Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive lossless image coding using least squares optimization with edge-look-ahead

In predictive image coding, the least squares (LS)-based adaptive predictor is noted as an efficient method to improve prediction result around edges. However pixel-by-pixel optimization of the predictor coefficients leads to a high coding complexity. To reduce computational complexity, we activate the LS optimization process only when the coding pixel is around an edge or when the prediction error is large. We propose a simple yet effective edge detector using only causal pixels. The system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation to prevent the occurrence of a large prediction error. Our experiments show that the proposed approach can achieve a noticeable reduction in complexity with only a minor degradation in the prediction results.
TOPICAL REVIEW: Theory of nonlinear charge transport, wave propagation, and self-oscillations in semiconductor superlattices

Nonlinear charge transport in semiconductor superlattices under strong electric fields parallel to the growth direction results in rich dynamical behaviour including the formation of electric field domains, pinning or propagation of domain walls, self-sustained oscillations of the current and chaos. Theories of these effects use reduced descriptions of transport in terms of average charge densities, electric fields, etc. This is simpler when the main transport mechanism is resonant tunnelling of electrons between adjacent wells followed by fast scattering between subbands. In this case, we will derive microscopically appropriate discrete models and boundary conditions. Their analyses reveal differences between low-field behaviour where domain walls may move oppositely or parallel to electrons, and high-field behaviour where they can only follow the electron flow. The dynamics is controlled by the amount of charge available in the superlattice and doping at the injecting contact. When the charge inside the wells becomes large, boundaries between electric field domains are pinned resulting in multistable stationary solutions. Lower charge inside the wells results in self-sustained oscillations of the current due to recycling and motion of domain walls, which are formed by charge monopoles (high contact doping) or dipoles (low contact doping). Besides explaining wave motion and subsequent current oscillations, we will show how the latter depend on such controlling parameters as voltage, doping, temperature, and photoexcitation.
An Empirical Study on Thematic Knowledge Acquisition Based on Syntactic Clues and Heuristics

Thematic knowledge is a basis of semantic interpretation. In this paper, we propose an acquisition method to acquire thematic knowledge by exploiting syntactic clues from training sentences. The syntactic clues, which may be easily collected by most existing syntactic processors, reduce the hypothesis space of the thematic roles. The ambiguities may be further resolved by the evidences either from a trainer or from a large corpus. A set of heuristics based on linguistic constraints is employed to guide the ambiguity resolution process. When a trainer is available, the system generates new sentences whose thematic validities can be justified by the trainer. When a large corpus is available, the thematic validity may be justified by observing the sentences in the corpus. Using this way, a syntactic processor may become a thematic recognizer by simply deriving its thematic knowledge from its own syntactic knowledge.
Case-Based Study of Interorganizational Information Systems Implementation Critical Success Factors

With the rapid diffusion of interorganizational information systems (IOS) as a strategic tool, enterprise concerns more about the IOS implementation critical success factors (CSFs) to gain the maximum benefit from it. However, previous researches have put little energy into the research of IOS implementation CSFs. This paper proposes a three-level model of IOS implementation CSFs with seven sub-factors in total. Supported by a real case study, this paper illustrates each factor in detail from both the proactive and reactive enterprise viewpoint, providing useful recommendations for practice.
Modeling Document Dynamics: an Evolutionary Approach

News articles about the same event published over time have properties that challenge NLP and IR applications. A cluster of such texts typically exhibits instances of paraphrase and contradiction, as sources update the facts surrounding the story, often due to an ongoing investigation. The current hypothesis is that the stories evolve over time, beginning with the first text published on a given topic. This is tested using a phylogenetic approach as well as one based on language modeling. The fit of the evolutionary models is evaluated with respect to how well they facilitate the recovery of chronological relationships between the documents. Over all data clusters, the language modeling approach consistently outperforms the phylogenetics model. However, on manually collected clusters in which the documents are published within short time spans of one another, both have a similar performance, and produce statistically significant results on the document chronology recovery evaluation.
A visual mapping approach for trend identification in multi-attribute data

Organizations and firms are increasingly capturing more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multi-dimensional) and temporal in nature. Data mining and business intelligence techniques are typically used to discover patterns in this data; however, mining meaningful temporal relationships is often difficult. We introduce a new temporal data analysis and visualization technique for representing trends in multi-attribute temporal data using a clustering-based approach. We define a new analytical construct called the temporal cluster graph which maps multi-attribute temporal data into a two-dimensional trend graph that clearly identifies trends in dominant data types over time. We also present C-TREND, a system that implements the proposed technique, and demonstrate applications of technique by analyzing the change in technical characteristics of wireless networking technologies over a six year period.
Caching Files with a Program-based Last N Successors Model

Recent increases in CPU performance have outpaced increases in hard drive performance. As a result, disk operations have become more expensive in terms of CPU cycles spent waiting for disk operations to complete. File prediction can mitigate this problem by prefetching files into cache before they are accessed. However, incorrect prediction is to a certain degree both unavoidable and costly. We present the Program-based Last N Successors (PLNS) file prediction model that identifies relationships between files through the names of the programs accessing them. Our simulation results show that PLNS makes at least 21.11% fewer incorrect predictions and roughly the same number of correct predictions as the last-successor model. We also examine the cache hit ratio of applying PLNS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLNS and LRU together can perform as well as a cache up to 40 times larger using LRU alone.
Mining query subtopics from search log data

Most queries in web search are ambiguous and multifaceted. Identifying the major senses and facets of queries from search log data, referred to as query subtopic mining in this paper, is a very important issue in web search. Through search log analysis, we show that there are two interesting phenomena of user behavior that can be leveraged to identify query subtopics, referred to as `one subtopic per search' and `subtopic clarification by keyword'. One subtopic per search means that if a user clicks multiple URLs in one query, then the clicked URLs tend to represent the same sense or facet. Subtopic clarification by keyword means that users often add an additional keyword or keywords to expand the query in order to clarify their search intent. Thus, the keywords tend to be indicative of the sense or facet. We propose a clustering algorithm that can effectively leverage the two phenomena to automatically mine the major subtopics of queries, where each subtopic is represented by a cluster containing a number of URLs and keywords. The mined subtopics of queries can be used in multiple tasks in web search and we evaluate them in aspects of the search result presentation such as clustering and re-ranking. We demonstrate that our clustering algorithm can effectively mine query subtopics with an F1 measure in the range of 0.896-0.956. Our experimental results show that the use of the subtopics mined by our approach can significantly improve the state-of-the-art methods used for search result clustering. Experimental results based on click data also show that the re-ranking of search result based on our method can significantly improve the efficiency of users' ability to find information.
Inducible Mouse Models for Cancer Drug Target Validation

Genetically-engineered mouse (GEM) models have provided significant contributions to our understanding of cancer biology and developing anticancer therapeutic strategies. The development of GEM models that faithfully recapitulate histopathological and clinical features of human cancers is one of the most pressing needs to successfully conquer cancer. In particular, doxycycline-inducible transgenic mouse models allow us to regulate (induce or suppress) the expression of a specific gene of interest within a specific tissue in a temporal manner. Leveraging this mouse model system, we can determine whether the transgene expression is required for tumor maintenance, thereby validating the transgene product as a target for anticancer drug development (target validation study). In addition, there is always a risk of tumor recurrence with cancer therapy. By analyzing recurrent tumors derived from fully regressed tumors after turning off transgene expression in tumor-bearing mice, we can gain an insight into the molecular basis of how tumor cells escape from their dependence on the transgene (tumor recurrence study). Results from such studies will ultimately allow us to predict therapeutic responses in clinical settings and develop new therapeutic strategies against recurrent tumors. The aim of this review is to highlight the significance of doxycycline-inducible transgenic mouse models in studying target validation and tumor recurrence.
iPDA: An integrity-protecting private data aggregation scheme for wireless sensor networks

Data aggregation is an efficient mechanism widely used in wireless sensor networks (WSN) to collect statistics about data of interests. However, the shared-medium nature of communication makes the WSNs are vulnerable to eavesdropping and packet tampering/injection by adversaries. Hence, how to protect data privacy and data integrity are two major challenges for data aggregation in wireless sensor networks. In this paper, we present iPDA- an integrity-protecting private data aggregation scheme. In iPDA, data privacy is achieved through data slicing and assembling technique; and data integrity is achieved through redundancy by constructing disjoint aggregation paths/trees to collect data of interests. In iPDA, the data integrity-protection and data privacy-preservation mechanisms work synergistically. We evaluate the performance of iPDA scheme in terms of communication overhead and data aggregation accuracy, comparing with a typical data aggregation scheme - TAG, where no integrity protection and privacy preservation is provided. Simulation results show that iPDA achieves the design goals while still maintains the efficiency of data aggregation.
Solution-processable organic-inorganic hybrid transparent electrode for optoelectronic applications

ii Acknowledgements . v Abbreviations . vi List of Figures . viii List of Tables . xvii Chapter 1: Introduction . 1 1.1. Background and motivations . 1 1.2. Solution processable optoelectronic devices: opportunities and challenges . 4 1.2.1. Quantum dot light emitting diodes . 4 1.2.2. Organic solar cells . 7 1.3. Thesis objectives . 10 1.4 Organization of this thesis . 11 Chapter 2: Recent progress in solution processable transparent electrodes and interfacial modification layers . 14 2.1 Solution processable transparent conducting materials . 15 2.1.1 Graphene nanosheets . 15 2.1.2 Carbon nanotubes . 17 2.1.3 Conductive polymers . 18 2.1.4 Silver nanowires . 20 2.2 Overview of anode and cathode interfacial layers . 22
Query processing in a system for distributed databases (SDD-1)

This paper describes the techniques used to optimize relational queries in the SDD-1 distributed database system. Queries are submitted to SDD-1 in a high-level procedural language called Datalanguage. Optimization begins by translating each Datalanguage query into a relational calculus form called an envelope, which is essentially an aggregate-free QUEL query. This paper is primarily concerned with the optimization of envelopes. Envelopes are processed in two phases. The first phase executes relational operations at various sites of the distributed database in order to delimit a subset of the database that contains all data relevant to the envelope. This subset is called a reduction of the database. The second phase transmits the reduction to one designated site, and the query is executed locally at that site. The critical optimization problem is to perform the reduction phase efficiently. Success depends on designing a good repertoire of operators to use during this phase, and an effective algorithm for deciding which of these operators to use in processing a given envelope against a given database. The principal reduction operator that we employ is called a semijoin. In this paper we define the semijoin operator, explain why semijoin is an effective reduction operator, and present an algorithm that constructs a cost-effective program of semijoins, given an envelope and a database.
Prior expectations in pedagogical situations

Much of human learning occurs in social situations, and among these, pedagogical situations may afford the most powerful learning. In pedagogical situations, a teacher chooses the concept that they are going to teach and the examples that they use to teach the concept. If learners know that a teacher is helpful and understands the implications, this could support strong inferences. In previous work, Shafto and Goodman (2008) proposed and tested a model of pedagogical data selection. We integrate special-purpose pedagogical expectations in this framework, and derive a task that allows independent assessment of pedagogical expectations. Two experiments contrast people’s expectations about pedagogical and communicative situations. The results show that people’s expectations differ in these situations, and that in pedagogical situations people expect teachers to present generalizable and semantically coherent knowledge. We discuss the implications for modeling learning in pedagogical settings, as well as for understanding human learning more broadly.
RSS Self-calibration Protocol for WSN Localization

In wireless sensor network (WSN) localization system, received signal strength (RSS) is one of the simplest ranging methods. However, in real situation, a RSS is affected by some errors which are caused by multi-path channel and dynamic environment. In this paper, we propose RSS self-calibration protocol to attack the errors. The RSS self-calibration protocol can adopt the calibration parameters according to environment as time goes by
Efficient verifiable fuzzy keyword search over encrypted data in cloud computing

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud by users. To maintain the confidentiality of sensitive user data against untrusted servers, the data should be encrypted before they are uploaded. However, this raises a new challenge for performing search over the encrypted data efficiently. Although the existing searchable encryption schemes allow a user to search the encrypted data with confidentiality, these solutions cannot support the verifiability of searching result. We argue that a cloud server may be selfish in order to save its computation ability or bandwidth. For example, it may execute only a fraction of the search and returns part of the searching result. In this paper, we propose a new verifiable fuzzy keyword search scheme based on the symbol-tree which not only supports the fuzzy keyword search, but also enjoys the verifiability of the searching result. Through rigorous security and efficiency analysis, we show that our proposed scheme is secure under the proposed model, while correctly and efficiently realizing the verifiable fuzzy keyword search. The extensive experimental results demonstrate the efficiency of the proposed scheme.
A multi-functional MEMS-SPM for quantitative characterization of nano-objects

Based upon the micro-fabrication technology, a series of MEMS scanning probe microscopes (MEMS-SPM) have been developed in the national metrology institute Physikalisch-Technische Bundesanstalt (PTB) in Braunschweig. In comparison with those traditional AFMs, the MEMS-SPM features generally a vertical deflection up to 10 μm with a resolution of 0.2 nm, a non-linearity less than 0.03%, and a testing force up to several hundreds of μN with a force resolution down to 1 nN by means of a capacitive displacement sensing technique. As a result, these MEMS-SPMs can be successfully applied in the field of nanodimensional and nanomechanical metrology. Mechanical design of the MEMS-SPM is reported in this manuscript. Proof-of-principle measurements using a prototype of the MEMS-SPM are detailed in this manuscript, verifying the capabilities of the MEMS-SPM.
From WAP Services to Mobile Video: Trends in Mobile Services in Finland

For a long time Finland was seen as the most successful country in Europe in terms of the adoption and use of advanced mobile services. This paper discusses trends in service adoption. We discuss the development in the Finnish telecommunication market and trends in actual and future user behaviour with regard to three groups of mobile services. Based on survey's from 2004, 2005 and 2006, in which the same concepts were measured, we test models in which concepts as innovativeness, flexibility, status and entertainment benefits are related to mobile travel service, traditional entertainment and mobile Internet services. Our analysis based on the data we collected over three years show that we cannot understand the actual and future use unless we take the subtleties and usability of specific services, i. e. the characteristics and user values of the services involved, into account.
A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the higher order singular value decomposition (HOSVD) method and the kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.
How Good is the Bankscope Database? A Cross-Validation Exercise with Correction Factors for Market Concentration Measures

The paper examines the quality of the BankScope database, by comparing results based on it to those obtained from the population-level data for India disseminated by the Reserve Bank of India. Despite good coverage and minor reporting errors in the individual reported units, strong evidence of selectivity bias in BankScope data for India is found. A major source of the selectivity bias for India is the almost total omission of Regional Rural Banks and Foreign Banks. It is shown that this selectivity bias affects estimates of all summary statistical measures and could lead users of the data to conclude that the Indian banking market is unimodal when, in reality, it is segmented and has a bimodal pattern. Kolmogorov-Smironov tests reveal that neither the distribution of the log of total assets nor that of market shares based on the BankScope data could be treated same as the corresponding population distributions for India. Despite these limitations, the paper shows that a few popularly used market concentration measures could be estimated from BankScope data accurately, provided the coverage ratio with respect to the size variable is known from alternative sources and is adequate. Coverage of about 90% with respect to the size variable is found to be sufficient for approximating population HHI. For k-bank concentration measures, accurate estimates could be obtained if, in addition, the top k banks in the population are also available in the sample. In contrast, for entropy measures, results indicate that adequate coverage with respect to both the size variable and the number of financial entities would be required.
A k‐nearest‐neighbor simulator for daily precipitation and other weather variables

A multivariate, nonparametric time series simulation method is provided to generate random sequences of daily weather variables that “honor” the statistical properties of the historical data of the same weather variables at the site. A vector of weather variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, average wind speed, and precipitation) on a day of interest is resampled from the historical data by conditioning on the vector of the same variables (feature vector) on the preceding day. The resampling is done from the k nearest neighbors in state space of the feature vector using a weight function. This approach is equivalent to a nonparametric approximation of a multivariate, lag 1 Markov process. It does not require prior assumptions as to the form of the joint probability density function of the variables. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, is provided. Results are compared with those from the application of a multivariate autoregressive model similar to that of Richardson [1981].