoxicity of these products is multi-factorial and not solely dependent on p53 expression. Impressively, molecules of potent and moderate potency (except chrysin) showed low resistant fraction in both gynecological solid tumor cancer cell lines. In conclusion, our data showed wide range of variable efficacy of several molecules of natural origin against two hormone dependent solid tumor cell lines.
Modèles mixtes en génétique animale : sélection de variables par optimisation combinatoire

En selection genomique animale, un des enjeux consiste a identiﬁer un sous-ensemble de marqueurs genomiques explicatifs pour un trait d'interet quantitatif. La speciﬁcite des etudes animales necessite l'utilisation de modeles mixtes, du fait des liens de parente entre individus. Nous proposons d'eﬀectuer, dans ce cadre, une selection des marqueurs d'interet a l'aide de methodes d'optimisation combinatoire.
CMOS Image Sensor Random Telegraph Noise Time Constant Extraction From Correlated To Uncorrelated Double Sampling

A new method for on-chip random telegraph noise (RTN) characteristic time constant extraction using the double sampling circuit in an 8.3 Mpixel CMOS image sensor is described. The dependence of the measured RTN on the time difference between the double sampling and the key equation used for time constant extraction are derived from the continuous time RTN model and the discrete event RTN model. Both approaches lead to the same result and describe the data reasonably well. From the detailed study of the noisiest 1000 pixels, we find that about 75% to 85% of them show the signature of a single-trap RTN behavior with three distinct signal levels, and about 96% of the characteristic time constants fall between 1 μs and 500 μs with the median around 10 μs at room temperature.
Deep Active Learning Multiclass Classifier for the Sentimental Analysis in Imbalanced Unstructured Text Data

This paper presents an active learning framework for multi-class sentiment analysis, focusing on addressing the challenges of class imbalance. The proposed approach combines Convolutional Neural Networks (CNN) as the base classifier, logistic regression as the meta classifier, and alternative meta classifiers including Random Forest, SVM, and Gradient Boosting. The active learning algorithm intelligently selects informative instances for labeling, reducing the need for large amounts of labeled data and manual annotation efforts. The CNN-based classifier automatically extracts relevant features from the text data, enhancing sentiment analysis capabilities. The logistic regression meta-classifiers improve classification performance. Experimental results demonstrate the effectiveness of the proposed approach in handling class imbalance and achieving accurate sentiment classification across different classes. The active learning framework with CNN as the base classifier and logistic regression as the meta-classifier shows promising results, offering a valuable solution for multi-class sentiment analysis tasks where labeled data acquisition is resource-intensive.
Stereo image compression with disparity compensation using the MRF model

In coming years there will be an increasing demand for realistic 3-D display of scenes using such popular approaches as stereo or multi-view images. As the amount of information displayed increases so does the need for digital compression to ensure efficient storage and transmission of the sequences. In this paper, we introduce a new approach to stereo image compression based on the MRF model and MAP estimation. The basic strategy will be to encode the right image as a reference, then estimate the disparity between blocks in the right and left images and transmit the disparity and the error between the disparity compensated left image and the original. This approach has been used in the literature and is akin to the block matching technique used for motion compensation in video coders. The main drawback in this approach is that as the block size becomes smaller the overhead required to transmit the disparity map becomes too large. Also, simple block matching algorithms frequently fail to provide good matching results because the correspondences are locally ambiguous due to noise, occlusion, and repetition or lack of texture. The novelty in our work is that to compute the disparity map we introduce an MRF model with its corresponding energy equation. This allow us to incorporate smoothness constraints, to take into account occlusion, and to minimize the effect of noise in the disparity map estimation. Obtaining a smooth disparity is beneficial as it reduces the overhead required to transmit the disparity map. It is also useful for video coding since the robustness against noise ensures that disparity maps in successive frames will be very similar. We describe this new formulation in detail and provide compression results.
Properties and Plasticity of Paired-Pulse Depression at a Central Synapse

Synaptic depression was studied at the axo-axonic connection between the goldfish Mauthner axon and identified cranial relay interneurons using simultaneous presynaptic and postsynaptic recordings and a paired-pulse stimulus paradigm. We used interstimulus intervals (ISIs) ranging from 10 msec to 1 sec and a cycle time of ∼5 sec. Depression (Δ EPSP/EPSP1) was maximal at the shorter intervals (80%) and decreased exponentially with a τ ∼ 400 msec (360 ± 107 msec, mean ± SD). We found the amplitudes of the first and second EPSP were not correlated, indicating the magnitude of depression does not depend on the amount of transmitter released by the conditioning stimulus. At short ISIs, the latency of EPSP2 was 23% longer than that of EPSP1 and recovered to control with τ ∼ 400 msec, whereas rise time and decay time were not altered significantly. The latency distribution, which is determined by the timing of the first quantum released each trial, was used to derive α(t), the rate of evoked exocytosis after an action potential. α(t) was biphasic, and both components were consistently delayed during depression. Presynaptic manipulations of putative intracellular regulatory pathways, such as Ca2+ and GTPγS injections, preferentially affected the amplitude of EPSP1 or EPSP2. These results are not consistent with simple depletion of the available pool of synaptic vesicles as the major mechanism underlying depression. They rather suggest that it is attributable to a modification or refractoriness of the release process and that there may be multiple pathways subserving evoked exocytosis.
The Amsterdam Hypermedia Model: Extending Hypertext to Support Real Multimedia

We present a model of hypermedia that allows the combination of “hyper-structured” information with dynamic multimedia information. The model is derived by extending the Dexter hypertext reference model and the CMIF multimedia model. The Amsterdam hypermedia model allows the following, in addition to the model provided by Dexter: • the composition of multiple dynamic media, in order to specify a collection of time-based media making up a complete multimedia presentation; • the definition of channels for specifying default presentation information, allowing the specification of the presentation characteristics of nodes at a more general level than that for an individual node; • the composition of existing presentations into larger presentations, taking into account possible clashes of resource usage; • the inclusion of temporal relations while maintaining the separation of structure and presentation information, where time-based relationships are treated as presentation information; • the definition of context for the source and destination anchors of a link in order to specify the parts of a presentation affected on following the link. The Amsterdam hypermedia model enables the description of structured multimedia documents, incorporating time at a fundamental level, and extending the hypertext notion of links to timebased media and compositions of different media. The paper is organised as follows. The Dexter hypertext model and the CMIF multimedia model are summarised, and their limitations for use as a more general hypermedia model are discussed. The extensions included in the Amsterdam hypermedia model are described and a summary of the resulting model is given.
Integrated System Design for Broadcast Program Infringement Detection

Supervision of television and radio broadcast programs by the “Komisi Penyiaran Indonesia (KPI)” Central Java was still performed manually i.e. direct supervision by humans. It certainly had some weaknesses related to the human error such as tiredness and weary eyes. Therefore, we needed intelligent software that could automatically detect broadcast infringement. Currently, research in this area had not been studied. This research was to design an integrated system to detect broadcast infringement including data design, architecture design and main module interface design. Two main stages in this system are the Indonesian language speech recognition and detection of infringements of the broadcast program. With the method of Mel Frequency cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) speech recognition application that used the 1050 sample data produces about 70% accuracy rate. This research would continue to implement the plan that had been created using speech recognition applications that had been built.
Cross-national measurement of polarization in political discourse: Analyzing floor debate in the U.S. the Japanese legislatures

Political polarization in public space can seriously hamper the function and the integrity of contemporary democratic societies. In this paper, we propose a novel measure of such polarization, which, by way of simple topic modelling, quantifies differences in collective articulation of public agendas among relevant political actors. Unlike most other polarization measures, our measure allows cross-national comparison. Analyzing a large amount of speech records of legislative debate in the United States Congress and the Japanese Diet over a long period of time, we have reached two intriguing findings. First, on average, Japanese political actors are far more polarized in their issue articulation than their counterparts in the U.S., which is somewhat surprising given the recent notion of U.S. politics as highly polarized. Second, the polarization in each country shows its own temporal dynamics in response to a different set of factors. In Japan, structural factors such as the roles of the ruling party and the opposition often dominate such dynamics, whereas the U.S. legislature suffers from persistent ideological differences over particular issues between major political parties. The analysis confirms a strong influence of institutional differences on legislative debate in parliamentary democracies.
CME Information

This activity underwent peer review in line with the standards of editorial integrity and publication ethics maintained by American Journal of Hematology. The peer reviewers have no conflicts of interest to disclose. The peer review process for American Journal of Hematology is single blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review. Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services’s Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non-conflicted expert.