Miracle-dz/UniFAR-FTUs huggingface dataset:
"Chinese Philosophy" at European Universities: A Threefold Utopia

Abstract:The question of whether or not Chinese thought can be called "philosophy" has hitherto failed to lead to any conclusion. The futility of rational arguments is related to the academic institutions that shape our views. This article explores European academe, more specifically the History and Philosophy departments of the University of Leuven (Belgium). It argues that the problem lies not only with Philosophy, but also with Europe.
Simple Heuristics That Make Us Smart

Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is about fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions. Available in OSO:
JOpenShowVar: An open-source cross-platform communication interface to Kuka robots

This paper introduces JOpenShowVar, a Java open-source cross-platform communication interface to Kuka robots that allows for reading and writing variables and data structures of the controlled manipulators. This interface, which is compatible with all Kuka robots that use KR C4 and previous versions, runs as a client on a remote computer connected with the Kuka controller via TCP/IP. JOpenShowVar opens up to a variety of possible applications making it possible to use different input devices, sensors and to develop alternative control methods. To show the potential of the proposed interface, two case studies are presented. In the first one, JOpenShowVar is used to control a Kuka KR 6 R900 SIXX (KR AGILUS) robot with an Android mobile device. In the second case study, the same manipulator is controlled with a Leap Motion Controller that supports hand and finger motions as input without requiring contact or touching. Related simulations are carried out to validate efficiency and flexibility of the proposed communication interface.
Multi Parameter Programming Approach for State Feedback Optimal Control of Constrained Linear System

Based upon iterative solution of multiparametric quadratic programming (MPQP) problem, explicit solution to model predictive control (MPC) of the constrained linear time-varying system is obtained. By using the principle of optimum and MPQP, an explicit algorithm for obtaining state feedback optimal control law of the constrained linear time-varying system is proposed. This paper shows that, for constrained linear time-varying system, both the control law for MPC and the state feedback optimal control law are time-varying piece wise linear. At the end of the paper, simulations for a vibration control of elevator's mechanical system are made
A Low-Power CMOS Modulator for Ultra-Wideband Transmitters

A low-power CMOS modulator for carrier-based ultra-wideband (UWB) transmitters is presented. The core of the circuit consists of an oscillator and two control switches. The oscillator sets the center frequency of the UWB signal. The spectrum of the baseband data pulse is frequency translated to the desired band without using an explicit mixer. Since in many pulse-based UWB systems, in particular those used in radar and low-data-rate communication applications, a short duration pulse with a low pulse repetition frequency (PRF) is used, the power consumption of the modulator can be reduced significantly by turning off the oscillator between the pulses. This is accomplished using a combination of two MOS switches. By manipulating the pulse width and phase delay of the pulse signals applied to these switches, the time and frequency domain specifications of the transmitted UWB signal are adjusted. A prototype UWB modulator is designed and simulated in a 0.18mum CMOS technology. Simulation results for operation in the UWB low band, i.e., 3.1 to 5 GHz are presented.
Verifying other-race identity in forensic settings: Increasing tolerance for variability in appearance

representation of a face (a Face Recognition Unit; FRU) (Bruce & Young, 1986), which is resistant to interference from variability in appearance attributable to environmental (e.g., lighting), camera (e.g., lens) and intrinsic (e.g., make-up, expression) factors (Burton, Jenkins, Hancock, & White, 2005). With each new encounter, tolerance for variability increases. My results suggest a similar process occurs for other-race faces. In Experiment 2, I included target-absent trials to investigate whether withinperson variability also helps to detect that the target identity is absent from the lineup. The results of Experiment 2 suggest that the learning observed in Experiment 1 is somewhat illusory. Under conditions of uncertainty, I found only a weak benefit of viewing multiple images when the target is present and no benefit when the target is absent. In Experiment 1, the only type of error participants could have made was a misidentification. However in Experiment 2, a new type of error was introduced on target-present trials: misses. Although less frequent than misidentifications, the introduction of misses at least partially accounts for the reduced improvement relative to Experiment 1, in which participants selected a face on every trial. Most notably, I found no evidence that adding exemplars helped participants to detect that the target identity was absent. Indeed, I observed a non-significant trend in the opposite direction, such that the proportion of participants’ selecting an identity from the lineup despite the target being absent rose from about 60% to 70% after participants were presented with four images. Although non-significant results from the paired sample t-tests could be attributable to low power (0.67), the results of these supplemental analyses were consistent with corresponding linear contrast analyses, which had ample power (0.99) to
Hierarchical modeling approach for the automatic planning of space operation mission

Space operation needs the collaboration of many systems and its execution is a complex process, which greatly increases the difficulty of knowledge representation of system state, timing and constraints. Usually, the realization of a mission requires the cooperation of multiple sub-missions, so the action level model, which is a single level modeling approach, cannot support the automatic planning of space operation. The paper builds the model field of space operation by using hierarchical modeling method. It divides operation activities into two levels: action level and mission level. The model of action is created by the relations and constraints between action and system states, and the model of mission is built by sub-missions and their constraints. The models of actions, missions and constraints such as resources compose the field of space operation. Analysis and simulation shows that the approach could model space operation effectively.
Bayesian Sketch Learning for Program Synthesis

We present a Bayesian statistical approach to the problem of automatic program synthesis. Our synthesizer starts by learning, offline and from an existing corpus, a probabilistic model of real-world programs. During synthesis, it is provided some ambiguous and incomplete evidence about the nature of the programming task that the user wants automated, for example sets of API calls or data types that are relevant for the task. Given this input, the synthesizer infers a posterior distribution over type-safe programs that assigns higher likelihood to programs that, according to the learned model, are more likely to match the evidence. We realize this approach using two key ideas. First, our learning techniques operate not over code but syntactic abstractions, or sketches, of programs. During synthesis, we infer a posterior distribution over sketches, then concretize samples from this distribution into type-safe programs using combinatorial techniques. Second, our statistical model explicitly models the full intent behind a synthesis task as a latent variable. To infer sketches, we first estimate a posterior distribution on the intent, then use samples from this posterior to generate a distribution over possible sketches. We show that our model can be implemented effectively using the new neural architecture of Bayesian encoder-decoders, which can be trained with stochastic gradient descent and yields a simple inference procedure. We implement our ideas in a system, called Bayou, for the synthesis of API-heavy Java methods. We train Bayou on a large corpus of Android apps, and find that the trained system can often synthesize complex methods given just a few API method names or data types as evidence. The experiments also justify the design choice of using a latent intent variable and the levels of abstraction at which sketches and evidence are defined.
A novel algorithm for restructuring web search results by clustering pseudo documents

The search results produced by the search engine are in scattered form. The user may enter the same query for different information needs. So, it is difficult for user to get accurate information related to the submitted query. In this system feedback session is used to show the click sequences of user for a particular keyword. These click sequences are mapped into TF-IDF vector which is used to generate pseudo document. A Pseudo document represents the most important words for the searched keywords. User search goals are generated by using K-means clustering algorithm from pseudo document and the restructuring of web search results is done on the basis of user search goal. But, sometimes the URLs are not considered in clustering because of less TF-IDF values. This results in omission of some URLs from restructured result. So we propose new algorithm to restructure web search results. By using this algorithm the restructuring is done properly and all the URLS are present in the restructuring results. Finally, the metric VAP, risk and CAP are calculating the performance of restructuring. After conducting experiments on the system, we observed that the results were improved.
Speech retrieval of Mandarin broadcast news via mobile devices

This paper presents a system for speech retrieval of Mandarin broadcast news. First, several data-driven and unsupervised approaches are integrated into the broadcast news transcription system to improve the speech recognition accuracy and efficiency. Then, a multi-scale indexing paradigm for broadcast news retrieval is proposed to make use of the special structural properties of the Chinese language as well as to alleviate the problems caused by the speech recognition errors. Finally, we use the PDA as the platform and Mandarin broadcast news stories collected in Taiwan as the document collection to establish a speech-based multimedia information retrieval prototype system. Very encouraging results are obtained.
Searching phase optimize in PTS-APPR method by GA for PAPR reduction in OFDM-WLAN systems

In this paper, we propose a new PAPR reduction using the hybrid of a partial transmit sequences (PTS) and an adaptive peak power reduction (APPR) methods with genetic algorithm (GA). These methods are used in an Orthogonal Frequency Division Multiplexing (OFDM) system. The OFDM employs orthogonal sub-carriers for data modulation. These sub-carriers unexpectedly present a large Peak to Average Power Ratio (PAPR) in some cases. In order to reduce PAPR, the sequence of input data is rearranged by PTS. The APPR method is also used to controls the peak level of modulation signals by an adaptive algorithm. A proposed reduction method consists of these two methods and realizes both advantages at the same time. In order to make the optimum condition on PTS for PAPR reduction, a quite large calculation cost must be demanded and thus it is impossible to obtain the optimum PTS. In the proposed method by using genetic algorithm, the total calculation cost becomes drastically reduced. The parameter for simulation used standard WLAN in IEEE 802.11a system. In simulation results, the proposed method shows the improvement on PAPR and also reveals the high performance on bit error rate (BER) of an OFDM system.
NeuroDSP Accelerator for Face Detection Application

Neuro-Inspired Vision approach, based on models from biology, allows to reduce the computational complexity. One of these models - The Hmax model - shows that the recognition of an object in the visual cortex mobilizes V1, V2 and V4 areas. From the computational point of view, V1 corresponds to the area of the directional filters (for example Gabor filters or wavelet filters). This information is then processed in the area V2 in order to obtain local maxima. This new information is then sent to an artificial neural network. This neural processing module corresponds to area V4 of the visual cortex and is intended to categorize objects present in the scene. In order to realize autonomous vision systems (low-power consumption) with such treatments inside, we studied a new architeure of a Neural Processor named NeuroDSP. We describe in this paper an optimized Hmax model implementation on this Neural Processor for a face detection application.
Uranus' aurorae past equinox

The aurorae of Uranus were recently detected in the far ultraviolet with the Hubble Space Telescope (HST) providing a new, so far unique, means to remotely study the asymmetric Uranian magnetosphere from Earth. We analyze here two new HST Uranus campaigns executed in Sept. 2012 and Nov. 2014 with different temporal coverage and under variable solar wind conditions numerically predicted by three different MHD codes. Overall, HST images taken with the Space Telescope Imaging Spectrograph reveal auroral emissions in three pairs of successive images (one pair acquired in 2012 and two in 2014), hence six additional auroral detections in total, including the most intense Uranian aurorae ever seen with HST. The detected emissions occur close the expected arrival of interplanetary shocks. They appear as extended spots at southern latitudes, rotating with the planet. They radiate 5-24 kR and 1.3-8.8 GW of ultraviolet emission from H$_2$, last for tens of minutes and vary on timescales down to a few seconds. Fitting the 2014 observations with model auroral ovals constrains the longitude of the southern (northern) magnetic pole to 104+\-26° (284-\-26°) in the Uranian Longitude System. We suggest that the Uranian near-equinoctial aurorae are pulsed cusp emissions possibly triggered by large-scale magnetospheric compressions.
Intergalactic medium heating by dark matter

We derive the evolution of the energy deposition in the intergalactic medium (IGM) by dark matter (DM) decays/annihilations for both sterile neutrinos and light dark matter (LDM) particles. At z > 200 sterile neutrinos transfer a fraction f(abs) similar to 0.5 of their rest mass energy into the IGM; at lower redshifts this fraction becomes less than or similar to 0.3 depending on the particle mass. The LDM particles can decay or annihilate. In both the cases f(abs) similar to 0.4-0.9 at high (> 300) redshift, dropping to approximate to 0.1 below z = 100. These results indicate that the impact of DM decays/annihilations on the IGM thermal and ionization history is less important than previously thought. We find that sterile neutrinos (LDM) decays are able to increase the IGM temperature by z = 5 at most up to 4 K (100 K), about 50-200 times less than predicted by estimates based on the assumption of complete energy transfer to the gas.
A New Adaptive Ensemble Boosting Classifier for Concept Drifting Stream Data

—With the emergence of large-volume and high speed streaming data, mining of stream data has become a focus on increasing interests in real applications including credit card fraud detection, target marketing, network intrusion detection, etc. The major new challenges in stream data mining are, (a) since streaming data may flow in and out indefinitely and in fast speed, it is usually expected that a stream data mining process can only scan a data once and (b) since the characteristics of the data may evolve over time, it is desirable to incorporate evolving features of streaming data. This paper introduced new adaptive ensemble boosting approach for the classification of streaming data with concept drift. This adaptive ensemble boosting method uses adaptive sliding window and Hoeffding Tree with naï ve bayes adaptive as base learner. The result shows that the proposed algorithm works well in changing environment as compared with other ensemble classifiers.
A Conclusive Life Cycle of Discourse Modeling

A well modeled discourse is the fundamental work of building a dialogue system within a problem domain. The detail of the modeling process could be different from domain to domain. Many researchers have identified different dimensions to model the discourse structures in their own domains. Nevertheless, like most of the software engineering issues, even though the detail process could be domain dependant, some general guidelines can still be extracted by looking into the detail of existing methods and strategies. By comparing and concluding some discourse modeling approaches in the current literature, I have formulated a conclusive life cycle of this work.
Voice-Identity Processing Deficit - The Cognitive and Neural Mechanisms of Phonagnosia

The voice contains elementary social communication cues: it conveys speech, as well as paralinguistic information pertaining to the emotional state or the identity of the speaker. In contrast to neuroscientific research on vocal-speech and vocal-emotion processing, voice-identity processing has been less explored. This seems surprising, given the day-to-day significance of person recognition by voice. A valuable approach to unravel how voice-identity processing is accomplished is to investigate people who have a selective deficit in voice recognition. Such a deficit has been termed phonagnosia. In the present dissertation, I investigate phonagnosia following brain damage (i.e. acquired phonagnosia), as well as phonagnosia cases without apparent brain lesion (i.e. developmental phonagnosia). I in-depth characterise the underlying cognitive, neuralfunctional, and neuro-anatomical mechanisms of phonagnosia by means of comprehensive behavioural testing as well as functional and structural magnetic resonance imaging. The findings of this dissertation inform the current model of voiceidentity processing by (i) delivering novel evidence of brain regions that crucially contribute to voice-identity processing, and by (ii) emphasising the multistage nature of voice-identity processing. We showed that dysfunction at different cognitive stages results in behaviourally distinct phonagnosia sub-types. Generally, advanced scientific knowledge about voice-identity processing as provided in the current dissertation also propels practical applications such as clinical treatment programs and artificial voicerecognition systems.
Ontology-based documentation extraction for semi-automatic migration of Java code

Migrating libraries is not a trivial task, even under the simplest assumption of a downward compatible upgrade. We propose a novel approach to partially relieve programmers from this task, based on the simple observation that class, method and field names and comments contained in a Java library should be a good approximation of its semantics, and that code migration requires knowing the semantic similarities between the two libraries. Following this assumption, we borrow the main concepts and notions from the Semantic Web, and show how (1) an ontology can be automatically generated from the relevant information extracted from the code of the library; (2) semantic similarities between two different libraries can be found by running a particular ontology matching (a.k.a. ontology alignment) algorithm on the two ontologies extracted from the libraries. The main advantages of the approach are that ontology extraction can be fully automated, without adding ad-hoc code annotations, and that results and tools produced by the Semantic Web research community can be directly re-used for our purposes. Experiments carried out even with simple and efficient freely available matchers show that our approach is promising, even though it would benefit from the use of more advanced ontology matchers possibly integrated with a component for checking type compatibility of the computed alignments.
Berk-Nash Equilibrium : A Framework for Agents with Misspecified Models

We develop an equilibrium framework that relaxes the standard assumption that people have a correctly-specified view of their environment. Each player is characterized by a (possibly misspecified) subjective model, which describes the set of possible beliefs (over distributions over others’ actions and payoff relevant states). We introduce the notion of a Berk-Nash equilibrium: Each player follows a strategy that is optimal given her belief, and her belief is restricted to be the best fit among the set of beliefs she considers possible. The notion of best fit is formalized in terms of minimizing the relative entropy and depends on the equilibrium strategy profile. Standard solution concepts such as Nash equilibrium and self-confirming equilibrium constitute special cases where players learn with correctly-specified models. The framework provides a systematic approach to modeling players with misspecified views and also unifies a specific bounded rationality literature where mistakes are driven by misspecifications. Importantly, we provide a learning foundation for Berk-Nash equilibrium by extending and combining results from the statistics literature on misspecified learning and the economics literature on learning in games. ∗We thank Vladimir Asriyan, Pierpaolo Battigalli, Larry Blume, Aaron Bodoh-Creed, Sylvain Chassang, Emilio Espino, Erik Eyster, Drew Fudenberg, Yuriy Gorodnichenko, Philippe Jehiel, Stephan Lauermann, Natalia Lazzati, Kristóf Madarász, Matthew Rabin, Ariel Rubinstein, Joel Sobel, Jörg Stoye, and several seminar participants for helpful comments. Esponda: Olin Business School, Washington University in St. Louis, 1 Brookings Drive, Campus Box 1133, St. Louis, MO 63130, iesponda@wustl.edu; Pouzo: Department of Economics, UC Berkeley, 530-1 Evans Hall #3880, Berkeley, CA 94720, dpouzo@econ.berkeley. edu.