Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnitude-sensitive preference formation

Our understanding of the neural computations that underlie the ability of animals to choose among options has advanced through a synthesis of computational modeling, brain imaging and behavioral choice experiments. Yet, there remains a gulf between theories of preference learning and accounts of the real, economic choices that humans face in daily life, choices that are usually between some amount of money and an item. In this paper, we develop a theory of magnitude-sensitive preference learning that permits an agent to rationally infer its preferences for items compared with money options of different magnitudes. We show how this theory yields classical and anomalous supply-demand curves and predicts choices for a large panel of risky lotteries. Accurate replications of such phenomena without recourse to utility functions suggest that the theory proposed is both psychologically realistic and econometrically viable.
Automatic Multi-Lingual Information Extraction

Information Extraction(IE) is a burgeoning technique because of the explosion of internet. So far, most of the IE systems are focusing on English text; and most of them are in the supervised learning framework, which requires large amount of human labor; and most of them can only work in narrow domain, which is domain dependent. These systems are difficult to be ported to other languages, other domains because of these inherent shortcomings. Currently, besides western languages like English, there are many other Asian languages which are much different from English. In English, words are delimited by white-spaces so computer can easily tokenize the input text string. In many languages like Chinese, Japanese, Thai and Korea, they do not have word boundaries between words. This poses a difficult problem for the information extraction for those languages. In this thesis, we intend to implement a self-contained, language independent automatic IE system. The system is automatic because we are using a unsupervised learning framework in which no labeled data is required for training or a semi-supervised learning framework in which small amount of labeled data and large amount of unlabeled data are used. Specificly, we deal with Chinese and English languages name entity recognition and entity relation extraction, but the system can be easily extended to any other languages and other tasks. We implement an unsupervised Chinese word segmenter, a Chinese POS tagger, and we extend maximum entropy models to incorporate unlabeled data for general information extraction.
Factoring in Skew-Polynomial Rings over Finite Fields

Efficient algorithms are presented for factoring polynomials in the skew-polynomial ringFx;? , a non-commutative generalization of the usual ring of polynomialsFx, whereFis a finite field and ? :F? Fis an automorphism (iterated Frobenius map). Applications include fast functional decomposition algorithms for a class of polynomials inFx whose decompositions are “wild” and previously thought to be difficult to compute.
Exploiting P2P in the Creation of Game Worlds

Peer-to-peer networks are a promising platform for supporting entirely decentralized, distributed multi-user gaming; however, multi-player games typically require highly predictable performance from the underlying network. This is at odds with the inherently unreliable nature of peer-to-peer environments. Existing approaches to providing peer-to-peer support for multiplayer gaming focus on compensating for the unpredictability of the underlying network. We propose that rather than trying to compensate for these factors, they can be exploited together with information about the peer-to-peer network in order to address the problem of maintaining a novel gaming experience in the absence of a central authority. In order to explore our proposition, we model the measurable properties of P2P networks within a distributed multi-player game – Net World. We do this is such a way that the heterogeneous and unpredictable nature of the peerto-peer environment becomes a positive part of the players experience.
One-Class Classification with Extreme Learning Machine

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.
Head pose-free appearance-based gaze sensing via eye image synthesis

This paper addresses the problem of estimating human gaze from eye appearance under free head motion. Allowing head motion remains challenging because eye appearance changes significantly for different head poses, and thus new head poses require new training images. To avoid repetitive training, we propose to produce synthetic training images for varying head poses. First, we model pixel displacements between head-moving eye images as 1D pixel flows, and then produce such flows to synthesize new training images from the original training images captured under a fixed default head pose. Specifically, we produce all the required 1D flows by using only four additionally captured images. Our method was successfully tested with extensive experiments to demonstrate its effectiveness.
A Framework for Considering Comprehensibility in Modeling

Comprehensibility in modeling is the ability of stakeholders to understand relevant aspects of the modeling process. In this article, we provide a framework to help guide exploration of the space of comprehensibility challenges. We consider facets organized around key questions: Who is comprehending? Why are they trying to comprehend? Where in the process are they trying to comprehend? How can we help them comprehend? How do we measure their comprehension? With each facet we consider the broad range of options. We discuss why taking a broad view of comprehensibility in modeling is useful in identifying challenges and opportunities for solutions.
Information preservation in statistical privacy and bayesian estimation of unattributed histograms

In statistical privacy, utility refers to two concepts: information preservation -- how much statistical information is retained by a sanitizing algorithm, and usability -- how (and with how much difficulty) does one extract this information to build statistical models, answer queries, etc. Some scenarios incentivize a separation between information preservation and usability, so that the data owner first chooses a sanitizing algorithm to maximize a measure of information preservation and, afterward, the data consumers process the sanitized output according to their needs [22, 46]. We analyze a variety of utility measures and show that the average (over possible outputs of the sanitizer) error of Bayesian decision makers forms the unique class of utility measures that satisfy three axioms related to information preservation. The axioms are agnostic to Bayesian concepts such as subjective probabilities and hence strengthen support for Bayesian views in privacy research. In particular, this result connects information preservation to aspects of usability -- if the information preservation of a sanitizing algorithm should be measured as the average error of a Bayesian decision maker, shouldn't Bayesian decision theory be a good choice when it comes to using the sanitized outputs for various purposes? We put this idea to the test in the unattributed histogram problem where our decision- theoretic post-processing algorithm empirically outperforms previously proposed approaches.
Identifying attachment areas on sketched symbols

In the field of syntactic recognition of hand-drawn visual languages, the recognition of the relations among graphical symbols is one of the first important tasks to be accomplished and is usually reduced to recognize the attachment areas of each symbol and the relations among them. In this paper we present an approach for identifying attachment areas on sketched symbols. The approach is independent from the method used to recognize symbols and assumes that the symbol has already been recognized. The approach is evaluated through a user study aimed at comparing the attachment areas detected by the system to those devised by the users. The results show that the system can identify attachment areas with a reasonable accuracy.
An efficient false alarm reduction approach in HTTP-based botnet detection

In recent years, bots and botnets have become one of the most dangerous infrastructure to carry out nearly every type of cyber-attack. Their dynamic and flexible nature along with sophisticated mechanisms makes them difficult to detect. One of the latest generations of botnet, called HTTP-based, uses the standard HTTP protocol to impersonate normal web traffic and bypass the current network security systems (e.g. firewalls). Besides, HTTP protocol is commonly used by normal applications and services on the Internet, thus detection of the HTTP botnets with a low rate of false alarms (e.g. false negative and false positive) has become a notable challenge. In this paper, we review the current studies on HTTP-based botnet detection in addition to their shortcomings. We also propose a detection approach to improve the HTTP-based botnet detection regarding the rate of false alarms and the detection of HTTP bots with random patterns. The testing result shows that the proposed method is able to reduce the false alarm rates in HTTP-based botnet detection successfully.
Program Comprehension through Software Habitability

The comprehensive understanding of a large software system is a daunting task because of the sheer size and complexity that such systems exhibit. In this context software visualization is a widely used approach, since well-conceived visual representations allow one to spot patterns. The large majority of visualizations use 2D representations, because they are easier to construct, navigate, and interact with. 3D representations usually exploit the 3rd dimension as an additional means to encode quantitative values, which is dismissed by many as a too small benefit in the light of the added complexity in terms of navigation and interaction. We argue that a well-constructed, interactive, and easily navigable 3D visualization can greatly help in program comprehension tasks by supporting habitability. Habitability transmits to a developer the notion that a software system is a physical space with strong orientation points. This can give developers the feeling of being "at home" in a system. We propose a 3D visualization of software systems hinging on the city metaphor. It is useful for program comprehension because it leads to clarity about the overall structure of a system. We apply our visualization technique on two large systems and discuss its benefits and drawbacks.
KUNLP system using Classification Information Model at SENSEVAL-2

The classification information model or CIM classifies instances by considering the discrimination ability of their features, which was proven to be useful for word sense disambiguation at Senseval-1. But the CIM has a problem of information loss. KUNLP system at Senseval-2 uses a modified version of the CIM for word sense disambiguation. We used three types of features for word sense disambiguation: local, topical, and bigram context. Local and topical context are similar to Chodorow's context and refer to only unigram information. The window of a bigram context is similar to that of a local context but a bigram context refers to only bigram information. We participated in the English lexical sample task and the Korean lexical sample task, where our systems ranked high.
High-Performance RF Devices and Components on Flexible Cellulose Substrate by Vertically Integrated Additive Manufacturing Technologies

This paper aims to demonstrate that novel additive manufacturing (AM) technologies like metal adhesive laminate and multilayer inkjet printing can be effectively exploited to fabricate high-performing radio-frequency passive components on flexible substrates. Both processes are substrate independent and therefore suitable for manufacturing circuits on several unconventional materials, such as photo-paper. In addition, their complementary features can be combined to develop a novel hybrid process. Proof-of-concept AM prototypes of passive components, such as capacitors and inductors, exhibiting quality factors over 70, never achieved before on paper, and self-resonant frequencies beyond 4 GHz are described. The maximum inductance and capacitance per unit area are 1.4 nH/mm2 and 6.5 pF/mm2, respectively. Moreover, an AM RF mixer with a conversion loss below 10 dB is demonstrated still on paper substrate. The mixer, fabricated with the copper adhesive laminate method, operates at 1 GHz and exploits a lumped balun transformer connected to two packaged diodes in series.
Protein Classification via Kernel Matrix Completion

Although 3D structure of a protein is valuable to predict its function, it is still far more difficult and costly to measure coordinates of atoms in a protein than sequencing its amino acids. We often do not know the 3D structures of all the proteins at hand. Let us consider a kernel matrix that consists of kernel values representing protein similarities in terms of their 3D structures where some of the entries are missing because structure information of some proteins are unavailable whereas their amino acid sequences are readily available. We proposes to estimate the missing entries by means of another kernel matrix derived from amino acid sequences. Basically a parametric model is created from the sequence kernel matrix, and the missing entries of the structure kernel matrix are estimated by fitting this model to existing entries. For model fitting, we adopt two algorithms: e-projection and em algorithm based on the information geometry of kernel matrices. We performed protein classification experiments by using support vector machines. Our results show that these algorithms can effectively estimate the missing entries.
Smart automotive lighting for vehicle safety

It is believed that vehicle-to-vehicle (V2V) communications and accurate positioning with sub-meter error could bring vehicle safety to a different level. However, to this date it is still unclear whether the envisioned V2V standard, dedicated short-range communications, can become available in commercially available vehicle products, while widely available consumergrade GPS receivers do not provide the required accuracy for many safety applications. In this article, combining visible light communications and visible light positioning, we propose the use of smart automotive lighting in vehicle safety systems. These lights would be able to provide the functions of illumination and signaling, reliable communications, and accurate positioning in a single solution. The proposed solution has low complexity, and is shown to be scalable in high vehicle density and fast topology changing scenarios. In this article, we also present several design guidelines for such a system, based on the results of our analytic and empirical studies. Finally, evaluation of our prototype provides evidence that the system can indeed detect potential risks in advance and provide early warnings to the driver in real-world scenarios, lowering the probability of traffic accidents.
Liberation from equations: An equation-free method reveals the ecological interaction networks

19 Mapping the network of ecological interactions is key to understanding the composition, stability, 20 function and dynamics of microbial communities. These ecosystem properties provide the 21 mechanistic basis for understanding and designing microbial treatments that attempt to promote 22 human health and provide environmental services. In recent years various approaches have been 23 used to reveal microbial interaction networks, inferred from metagenomic sequencing data using 24 time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not 25 possible to capture details of the ecological interactions behind complex microbial dynamics. Here, 26 we develop the sparse S-map method (SSM), which generates a sparse interaction network from a 27 multivariate ecological time-series without presuming any mathematical formulation for the 28 underlying microbial processes. We show that this method outperforms a comparative equation-29 based method and that the results were robust to the range of observational errors and quantity of 30 data that we tested. We then applied the method to the microbiome data of six mice and found that 31 the mice had similar interaction networks when they were middle-to old-aged
The SAX-3 Receptor Stimulates Axon Outgrowth and the Signal Sequence and Transmembrane Domain Are Critical for SAX-3 Membrane Localization in the PDE Neuron of C. elegans

SAX-3, a receptor for Slit in C. elegans, is well characterized for its function in axonal development. However, the mechanism that regulates the membrane localization of SAX-3 and the role of SAX-3 in axon outgrowth are still elusive. Here we show that SAX-3::GFP caused ectopic axon outgrowth, which could be suppressed by the loss-of-function mutation in unc-73 (a guanine nucleotide exchange factor for small GTPases) and unc-115 (an actin binding protein), suggesting that they might act downstream of SAX-3 in axon outgrowth. We also examined genes related to axon development for their possible involvement in the subcellular localization of SAX-3. We found the unc-51 mutants appeared to accumulate SAX-3::GFP in the neuronal cell body of the posterior deirid (PDE) neuron, indicating that UNC-51 might play a role in SAX-3 membrane localization. Furthermore, we demonstrate that the N-terminal signal sequence and the transmembrane domain are essential for the subcellular localization of SAX-3 in the PDE neurons.
Multiple – Symbol Differential Detection Based on Computational Geometry

In this paper, the application of combinatorial geometry to noncoherent multiple–symbol differential det ection (MSDD) is considered. The resulting algorithm is referred to as CG–MSDD. Analytical expressions for both the complexity and the error–rate performance of CG–MSDD are derived and it is shown that its complexity is polynomial in the lengthN of the MSDD observation window if the rank of the N × N channel autocorrelation matrix is fixed, but in fact exponential in N if standard fading models are considered. Compared to popular sphere–decoder based MSDD, CG–MSDD is superior (i) in low– signal–to–noise power ratio (SNR) slow–fading channels as its complexity is independent of the SNR, (ii) as its complexityis constant, i.e., independent of the particular channel and n oise realization, and (iii) asymptotically, as its complexity exponent only scales linearly with the bandwidth of the fading proces. I. I NTRODUCTION Multiple–symbol differential detection (MSDD) for fading and non–fading additive white Gaussian noise (AWGN) channels has been devised in the late eighties and early nineties as an efficient means for reliable data communication without t he need for channel estimation, cf. e.g. [1], [2], [3], [4], [5] . The main disadvantage of MSDD is its high computational complexity, which, in a brute–force approach, grows exponenti ally with the block size or so–called observation window size N . Recently, various authors have devised the application of computationally efficient tree–search algorithms, includ ing sphere decoding (SD), to solve the MSDD problem for fading channels, cf. e.g. [6], [7], [8]. They found that, for wide ranges ofN and signal–to–noise ratios (SNRs), these detectors can drastically reduce the complexity required to perform MSDD. However, these algorithms also have a number of drawbacks. (i) Theirinstantaneouscomputational complexity is a random variable, which is considered disadvantageous for practical implementation. (ii) Their averagecomputational complexity may become prohibitively large in low SNRs. (iii) If the maximum–likelihood (ML) MSDD solution is desired at all times, the worst–case complexity increases exponentially withN . It has been shown in [9] that for an SD implementation with Fincke–Pohst enumeration even averag e complexity experiences this exponential growth (cf. also [ 10] for SD in coherent detection). This work was in part supported by the Deutsche Forschungsge meinschaft (DFG) under grant HU 634/6-1. In this paper, we therefore take a completely different approach to MSDD, inspired by the recent work by Manglani et al. [11], who expressed the multiuser detection problem in code–division multiple access (CDMA) as the quadratic maximization problem x̂ = argmax x∈{0,1}n {