Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching a specific person in a specific location using deep features

Video instance search or also well known as object retrieval is a fundamental task in computer vision field and has a lot of applications. Most state-of-the-art systems are based on the Bag-of-Words model (BOW) for representing video frames and target object. When searching on nearly planar and rich-textured objects such as buildings and book cover, BOW argue to be a suitable model with very high performance. However, when searching on harder but more popular objects such as a specific person, BOW model still keep a lower performance. In this paper, we consider a new type of query which covers most popular topics: searching a person in a specific location. Inspired by recent successes of deep learning techniques, we propose new framework which leverage the powerful of both BOW model and deep feature in instance search. In particular, we use a linear kernel classifier instead of using L2 distance to compute similarity between two deep features. For further improvement, scene tracking are employed to deal with the cases face of query person is not detected. To evaluate the proposed methods, we conduct experiments over a standard benchmark dataset (TRECVID Instance Search 2016) with more than 300 GB in storage and 464 hours in duration. The results show that, our proposed methods significant improve the baseline system.
Analytical Study on Improving Lookup Performance of Distributed Hash Table Systems under Churn

Distributed hash table (DHT) systems can provide infrastructure supports for a wide variety of Internet-scale applications. However, the phenomenon of system churn (i.e., system dynamics) degrades the lookup performance of DHT systems and adversely impact the performance of applications built on top of them. To date, a number of approaches have been proposed to handle the churn from both the system side and the client side, but most of these approaches demonstrate their feasibility under certain scenarios via simulation or empirical experiments. There lacks theoretical analysis to direct how to make design choices under different scenarios and how to configure their parameters optimally. In this paper, we analytically study three important aspects on improving DHT lookup performance under churn: lookup strategy, lookup parallelism and lookup key replication. Our objective is to build a theoretical basis for DHT designers to make better design choices for practical deployment. We first compare the performance of two representative lookup strategies recursive routing and iterative routing, and explore the existence of better alternatives. Then we show the effectiveness of parallel lookup in systems with different churn rates and how to select the optimal degree of parallelism. Due to the importance of key replication on lookup performance, we analyze the expected lifetime of replicated keys under two different replication policies, and discuss how to make configuration in different environments. Besides analytical study, our results are also validated by simulation, and Kad [1] is taken as a case to show the meaningfulness of our analysis.
Observing Magnetic Fields on Large Scales

Observations of magnetic fields on scales up to several Mpc are important for understanding cluster and large-scale structure evolution. Our current census of such structures is heavily biased -- towards fields of several microG, towards fields in deep potential wells, and towards high inferred field strengths in cooling flow and other clusters from improper analysis of rotation measure data. After reviewing these biases, I show some recent results on two relics that are powered in very different ways. I describe new investigations that are now uncovering weak diffuse fields in the outskirts of clusters and other low density environments, and the good prospects for further progress.
A bilinear and three-dimensional model of acculturation for Puerto Ricans living in Central Florida

In breaking with past patterns of Puerto Rican immigrants to the U.S., the Central Florida Puerto Rican diaspora offers a unique opportunity for psychologists to study acculturation. Four measurement models of acculturation were examined in a sample of 484 Central Florida Puerto Ricans using of confirmatory factor analysis (CFA). The models tested varied in linearity (i.e., unilinear and bilinear) and dimensionality (i.e., cultural behaviors, values, and identity). As hypothesized, the bilinear three-dimensional model (i.e., cultural behaviors dimension, cultural values dimension, and ethnic identity dimension) resulted in the best model fit, χ 2 (178) = 423.23, CFI = . 95, TLI =. 93, RMSEA = . 07, SRMR = . 05. The results suggest that acculturation among Puerto Ricans living in Central Florida is best conceptualized as a bilinear phenomenon in which individuals simultaneously adhere to European American and Puerto Rican culture. Within both cultural orientations, there seems to be related yet distinct values, behavioral, and ethnic identity dimensions. Puerto Rican behaviors, values, and ethnic identity domains were negatively related to European American behaviors and ethnic identity. INDEX WORDS: Acculturation, Puerto Ricans, culture, values, ethnic identity, confirmatory factor analysis A BILINEAR AND THREE-DIMENSIONAL MODEL OF ACCULTURATION FOR PUERTO RICANS LIVING IN CENTRAL FLORIDA
The Effect of Border Noise on the Performance of Projection-Based Page Segmentation Methods

Projection methods have been used in the analysis of bitonal document images for different tasks such as page segmentation and skew correction for more than two decades. However, these algorithms are sensitive to the presence of border noise in document images. Border noise can appear along the page border due to scanning or photocopying. Over the years, several page segmentation algorithms have been proposed in the literature. Some of these algorithms have come into widespread use due to their high accuracy and robustness with respect to border noise. This paper addresses two important questions in this context: 1) Can existing border noise removal algorithms clean up document images to a degree required by projection methods to achieve competitive performance? 2) Can projection methods reach the performance of other state-of-the-art page segmentation algorithms (e.g., Docstrum or Voronoi) for documents where border noise has successfully been removed? We perform extensive experiments on the University of Washington (UW-III) data set with six border noise removal methods. Our results show that although projection methods can achieve the accuracy of other state-of-the-art algorithms on the cleaned document images, existing border noise removal techniques cannot clean up documents captured under a variety of scanning conditions to the degree required to achieve that accuracy.
A Packaged MEMS-Based 5-bit  $X$ -Band High-Pass/Low-Pass Phase Shifter

This paper demonstrates the first packaged microelectromechanical systems-based phase shifter using high-pass/low-pass circuit topology. The device was fabricated with a process resembling available low-cost commercial Si and SiGe processes, and demonstrates an average loss of 4.5 dB with an rms phase error better than 10deg for 8-12 GHz. Thermal compression bonding is used to package the phase shifter with hermeticity confirmed by Military Standard 883G, Method 1014.12. The total shifter area is 9.2 mm2. Performance is compared to and found competitive with phase shifters using more expensive GaAs and BST technologies, which are less easily used in system-on-chip applications.
Analysis of Interest Rate Curves Clustering Using Self-Organising Maps

The paper presents the analysis of temporal evolution of interest rate curves (IRC). IRC are considered as objects embedded in a high-dimensional space composed of 13 different maturities. The objective of the analysis was to apply a nonlinear nonparametric tool (Self-Organising Maps) to study the clustering of IRC in three different representations: the original curves, the increments and 3-parametric Nelson-Siegel model. An important finding of this study reveals the temporal clustering of IRC behaviour which is related to the market evolution. Other results include the relative analysis of CHF-EUR evolution and the clustering found in the evolution of factors used by Nelson-Siegel model. The analysis of the consistency of these factors to represent the typical IRC behaviour requires further work. Current results are useful for the development of interest rates forecasting models and financial risk management.
Discussion of Comparing Predictive Accuracy , Twenty Years Later : A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests by F . X . Diebold Discussion

This interesting article provides an insiders view of the early days of the modernforecast comparison literature, which might be dated as beginning with Diebold and Mariano (1995) and West (1996), papers which spawned numerous extensions and re nements over the subsequent years.1 The article also reviews how forecast evaluation methods have been employed in the literature, o¤ering some criticism of the use of split-sample forecast comparison techniques (like the DieboldMariano test) when applied to model comparison rather than forecast comparison. My discussion is a personal perspective on a personal perspective on the DM test, and it is perhaps a bold move to debate the DM test with D,but below I will do so. The key ingredient in a DM test is the loss di¤erential:
Incorporating extrinsic object properties in robotic semantic mapping

Most robot semantic mapping methods only consider the intrinsic properties of landmarks and objects inside a scene, by detecting them with their appearances, and some other methods include extrinsic properties with manually designed object relations. In this work, we use relational operators to capture the extrinsic property values, and adopt conditional random field to integrate intrinsic and extrinsic property values into semantic mapping. We compare our approach with three types of semantic maps, and show that our approach allows the robot to find designated objects more accurately.
Facial Expression Recognition using Gabor Wavelet

expression recognition (FER) has good applications in different aspects of day-to-day life. But not yet realized due to unavailability of effective expression recognition techniques. This paper discusses the application of Gabor filter based feature extraction by using feed-forward neural networks (classifier) for recognition of four different facial expressions from still pictures of the human face. The study presented here gives simple method in facial expression recognition. The study presented here gives 72.50% recognition of facial expression for the entire database of JAFEE. In this study the Japanese Female Facial Expression (JAFFE) database used which contains expressers that expressed expressions. KeywordsGABOR, FER, JAFFE.
Implementation of a Memory Disclosure Attack on Memory Deduplication of Virtual Machines

SUMMARY Memory deduplication improves the utilization of physical memory by sharing identical blocks of data. Although memory deduplication is most effective when many virtual machines with same operating systems run on a CPU, cross-user memory deduplication is a covert channel and causes serious memory disclosure attack. It reveals the existence of an application or file on another virtual machine. The covert channel is a difference in write access time on deduplicated memory pages that are re-created by Copy-On-Write, but it has some interferences caused by execution environments. This paper indicates that the attack includes implementation issues caused by memory alignment, self-reflection between page cache and heap, and run-time modification (swap-out, anonymous pages, ASLR, preloading mechanism, and self-modification code). However, these problems are avoidable with some techniques. In our experience on KSM (kernel samepage merging) with the KVM virtual machine, the attack could detect the security level of attacked operating systems, find vulnerable applications, and confirm the status of attacked applications.
High order statistics for robust speech/non-speech detection

In noisy environments, a robust speech/non-speech detection is necessary for speech recognition. This paper presents a new method for speech/non-speech detection using third-order moments. The analysis of the energy third-order moment behaviour gives useful information on energy distribution. The new algorithm is compared to the one based on noise and speech statistics presented in [5]. The results show that the new algorithm outperforms the one based on noise and speech statistics only, especially in the case of noisy environments.
Influence of branch tunnels on CO diffusion simulation of main underground powerhouse construction ventilation

The airflow field and CO migration during the construction ventilation of underground powerhouse, taking into account influences of the branch tunnel and the main transformer chamber, are simulated using a 3-D transient single phase model. The effect of branch tunnel on the migration of airflow field and CO is analyzed. The results show that different positions of branch tunnels have different effect on the distribution of airflow field and CO, but they only exit nearby the branch tunnel.
Liquidity Risk, Leverage and Long-Run IPO Returns

We examine the risk-return characteristics of a rolling portfolio investment strategy where more than six thousand Nasdaq initial public offering (IPO) stocks are bought and held for up to five years. The average long-run portfolio return is low, but IPO stocks appear as ‘longshots’, as five-year buy-and-hold returns of 1000% or more are somewhat more frequent than for non-issuing Nasdaq firms matched on size and book-to-market ratio. The typical IPO firm is of average Nasdaq market capitalization but has relatively low book-to-market ratio. We also show that IPO firms exhibit relatively high stock turnover and low leverage, which may lower systematic risk exposures. To examine this possibility, we launch an easily constructed ‘low minus high’ (LMH) stock turnover portfolio as a liquidity risk factor. The LMH factor produces significant betas for broad-based stock portfolios, as well as for our IPO portfolio and a comparison portfolio of seasoned equity offerings. The factor-model estimation also includes standard characteristics-based risk factors, and we explore mimicking portfolios for leverage-related macroeconomic risks. Because they track macroeconomic aggregates, these mimicking portfolios are relatively immune to market sentiment effects. Overall, we cannot reject the hypothesis that the realized return on the IPO portfolio is commensurable with the portfolio’s risk exposures, as defined here.
Intelligent AP selection for indoor positioning in wireless local area network

Indoor positioning system in wireless local area network (WLAN) has been receiving increasing interest in pervasive computing applications. To keep balance between energy consumption on client device and positioning accuracy, AP selection strategy is always proposed to select the most discriminant APs for positioning. In this paper, we propose an intelligent AP selection method based on joint location information gain. In contrast to traditional AP selection methods which measure the discriminant ability of APs independently, we consider it jointly. By considering the correlation of the discriminant ability between different APs, more accurate measure of the discriminant ability can be taken. Besides, support vector regression (SVR) positioning algorithm is combined to estimate the location. Experiments are carried in a realistic WLAN indoor environment. Experimental results show that, by using the intelligent AP selection method, the proposed positioning algorithm maintains a high-level accuracy while reducing the energy consumption on client device significantly.
Semantic Mapping for Lexical Sparseness Reduction in Parsing

Bilexical information is known to be helpful in parse disambiguation, but the benefit is limited because of lexical sparseness. An approach us- ing word classes can reduce sparseness and po- tentially leads to more accurate parsing. Firstly, we describe a method identifying the depen- dency types of the Alpino parser for Dutch to which we would like to apply generaliza- tion. These are the types which are most likely to reduce the sparseness and positively affect parsing at the same time. Secondly, we provide preliminary results for enhancement of depen- dency types with semantic classes derived from a WordNet-like inventory for Dutch. Classes of varying degrees of generality are applied to three dependency types: nominal conjunc- tion, modification of adjective and modification of noun. We observe improvements in some concrete cases, whereas the overall parsing ac- curacy either remains unchanged or decreases. We identify drawbacks of human-built sense inventories, which provides motivation for a distributional semantic approach.
Fast multilevel methods for Markov chains

This paper describes multilevel methods for the calculation of the stationary probability vector of large, sparse, irreducible Markov chains. In particular, several recently proposed significant improvements to the multilevel aggregation method of Horton and Leutenegger are described and compared. Furthermore, we propose a very simple improvement of that method using an over‐correction mechanism. We also compare with more traditional iterative methods for Markov chains such as weighted Jacobi, two‐level aggregation/disaggregation, and preconditioned stabilized biconjugate gradient and generalized minimal residual method. Numerical experiments confirm that our improvements lead to significant speedup, and result in multilevel methods that are competitive with leading iterative solvers for Markov chains. Copyright © 2011 John Wiley & Sons, Ltd.
High-quality pseudo-random sequence generator based on one-dimensional extended cellular automata

Based on the basic theory of cellular automata (CA), using genetic algorithm to find the rules of CA, one-dimensional extended non-uniform CA is constructed to generate high quality random bit sequences. A stringent test of randomness is applied to the bit sequences. The results show that our extended CA generator outperforms other generators such as Shift Register Generator, Congruential Generator and Lagged Fibonacci Generator. This generator can meet the requirement of the security for cryptographic modules and provide a new method to generate pseudo-random sequences in cryptography.
Relative Efficiency of Third-Grade General Public Hospitals in Zhejiang Province, China

Bcakground: New Medical Reform was initiated from 2009 in China, of which public hospital reform is one of the core content. Analyzing the relative efficiency of Tertiary public hospital contributes to identify how to achieve their maximum outputs under the given inputs. Methods: Data envelopment approach and Malmquist index was used in this study. The input variables we used were the number of employees and the actual number of open beds, the output variables were the number of patient discharge and the number of the outpatients (included emergency visits). Results: According to the output-oriented DEA models, efficiency value of Traditional Chinese medicine hospital is 0.83, the western medicine hospital is 0.77. Economies scale trend of two types of hospitals has statistically significant difference. Malmquist index of productivity change of two types of hospitals improved because of the technological innovation, but the mean scale-change has decreased. Conclusion: It appears that the expected benefits from Phased health system reform in China have been partially achieved. Health policy shall strictly control scale of large hospitals, take different policy for western medicine and traditional Chinese medicine hospitals, facilitate extensive development change to connotative development, and boost quality management and delicacy management so that realize sustainable development.
Learning Diphone-Based Segmentation

This paper reconsiders the diphone-based word segmentation model of Cairns, Shillcock, Chater, and Levy (1997) and Hockema (2006), previously thought to be unlearnable. A statistically principled learning model is developed using Bayes' theorem and reasonable assumptions about infants' implicit knowledge. The ability to recover phrase-medial word boundaries is tested using phonetic corpora derived from spontaneous interactions with children and adults. The (unsupervised and semi-supervised) learning models are shown to exhibit several crucial properties. First, only a small amount of language exposure is required to achieve the model's ceiling performance, equivalent to between 1 day and 1 month of caregiver input. Second, the models are robust to variation, both in the free parameter and the input representation. Finally, both the learning and baseline models exhibit undersegmentation, argued to have significant ramifications for speech processing as a whole.
Study of the TCP Unfairness in a Wireless Environment 1

In this paper we analyze the TCP performance degradation introduced by a wireless media having transmission errors and the capability of the following mechanisms to correct it (i) improvements over the TCP Reno release (SACK, New-Reno and FACK); (ii) improvements over the classical “tail dropping” mechanism used by routers (RED and ECN); and (iii) a TCP implementation based on the usage of ECN routers that would be able to distinguish among losses due to congestion and losses due to transmission errors. Furthermore, we have analyzed the unfairness that occurs when TCP sources having high transmission error rates compete with sources having low transmission error rates.
Regret-based optimal recommendation sets in conversational recommender systems

Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an approach to recommender systems that incorporates an explicit utility model into the recommendation process in a decision-theoretically sound fashion. The system maintains explicit constraints on user utility based on preferences revealed by the user's actions. We investigate a new decision criterion, setwise minimax regret (SMR), for constructing optimal recommendation sets: we develop algorithms for computing SMR, and prove that SMR determines choice sets for queries that are myopically optimal. This provides a natural basis for generating compound critiques in conversational recommender systems. Our simulation results suggest that this utility-theoretically sound approach to user modeling allows much more effective navigation of a product space than traditional approaches based on, for example, heuristic utility models and product similarity measures.
novel flow battery : A lead acid battery based on an electrolyte with soluble ead ( II ) Part VIII . The cycling of a 10 cm × 10 cm flow cell

The design of a 10 cm × 10 cm flow cell for the soluble lead acid flow battery is described. A number of extended charge/discharge cycling experiments are presented to demonstrate the capability of the battery to cycle over lengthy periods and to identify the problems that limit the number of cycles that can be achieved. A charge efficiency below 100%, leading to a build up of deposits on both electrodes and a consequent drop in the concentration of Pb2+ in the electrolyte are found to limit cycle life. TE eywords: low battery ead(II) ions ead ead dioxide © 2009 Published by Elsevier B.V.