Miracle-dz/UniFAR-FTUs huggingface dataset:
Representations of Time Affect Willingness to Wait for Future Rewards

How do representations of the future shape behavior? Prior research has shown that people’s willingness to wait for a future reward decreases with increases in time. At the same time, this research has also shown that such effects can depend on the vividness of the future reward, as well as, on individual differences. The present research offers a potential explanation for these additional effects in demonstrating how representations of the future can depend not only on objective distances in time, but also on how distances in time are construed. In a series of three experiments using a delay discounting paradigm, we show that participants who represent the future as close to the present are more likely to wait for future rewards than those who represent the future as far, even when the objective distances are held constant. Applications are discussed to public policy issues such as global warming, and to episodic future thinking.
Employers ’ Search and the Efficiency of Matching *

Employers’ Search and the Efficiency of Matching Unskilled workers in low productivity jobs typically experience higher labour turnover. This paper shows how this empirical finding is related to variation in the efficiency of the matching process across occupations. A simple theoretical model of employers’ search shows that firms find it optimal to invest relatively little in advertisement and screening when recruiting for low productivity jobs. This generates more separations and higher turnover at the bottom than at the top of the jobs’ distribution. The analysis of a unique sample of British hirings, containing detailed information about employers’ recruitment practices, shows that more intensive recruitment leads to matches of better quality that pay higher wages, last longer and make employers more satisfied with the person taken on. JEL Classification: J63, J64, M51
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points. In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications. This paper presents an incremental DR solution. We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data. First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results. Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization. We demonstrate the effectiveness of our design with two case studies using real-world datasets.
False Prophet, or Genuine Savior? Assessing the Effects of Economic Openness on Sustainable Development, 1980-1999

While many herald globalizationâ€”the increasing interconnectedness of national economiesâ€”to be associated with rising standards of living across the globe, others fear its effects on sustainability. Antiglobalization forces and environmentalists view these developments as a threat to the welfare of future generations because of profligate and excessive current consumption. This study is the first to estimate the effects of dependence on trade, foreign direct investment (FDI), and an index of economic freedom on the World Bank's measure of sustainability (the genuine savings rate), which measures the rate at which investment in the total stock of manufactured, human, and natural capital exceeds its depreciation. Contrary to pessimists' fears, our indicators of economic openness show positive effects on sustainability, results that are robust to sample size, testing procedure, and several alternative specifications. The results support those who suggest that distorted economies tend to be both inefficient and damaging to future generations. If increasing trade, FDI, and economic freedom are hallmarks of globalization, then worries about its effects on future well-being are misplaced.Equal authorship. We are grateful to Erich Weede, Rick Auty, Simon Dietz, Paul Hensel, Jonathan Moses, Ragnar Torvik, Jostein Vik, Paivi Lujala, two reviewers, and the editor for comments and suggestions. The anonymous reviewers were extraordinarily constructive. Any remaining errors are entirely our fault. The data are available at a€ˆhttp://www.svt. ntnu.no/iss/Indra.de. Soysa/default.htma€‰.
The sharp, the flat and the shallow: Can weakly interacting agents learn to escape bad minima?

An open problem in machine learning is whether flat minima generalize better and how to compute such minima efficiently. This is a very challenging problem. As a first step towards understanding this question we formalize it as an optimization problem with weakly interacting agents. We review appropriate background material from the theory of stochastic processes and provide insights that are relevant to practitioners. We propose an algorithmic framework for an extended stochastic gradient Langevin dynamics and illustrate its potential. The paper is written as a tutorial, and presents an alternative use of multi-agent learning. Our primary focus is on the design of algorithms for machine learning applications; however the underlying mathematical framework is suitable for the understanding of large scale systems of agent based models that are popular in the social sciences, economics and finance.
Similarity of color images

We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L1-, L2-, L(infinity )-distance between two cumulative color histograms can be used to define a similarity measure of these two color distributions. We show that this method produces slightly better results than color histogram methods, but it is significantly more robust with respect to the quantization parameter of the histograms. The second technique is an example of a new approach to color indexing. Instead of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which is used for the retrieval is a weighted sum of the absolute differences between corresponding moments. Our tests clearly demonstrate that a retrieval based on this technique produces better results and runs faster than the histogram-based methods.
Intelligent User Association for Symbiotic Radio Networks Using Deep Reinforcement Learning

In this paper, we are interested in symbiotic radio networks (SRNs) and focus on the user association problem in SRNs. Specifically, in an SRN, the base station serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem.To overcome this issue, we propose a deep reinforcement learning (DRL) algorithm, which uses historical knowledge to infer the current information in order to make appropriate decisions for this user association problem. Finally, simulation results show that the proposed DRL algorithm achieves performance comparable to the optimal user association policy which requires perfect real-time information.
Memory-Attended Recurrent Network for Video Captioning

Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed. A potential disadvantage of such design is that it cannot capture the multiple visual context information of a word appearing in more than one relevant videos in training data. To tackle this limitation, we propose the Memory-Attended Recurrent Network (MARN) for video captioning, in which a memory structure is designed to explore the full-spectrum correspondence between a word and its various similar visual contexts across videos in training data. Thus, our model is able to achieve a more comprehensive understanding for each word and yield higher captioning quality. Furthermore, the built memory structure enables our method to model the compatibility between adjacent words explicitly instead of asking the model to learn implicitly, as most existing models do. Extensive validation on two real-word datasets demonstrates that our MARN consistently outperforms state-of-the-art methods.
Vulnerability Evaluation Accelerator for Lightweight Ciphers

Power analysis attacks against lightweight ciphers are barely examined. Therefore, to verify the safety of lightweight ciphers in the future, it is important to investigate a method to verify tamper resistance against power analysis attacks. To verify the tamper resistance by performing a simulation, an extremely long processing time is required to obtain power consumption waveforms. Therefore, it is important to reduce the processing time. This study proposes a method to verify the tamper resistance of lightweight ciphers against power analysis attacks using a FPGA accelerator in order to reduce the time to obtain power consumption waveforms.
For better and for worse: three lending relationships

Are close, long-term relationships between borrowers and lenders feasible in an increasingly competitive financial marketplace? How do relationships that have developed between banks and firms change when firms gain access to alternative funding sources, especially public securities markets? Can firms gain the best of both worlds by a judicious mixture of bank and public borrowing? Using three firms as examples, Mitchell Berlin sizes up the pros and cons of relationship lending.
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN

Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different contrasts. These approaches are potentially important for image imputation problems, where complete set of data is often difficult to obtain and image synthesis is one of the key solutions for handling the missing data problem. Unfortunately, the lack of the scalability of the existing GAN-based image translation approaches poses a fundamental challenge to understand the nature of the MR contrast imputation problem: which contrast does matter? Here, we present a systematic approach using Collaborative Generative Adversarial Networks (CollaGAN), which enable the learning of the joint image manifold of multiple MR contrasts to investigate which contrasts are essential. Our experimental results showed that the exogenous contrast from contrast agents is not replaceable, but other endogenous contrast such as T1, T2, etc can be synthesized from other contrast. These findings may give important guidance to the acquisition protocol design for MR in real clinical environment.
Orbital Floquet engineering of exchange interactions in magnetic materials

We present a scheme to control the spin-exchange interactions by manipulating the orbital degrees of freedom using a periodic drive. We discuss two different protocols for orbital Floquet engineering. In one case, a periodic drive modifies the properties of the ligand orbitals which mediate magnetic interactions between transition-metal ions. In the other case, we consider drive-induced mixing of d orbitals on each magnetic ion. We first find that an AC Stark shift of orbitals induces a change comparable to that induced from photoinduced hopping schemes, but expands the applicable frequency ranges. Next, we find that radiatively induced coherent vibrations provide a realistic path for Floquet orbital engineering with short pulses of electric fields weaker than 0.5 V/A producing 5%–10% changes in the magnetic coupling of Mott insulators such as the rare-earth titanates.
Integrating Artificial Intelligence into Weapon Systems

The integration of Artificial Intelligence (AI) into weapon systems is one of the most consequential tactical and strategic decisions in the history of warfare. Current AI development is a remarkable combination of accelerating capability, hidden decision mechanisms, and decreasing costs. Implementation of these systems is in its infancy and exists on a spectrum from resilient and flexible to simplistic and brittle. Resilient systems should be able to effectively handle the complexities of a high-dimensional battlespace. Simplistic AI implementations could be manipulated by an adversarial AI that identifies and exploits their weaknesses. In this paper, we present a framework for understanding the development of dynamic AI/ML systems that interactively and continuously adapt to their user's needs. We explore the implications of increasingly capable AI in the kill chain and how this will lead inevitably to a fully automated, always on system, barring regulation by treaty. We examine the potential of total integration of cyber and physical security and how this likelihood must inform the development of AI-enabled systems with respect to the "fog of war", human morals, and ethics.
A New Motion-Texture Graph for Generating Real-time Realistic Human Motions

Motion-Texture synthesis is a new field of research which aims at generating new human motions from a given motion capture (MoCap) data. The success of such techniques depends on the right selection of interpolation and distance functions among the motion data. Although the quaternion representation is the most mathematically accurate one, it is usually avoided due to the heavy computations needed. This paper proposes a fast technique for Motion-Texture synthesis using qauternions and based on Markov Random Fields Model (MRF) model. The current work proposes a new Motion-Texture Graph called Quaternions MRF Motion- Texture Graph (QMMTG). QMMTG technique uses quaternions to devise a proposed fast matching criterion and a proposed simple method for smoother transitions among QMMTG nodes. QMMTG technique manages to generate a wider range of new motions and even better results in some cases. Besides, the smoothing algorithm has perceptually enhanced the resulting motions in comparison to the previous works. The authors envision QMMTG as a start for benefiting from the quaternions accuracy in real-time Motion- Texture synthesis applications.
Memory management strategies for parallel volume rendering

The parallel implementation of the volume rendering technique offers the potential of solving this computationally complex problem in reasonable times. This paper discusses a virtual memory strategy which is used to cope with the very large distributed data sets associated with the ray casting algorithm used for volume rendering. Various caching techniques are examined to reduce communication latency associated with remote data fetches within a transputer network. Multi-threading tasks are also investigated in order to minimise the effect of data fetch delay and improve overall system performance for large multiprocessor systems.
JUST-IN-TIME LATENT SEMANTIC ADAPTATION ON LANGUAGE MODEL FOR CHINESE SPEECH RECOGNITION USING WEB DATA

A novel method is proposed, which is for performing just-in- time adaptation on language models in Chinese speech recognition using Web search engines. Latent semantic analysis (LSA) is employed to change the probability distribution of N-gram language model. The method has two advantages. First, it needs relatively small amount of data which can be obtained from Web on-the-fly. Second, comparing to traditional adaptation formula of LSA, the proposed approach is more efficient, which ensures second pass decoding to be performed with high speed. Experiments show that the perplexity of language model is reduced by over 13% after adaptation. A 4.29% relative reduction on WER is achieved in large vocabulary Chinese speech recognition over standard test set.
Privacy and Security in Mobile Cloud Computing: Review

Mobile cloud computing is computing of Mobile application through cloud. As we know market of mobile phones is growing rapidly. According to IDC, the premier global market intelligence firm, the worldwide Smartphone market grew 42.5% year over year in the first quarter of 2012.With the growing demand of Smartphone the demand for fast computation is also growing. Inspite of comparatively more processing power and storage capability of Smartphone’s, they still lag behind Personal Computers in meeting processing and storage demands of high end applications like speech recognition, security software, gaming, health services etc. Mobile cloud computing is an answer to intensive processing and storage demand of real-time and high end applications. Being in nascent stage, Mobile Cloud Computing has privacy and security issues which deter the users from adopting this technology. This review paper throws light on privacy and security issues of Mobile Cloud Computing. General Terms Privacy and Security.
A study of spontaneous echo contrast in patients with rheumatic mitral stenosis and normal sinus rhythm: an Indian perspective.

OBJECTIVE--To study the incidence of spontaneous echo contrast in left atrium of Indian patients with rheumatic mitral stenosis in normal sinus rhythm and to define its relations. SUBJECTS--Transthoracic and multiplane transoesophageal echocardiographic studies were performed in 89 consecutive patients with rheumatic mitral stenosis who were in normal sinus rhythm. RESULTS--Spontaneous echo contrast in the left atrium was seen in 57.3% of patients on multiplane transoesophageal echocardiography and in only 5.6% on transthoracic echocardiography. The mean mitral valve area was 1.07 (SD 0.33) cm2 and 1.32 (0.45) cm2 (P = 0.004), mean left atrial size was 4.27 (0.67) cm and 3.91 (0.5) cm (P = 0.029), mean diastolic pressure gradient was 12.64 (5.69) mm Hg and 10 (5.5) mm Hg (P = 0.049), and absence of mitral regurgitation was seen in 45% and 23% of patients respectively (P = 0.1). Among patients with spontaneous echo contrast, 31% had either left atrial/appendage thrombus or a history of embolism, upsilon 0% in patients without spontaneous echo contrast (P < 0.0001). CONCLUSIONS--There is a high incidence of spontaneous echo contrast in the left atrium in Indian patients with rheumatic mitral stenosis in normal sinus rhythm on multiplane transoesophageal echocardiography. These patients are likely to embolise or form thrombi in the left atrium. The presence of spontaneous echo contrast is also associated with significantly smaller mitral valve area, larger left atrium, and higher mean diastolic mitral pressure gradient.
Accurate junction detection and reconstruction in line-drawing images

In this paper, we present an approach for junction detection and reconstruction in line-drawing images. Our approach is shifted to a problem of high curvature point detection from the skeleton of image. Like this, it is independent of any vectorization process and parameter free. The junction reconstruction stage takes benefit of the reliable skeleton segments and their topological relations to reconstruct junctions and make their positions accurate. The experimental results show that the proposed method is competitive with other baseline methods and can achieve accurate junction detection with some pixel errors.
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems

We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e.g., the size may exceed computer memory or data are collected in real-time). In our proposed framework, stochasticity is introduced in two different frameworks as a means to overcome these computational limitations, and probability distributions that can exploit structure and/or sparsity are considered. Theoretical results on consistency of the approximations for both the stochastic Newton and the stochastic quasi-Newton methods are provided. The results show, in particular, that stochastic Newton iterates, in contrast to stochastic quasi-Newton iterates, may not converge to the desired least-squares solution. Numerical examples, including an example from extreme learning machines, demonstrate the potential applications of these methods.
Discriminative Reranking of Discourse Parses Using Tree Kernels

In this paper, we present a discriminative approach for reranking discourse trees generated by an existing probabilistic discourse parser. The reranker relies on tree kernels (TKs) to capture the global dependencies between discourse units in a tree. In particular, we design new computational structures of discourse trees, which combined with standard TKs, originate novel discourse TKs. The empirical evaluation shows that our reranker can improve the state-of-the-art sentence-level parsing accuracy from 79.77% to 82.15%, a relative error reduction of 11.8%, which in turn pushes the state-of-the-art documentlevel accuracy from 55.8% to 57.3%.
UML: Abstraction as a Criterion for Defining Class Diagrams and Object Diagrams

UML is undisputedly the most efficient and effective tool of information systems analysis and design. Abstraction as paradigm, represent the basis of an object-oriented approach to development of information system and software solutions. No matter what background team members have (i.e. information technology or problem domain experts), the capability of abstraction is of crucial importance, especially at the early phase of the system’ s structure identification. Since class and object diagrams specify system’ s structure, indicating how to identify and relate them, they are an issue for system analysts and designers. This paper examines the following issue: to what extent abstraction level influences the need for creating object diagrams in shaping class diagrams and vice versa to what extent concretization at the level of an object diagrams influences structural decomposition of class diagrams.