Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth States and Shocks

GDP growth typically vibrates with modest variation around a mean of a few percent per year, but periodically, mean growth undergoes a major shift, vibrating thereafter around a new level. I present a transmission mechanism with nonlinear dynamics that endogenously translates random sectoral shocks into just this sort of behavior, creating what might be thought of as multiple growth states. Small shocks cause vibration within a state. Sufficiently large shocks cause a state change. Behind the nonlinear dynamics lies essentially the same dynamic externality that drives the new-growth theory models. The relative balance of endogenous versus exogenous growth determines whether the economy will have multiple stable growth states. The model can generate data which looks much like the data generated by a Markov process of the sort identified in Hamilton (1989) or a trend-breaking process as in Perron (1989). The model has two output processes. Input factors are drawn into the leading process, where learning-by-doing further increases that process's technological lead. If the leading process is also the inherently high-growth process, then growth is fast for both technologies. Shocks to preferences and technologies cause endogenous switching of the leading sector role between the high-growth and slow-growth processes.
Implementing an XML based Unified Knowledge Manipulation Languages for SOAR cognitive architecture

SOAR is a major exemplar of architectural approach to machine cognition with numerous applications including speech recognition, machine perception, robotic, and strategy planning. We have replaced language used in SOAR architecture with our own developed XML based unified knowledge manipulation language (UKML) to demonstrate this language as a shared platform for procedural knowledge representation in cognitive architectures, and enhance SOAR with some valuable new features including the ability of manipulating XML formatted factual knowledge, greatly improved code readability and organization, and elimination of some ambiguities of Soar traditional language. This paper is a report on this implementation of UMKL, enriched with some examples and their results.
ONVERSION P ROCEDURES AND THE I NTERACTION OF P HONOLOGICAL AND O RTHOGRAPHIC L EXICAL F ORMS

In double naming tasks requiring the production of consecutive spoken and written responses to the same picture, subject ECA produced inconsistent lexical responses in the say-then-write (stimulus: organ; spoken response: “church;” written response: piano) but not in the write-then-say condition (organ ® piano ® “piano”). This observation, together with the fact that ECA had damage to the semantic system and to sublexical phoneme-grapheme conversion but not to sublexical grapheme-phoneme conversion procedures, is used to constrain claims about the organisation of lexical form knowledge. It is proposed that phonological and orthographic lexical forms are accessed autonomously, but interact via sublexical conversion procedures. In ECA, the one-way interaction between phonological and orthographic word forms is prevented by damage to phonemegrapheme procedures (hence, inconsistent responses in the say-then-write naming condition); the reverse interaction can take place because grapheme-phoneme conversion processes are spared (hence, the absence of inconsistent responses in the write-then-say naming condition).
MMSE-Based Non-Regenerative Parallel MIMO Relaying with Simplified Receiver

This paper considers a cooperative MIMO relaying system in which the source sends information to the destination with the aid of multiple relays, each equipped with multiple antennas. We design optimal linear relaying matrices that minimize the mean square error between the source and the received signal vectors under a total transmitted power constraint imposed on all the relays. The optimal matrices are obtained by solving the corresponding Karush-Kuhn-Tucker (KKT) conditions. Simulation results show significant advantages of the newly designed relaying matrices over other competing approaches in terms of bit-error rate (BER) performance. Moreover, this strategy enables the self- demultiplexing of the spatial substreams without the need for a MIMO combiner at the destination, so that a simplified receiver structure can be used without performance loss. In addition, the new design can serve as a suboptimal solution for multiuser MIMO relaying applications.
A Framework for the Design of the Heterogeneous Hierarchical Routing Architecture of a Dynamically Reconfigurable Application Specific Media Processor

We have recently proposed a tool set that will aid the design of a dynamically reconfigurable processor through the use of a set of analysis and design tools. As part of the tool set, in this paper we propose a heterogeneous hierarchical routing architecture. Compared to hierarchical and symmetrical FPGA approaches building blocks are of variable size. This results in heterogeneity between groups of building blocks at the same hierarchy level as opposed to classical H-FPGA approach. In this paper we also define the methodology for the design and implementation of the proposed architecture, which involves packing, hierarchy formation, placement, network scheduler tools.
MULTIPLE RHYTHMS AND SWITCHES IN THE NERVOUS SYSTEM

Networks of neurons in the nervous system can produce a variety of temporal patterns of difierent frequency; the same network can produce difierent rhythms at difierent times, or several rhythms at one time. We focus here on the gamma (30-90 Hz) and theta (4-12 Hz) rhythms produced in the hippocampus, a part of the nervous system critical for learning and recall. We discuss experiments and models that suggest that there are separate subnetworks that produce the difierent rhythms; the sharing of components of the networks induces competition between the rhythms, which can lead to suppression of one of the rhythms, or nesting of the rhythms. We show how low-dimensional maps can help to understand the properties of the cells and networks to allow this to happen.
Antigenic variation in malaria: in situ switching, relaxed and mutually exclusive transcription of var genes during intra‐erythrocytic development in Plasmodium falciparum

Members of the Plasmodium falciparum var gene family encode clonally variant adhesins, which play an important role in the pathogenicity of tropical malaria. Here we employ a selective panning protocol to generate isogenic P.falciparum populations with defined adhesive phenotypes for CD36, ICAM‐1 and CSA, expressing single and distinct var gene variants. This technique has established the framework for examining var gene expression, its regulation and switching. It was found that var gene switching occurs in situ. Ubiquitous transcription of all var gene variants appears to occur in early ring stages. However, var gene expression is tightly regulated in trophozoites and is exerted through a silencing mechanism. Transcriptional control is mutually exclusive in parasites that express defined adhesive phenotypes. In situ var gene switching is apparently mediated at the level of transcriptional initiation, as demonstrated by nuclear run‐on analyses. Our results suggest that an epigenetic mechanism(s) is involved in var gene regulation.
Modeling Graduality in Computerized Research on Synonyms

This paper deals with the problem of synonymy in linguistics and tries to bring a solution integrating the two notions of graduality and point of view in the automatic research on synonyms. The renunciation of the strict definition of synonymy which consists in interchanging two terms in all contexts allows us to sketch out a solution considering only the synonyms with respect to a given point of view. The dictionary of synonyms, as we conceive it, is a domain-structured dictionary. In fact, it is obvious that a user who is looking for the synonyms of a word, already knows the domain, and wants to find the synonyms of this word precisely in this domain. Our system constructs a "Domain-Fields-Taxemes" tree as a function of differential sema given by the expert. The leaves of this tree are taxemes and contain some words which, may be considered as equivalent with respect to one or several points of view. In this way, we can avoid the often circular and infinite lists used in the current synonym dictionaries, and which may contain words which have nothing in common with the initial word. Furthermore, the use of many-valued logic has enabled us to present to the user an ordered list of synonyms, with the best synonym at the top of this list.
Research and improvement of TFRC congestion control mechanism

The congestion control mechanism TFRC, which is widely used in current real-time streaming media, is analyzed in this paper. A proposal aiming at the requirement for smoothly change of transmit rate as well as short delay and jitter of real-time streaming media has been made that adjusting the transmit rate of the sender to the change of single trip delay jitter. In the meanwhile, a dynamic adaptive method is adopted in the determination of the jitter threshold. The result of simulation indicates that with the improved algorithm the related QoS has been improved.
The Disclosure and Diffusion of Security Information

With the nearly instantaneous dissemination of information in the modern era, policies regarding the disclosure of sensitive information have become the focus of significant discussion in several contexts. The fundamental debate centers on tradeoffs inherent in disclosing information that society needs, but that can also be used for nefarious purposes. Using information security as a research context, our empirical study examines the adoption of software vulnerabilities by a population of attackers. We compare attacks based on software vulnerabilities disclosed through full disclosure and limited disclosure mechanisms. We find that full disclosure accelerates the diffusion of attacks, increases the penetration of attacks within the target population, and increases the risk of first attack after the vulnerability is reported. Interestingly, the effect of full disclosure is greater during periods when there are more overall vulnerabilities reported, indicating that attackers may strategically focus on busy periods when the effort of security professionals is spread across many vulnerabilities. Although the aggregate volume of attacks remains unaffected by full disclosure, attacks occur earlier in the life cycle of the vulnerability. Building off our theoretical insights, we discuss the implications of our findings in more general contexts.
Sparse sampling based compensation method for FLAR

A sparse sampling based compensation (SSBC) method is proposed in this letter. The complex amplitudes (CAs) of clutter scatterers, which are larger than the sampling threshold (ST), are sampled along the direction Doppler curve (DDC) of each range cell. Then, the covariance matrix of each range cell is reconstructed by using the sampled clutter scatterers to mitigate the range-dependence problem in the forward-looking airborne radar (FLAR). Finally, the computer simulation results show that the computation burden is reduced considerably by using SSBC method as well as the space-time adaptive processing (STAP) performance of FLAR is kept.
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification

Distinguishing between paradigmatic relations such as synonymy, antonymy and hypernymy is an important prerequisite in a range of NLP applications. In this paper, we explore discourse relations as an alternative set of features to lexico-syntactic patterns. We demonstrate that statistics over discourse relations, collected via explicit discourse markers as proxies, can be utilized as salient indicators for paradigmatic relations in multiple languages, outperforming patterns in terms of recall and F1-score. In addition, we observe that markers and patterns provide complementary information, leading to significant classification improvements when applied in combination.
Optimized Irregular Low-Density Parity-Check Codes for Multicarrier Modulations over Frequency-Selective Channels

This paper deals with optimized channel coding for OFDM transmissions (COFDM) over frequency-selective channels using irregular low-density parity-check (LDPC) codes. Firstly, we introduce a new characterization of the LDPC code irregularity called “irregularity profile. ” Then, using this parameterization, we derive a new criterion based on the minimization of the transmission bit error probability to design an irregular LDPC code suited to the frequency selectivity of the channel. The optimization of this criterion is done using the Gaussian approximation technique. Simulations illustrate the good performance of our approach for different transmission channels.
Tone Features, Tone Perception, and Peak Alignment in Thai

This paper investigates the relationship between the phonological features of tone and tone perception in Thai. Specifically, it tests the hypothesis (proposed by Morén & Zsiga, 2006) that the principle perceptual cues to the five-way tonal contrast in Thai are high and low pitch targets aligned to moras. Results of four perception studies, one using natural speech and three using digitally-altered speech, are presented in support of the hypothesis. It is argued that, by associating tones to moras, a straightforward mapping from the abstract autosegmental features H and L to the production and perception of Thai tones, a heretofore elusive goal, can be accomplished. This result has consequences for theories of contour tone perception, the distinctive features of tone, and the alignment of pitch targets to the segmental string.
Study on the TOPN Abnormal Detection Based on the NetFlow Data Set

In recent years, with the increase of the scale and the complexity of the network, various abnormity flows begin to occur in the network. To know the running state of the network, the technology of NetFlow emerges as the times require. The NetFlow data are transmitted directly by the router which supports the function of NetFlow. Comparing with traditional data acquirement technology, the NetFlow technology needs not deployment in advance and acquires data conveniently, and it is gradually turning into the important data sources for the network management, maintenance, supervision and control. At present, there are still few abnormity detection methods based on the NetFlow data set. In this article, we introduced the principle and functions of the NetFlow, and put forward the TOPN flow abnormity detection method based on the NetFlow technology. This method can effectively detect the flow state in the network, reflect the network state and offer the information about abnormal network flows.
Using Multicriteria Decision Analysis to Support Research Priority Setting in Biomedical Translational Research Projects

Translational research is conducted to achieve a predefined set of economic or societal goals. As a result, investment decisions on where available resources have the highest potential in achieving these goals have to be made. In this paper, we first describe how multicriteria decision analysis can assist in defining the decision context and in ensuring that all relevant aspects of the decision problem are incorporated in the decision making process. We then present the results of a case study to support priority setting in a translational research consortium aimed at reducing the burden of disease of type 2 diabetes. During problem structuring, we identified four research alternatives (primary, secondary, tertiary microvascular, and tertiary macrovascular prevention) and a set of six decision criteria. Scoring of these alternatives against the criteria was done using a combination of expert judgement and previously published data. Lastly, decision analysis was performed using stochastic multicriteria acceptability analysis, which allows for the combined use of numerical and ordinal data. We found that the development of novel techniques applied in secondary prevention would be a poor investment of research funds. The ranking of the remaining alternatives was however strongly dependent on the decision maker's preferences for certain criteria.
e Design of Gaze Behavior for Social Robots

Humanlike robots are designed to communicate using human verbal and nonverbal language and engage in conversations with people where social gaze cues play an important role. While research in human-robot interaction has shown that people understand these cues and interpret them as valid signals for human communication, whether social gaze cues can serve as an effective communicative mechanism that leads to significant social outcomes remains unknown. Furthermore, work in this area lacks theoretical and empirical foundations for the design of communicative mechanisms to work with the human communicative system. e main research questions in this problem space are as follows: What are the design variables for social gaze behavior? How do we design gaze behavior for social robots? Can designed behaviors lead to positive, significant social outcomes in human-robot interaction such as better learning, stronger affiliation, and active participation in conversations? is dissertation seeks to find answers to these questions through (1) exploring the design space to identify design variables for social gaze, (2) adapting an approach based on modeling human behavior to designing robot behaviors, and (3) evaluating the social outcome of designed behaviors in three studies that focus on different functions of social gaze behavior. Study I focused on designing gaze behavior for communication of attention and found strong learning effects induced by a simple manipulation of how much a robot looks at one person more than the other. Study II looked at how different patterns of a robot’s gaze behavior lead to different levels of participation in a conversation with the robot and found strong effects of gaze cues in behavioral and subjective measures of participation, attentiveness, liking, and feelings of groupness. ese studies will be followed by a proposed third study that will look at how cues from a robot’s gaze might be used in establishing joint attention and evaluate how changes in the robot’s gaze at objects affect foreign language learning. is research will contribute to (1) the design of robotic systems with a set of design variables for social gaze and a theoretically and empirically grounded methodology for the design of communicative mechanisms for social technology and (2) human communication research with a deeper understanding of how human communicative mechanisms respond to artificially created social stimuli.
Exploitation and use of social annotations in information search

Today, with the strong growth of the internet, the search service has been developed rapidly, support web users to easily search for their information. However, with the explosion of information is increasingly enormous, how to return search results satisfy the user remains a difficult problem. Currently, many web-based bookmark systems (such as delicous.com) allows users to easily share and organize their favorite web pages online by using social annotations. The goals of this paper are: 1) exploiting social annotations from delicous.com; 2) using the similarity measure (Social Similarity Ranking) and measure the popularity of the web page (SocialPageRank), build search engine to assist users to search quickly and efficiently. Preliminary experimental results show that the social annotations based search results are very positive.
Browsing graphics without prior knowledge

In this paper, we present a system to browse a set of graphical documents without prior knowledge of their content. This system relies on a new structural method locating regions potentially embedding a symbol, using an unsupervised reconstruction of the documents from their representation as chain of points. The method is complemented by a querying mechanism that retrieves from these regions those that are the most similar to a given input symbol. This mechanism uses a specific user feedback scheme to improve the accuracy of the retrieval
PROBABILISTIC CONTEXT-FREE GRAMMARS IN NATURAL LANGUAGE PROCESSING

Context-free grammars (CFGs) are a class of formal grammars that have found numerous applications in modeling computer languages. A probabilistic form of CFG, the probabilistic CFG (PCFG), has also been successfully applied to model natural languages. In this paper, we discuss the use of PCFGs in natural language modeling. We develop PCFGs as a natural extension of the CFGs and explain one probabilistic parser for PCFGs in detail. We also outline two methods that are used for estimating the PCFG probabilities. Finally, we state the limitations of PCFGs and mention how it can be augmented for modeling natural languages better.
Mobile Robot Cooperation with Infrastructure For Surveillance : Towards Cloud Robotics

This paper proposes a cooperative framework among a mobile robot and an infrastructure for the sake of surveillance and identification. The infrastructure is comprised of a network of cameras, inertial sensors, a facial identification system and a GPU-enabled server. The cooperation among the mobile robot and infrastructure is service-based. The scene is observed by camera sensors. There is a mobile robot freely moving within the scene. For some security reasons the system gets interested in identification of a person inside the scene. The infrastructure provides three services to the mobile robot including positions of the person and robot as well as 3D information of the person. In the other sides, the mobile robot provides the facial (portrait) image of a requested person, as a service. Using these services the infrastructure can identify the person. The cameras in the infrastructure and also the camera on the mobile robot are rigidly coupled with an inertial sensor (IS). The inertial data provided by IS is used in two ways: definition of a fusion-based virtual camera and also virtual planes to register 3D data. Taking advantage of the defined virtual camera, a localization method is proposed to obtain the position of the robot with respect to the infrastructure. Experimental results are provided to demonstrate the feasibility and effectiveness of the proposed framework for the purpose of being used in cloud robotics.
Tagging of name records for genealogical data browsing

In this paper we present a method of parsing unstructured textual records briefly describing a person and their direct relatives, which we use in the construction of a browsing tool for genealogical data. The records have been created by researchers who are currently digitising a collection of historical archives stored at the Abbaye de Saint-Maurice, Switzerland. The string `Beatrix, daughter of Johannes Trona, of Saillon' is a typical example of a record. We wish to annotate every term (word and symbol) in our records with a label which describes whether the term is a name (e.g. `Beatrix'), a place (e.g. `Saillon'), or a relationship (e.g. `daughter'). Using this information, we are able to derive both a canonical form for each name (e.g. `Beatrix Trona'), and the relationships between people. We build upon work developed for the cleaning and standardization of names for record linkage corpora, adding several enhancements to deal with our more difficult data, which contains common name structures of French, Italian and Latin, over hundreds of years. We present an approach to this problem that works interactively with a user to annotate the data set accurately, greatly reducing the human effort required. We do this by learning a hidden Markov model representing a record structure, and finding structural patterns in new records. Finally, we present a brief overview of a tool we are developing to help genealogical researchers browse and search the data