Miracle-dz/UniFAR-FTUs huggingface dataset:
Model for the determination of seismic interaction between tunnel and underground station

Line 12 of Mexico City’s subway is in its final construction phase. The project tunnel passes through different geological formations and important deposits of soft soils, whose properties and thicknesses vary sharply over short distances. In addition, these deposits are significantly affected by the phenomenon of regional subsidence. The aim of this study is to determine differential stresses and relative displacements that may occur in the interaction between a circular tunnel and a rectangular underground station when they are subjected to an earthquake. A threedimensional finite difference model was formulated for this study to assess the influence of those stresses and relative displacements under seismic loading. The investigation considers the variation of material properties over time due to the phenomenon of regional subsidence.
A quantitative evaluation of maintainability enhancement by refactoring

Program refactoring is a technique to enhance the maintainability of a program. Although the concept itself is considered to be effective, there are few quantitative evaluation of its impact to the software maintainability. It is sometimes difficult to judge whether the refactoring in question should be applied or not without knowing the effect accurately. We propose a quantitative evaluation method to measure the maintainability enhancement effect of program refactoring. We focused on the coupling metrics to evaluate the refactoring effect. By comparing the coupling before and after the refactoring, we could evaluate the degree of maintainability enhancement. We applied our method to a certain program and showed that our method was really effective to quantify the refactoring effect and helped us to choose appropriate refactorings.
The CSTR entry to the Blizzard Challenge 2017

The annual Blizzard Challenge conducts side-by-side testing of a number of speech synthesis systems trained on a common set of speech data. Similar to 2016 Blizzard challenge, the task for this year is to train on expressively-read children’s story-books, and to synthesise speech in the same domain. The Challenge therefore presents an opportunity to investigate the effectiveness of several techniques we have developed when applied to expressive and prosodically-varied audiobook data. This paper describes the text-to-speech system entered by The Centre for Speech Technology Research into the 2017 Blizzard Challenge. The current system is a hybrid synthesis system which drives a unit selection synthesiser using the output from a neural network based acoustic and duration model. We assess the performance of our system by reporting the results from formal listening tests provided by the challenge.
Refinement types for Haskell

We present LiquidHaskell (http://goto.ucsd.edu/liquid), an automatic verifier for Haskell. LiquidHaskell uses Refinement types, a restricted form of dependent types where relationships between values are encoded by decorating types with logical predicates drawn from an efficiently SMT decidable theory (of arithmetic and uninterpreted functions.) In this talk, we will describe the key ingredients of LiquidHaskell. First, we will present a rapid overview of refinement types, including SMT solver based (decidable) subtyping, and inference. Decidability is achieved by eschewing the use of arbitrary terms inside types, and the use of indices to encode rich properties of data. Second, we will show how to recover some of the expressiveness lost by restricting the logic, with two new techniques: measures which encode structural properties of values and abstract refinements which enable generalization (i.e. quantification) over refinements. Third, we will discuss the curious interaction of laziness and refinement typing. In a nutshell, the technique of refinement typing can be viewed as a type-based generalization of Floyd-Hoare logics. Surprisingly, we demonstrate that under non-strict evaluation, these logics (and hence, classical refinement typing) is unsound, due to the presence of potentially divergent sub-computations. Fortunately, we show how soundness can be recovered with a termination analysis, itself, circularly bootstrapped off refinement typing. We have used LiquidHaskell to verify safety, functional correctness and termination properties of real-world Haskell libraries totalling mroe than 10,000 lines of code. Time permitting, we will present a demonstration of the tool and a few short case studies illustrating its use.
Single-ISA heterogeneous multi-core architectures for multithreaded workload performance

A single-ISA heterogeneous multi-core architecture is a chip multiprocessor composed of cores of varying size, performance, and complexity. This paper demonstrates that this architecture can provide significantly higher performance in the same area than a conventional chip multiprocessor. It does so by matching the various jobs of a diverse workload to the various cores. This type of architecture covers a spectrum of workloads particularly well, providing high single-thread performance when thread parallelism is low, and high throughput when thread parallelism is high. This paper examines two such architectures in detail, demonstrating dynamic core assignment policies that provide significant performance gains over naive assignment, and even outperform the best static assignment. It examines policies for heterogeneous architectures both with and without multithreading cores. One heterogeneous architecture we examine outperforms the comparable-area homogeneous architecture by up to 63%, and our best core assignment strategy achieves up to 31% speedup over a naive policy.
The prediction of protein secondary structure is an important step in the prediction of protein tertiary struc- ture. A new protein secondary structure prediction

The prediction of protein secondary structure is an important step in the prediction of protein tertiary structure. A new protein secondary structure prediction method, SVMpsi, was developed to improve the current level of prediction by incorporating new tertiary classi®ers and their jury decision system, and the PSI-BLAST PSSM pro®les. Additionally, ef®cient methods to handle unbalanced data and a new optimization strategy for maximizing the Q3 measure were developed. The SVMpsi produces the highest published Q3 and SOV94 scores on both the RS126 and CB513 data sets to date. For a new KP480 set, the prediction accuracy of SVMpsi was Q3 = 78.5% and SOV94 = 82.8%. Moreover, the blind test results for 136 non-redundant protein sequences which do not contain homologues of training data sets were Q3 = 77.2% and SOV94 = 81.8%. The SVMpsi results in CASP5 illustrate that it is another competitive method to predict protein secondary structure.
Engineering Applications of Arti ﬁ cial Intelligence

Fuzzy Petri nets (FPNs) are a potential modeling technique for knowledge representation and reasoning of rule-based expert systems. To date, many studies have focused on the improvement of FPNs and various new algorithms and models have been proposed in the literature to enhance the modeling power and applicability of FPNs. However, no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms. Giving this evolving research area, this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models. In addition, we provide a survey of the applications of FPNs for solving practical problems in variety of ﬁ elds. Finally, research trends in the current literature and potential directions for future investigations are pointed out, providing insights and robust roadmap for further studies in this ﬁ eld.
Undergraduate Economic Review Undergraduate Economic Review

Abstract I look for evidence of reference-dependent preferences in the National Football League (NFL). Under reference-dependent preferences, sports fans should react more strongly to surprising wins and losses than expected wins or losses. I use Google Trends to look at the impact of NFL game outcomes on the use of positive or negative words on Google search. While search activity did respond to NFL games, I did not find that this response was sensitive to how surprising the outcome was, and so did not find evidence of reference-dependent preferences.
Title: Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies Running Title: MRI-Based Pseudo-CT Synthesis for PET/MR

1 Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Madrid, Spain 2 Madrid-MIT M+Vision Consortium, Madrid, Madrid, Spain 3 Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA 4 Dept. of Computer Science, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain 5 Hospital Universitario HM Puerta del Sur, HM Hospitales, Mostoles, Madrid, Spain 6 MAP5, CNRS UMR 8145, University Paris Descartes, Paris, Paris, France
Computing eigenvalues of diagonalizable matrices in a quantum computer.

Solving linear systems and computing eigenvalues are two fundamental problems in linear algebra. For solving linear systems, many efficient quantum algorithms have been discovered. For computing eigenvalues, currently, we have efficient quantum algorithms for Hermitian and unitary matrices. However, the general case is far from fully understood. Combining quantum phase estimation, quantum algorithm to solve linear differential equations and quantum singular value estimation, we propose two quantum algorithms to compute the eigenvalues of diagonalizable matrices that only have real eigenvalues and normal matrices. The output of the quantum algorithms is a superposition of the eigenvalues and the corresponding eigenvectors. The complexities are dominated by solving a linear system of ODEs and performing quantum singular value estimation, which usually can be solved efficiently in a quantum computer. In the special case when the matrix $M$ is $s$-sparse, the complexity is $\widetilde{O}(s\rho^2 \kappa^2/\epsilon^2)$ for diagonalizable matrices that only have real eigenvalues, and $\widetilde{O}(s\rho\|M\|_{\max} /\epsilon^2)$ for normal matrices. Here $\rho$ is an upper bound of the eigenvalues, $\kappa$ is the conditioning of the eigenvalue problem, and $\epsilon$ is the precision to approximate the eigenvalues. We also extend the quantum algorithm to diagonalizable matrices with complex eigenvalues under an extra assumption.
Energy-Efﬁcient Resource Allocation for UAV-Enabled Information and Power Transfer with NOMA

—This paper investigates the energy efﬁciency optimization in a wireless communication network in which an unmanned aerial vehicle (UAV) deploys information and power transfer towards co-located information and energy receivers to enable downlink and uplink data transmission through non-orthogonal multiple access (NOMA). Using a constructed closed-form expression for the energy efﬁciency, a resource allocation mechanism aiming at maximizing the energy efﬁciency of the system is developed. To this end, an algorithm which jointly takes into account the downlink and uplink stages is proposed, using Lagrangian optimization and gradient decent methods. Numerical results and comparisons are provided. In particular, the results show an enhancement in energy efﬁciency for the NOMA scheme compared with OMA, and that less wireless power transfer time will be needed from the UAV to simultaneously charge energy receivers when using NOMA.
Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach

Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.
Molecular phylogenetics : state-ofthe-art methods for looking into the past

Only through MOLECULAR PHYLOGENETIC (see Glossary) studies can we understand fully the rapidly accumulating genomic sequence data and information regarding proteins' structure and function. Phylogenetic analyses used to be restricted to descriptive and speculative studies of evolutionary relationships, but recent advances in methodology allow us to recognize and exploit statistical dependencies among sequences sharing common ancestry in broader fields of research 1. Advances in two areas led to these radically improved methodologies. First, mathematical models of sequence evolution were improved greatly by the incorporation of biological, biochemical and evolutionary knowledge. Second, statistical testing has become an indispensable tool for phylogeneticists, allowing robust evaluation of complex evolutionary hypotheses 2. The widespread use of accurate models of evolution and statistical tests is necessary to extract the maximum amount of information from molecular sequence data. Many examples in the literature are still employing outdated methods and risk drawing erroneous or weakened conclusions. Here, we review the state of the art in practical molecular phylogenetic analysis. To be both powerful and robust, statistical inference techniques require accurate probabilistic models of the biological processes that generate the data observed. For the phylogenetic analysis of aligned sequences, virtually all methods describe sequence evolution using a model that consists of two components: a PHYLOGENETIC TREE and a description of the way individual sequences evolve by nucleotide or amino acid replacement along the branches of that tree. These replacements are usually described as the products of chance mutation events, and their occurrence at each sequence site is mathematically modelled by a MARKOV PROCESS. Typically, the same Markov process model is applied to each sequence site. Different models are distinguished by their assumptions or PARAMETERIZATIONS regarding the average rates of occurrence of all the possible replacements 3. It is now well established that more complex models, often describing replacement rates in terms of a variety of biological phenomena, generally give a statistically better fit to observed patterns of sequence evolution 4–9 , giving more accurate and robust estimates both of phylogeny 10–12 and the statistical confidence in the phylogeny 13. The development of more accurate models of sequence evolution has been a particularly active and fertile area of research in recent years, in the expectation that improved models will lead to improved phylogenetic inferences and understanding of molecular sequence evolution. There are two approaches to building models of sequence evolution. One approach is to build models EMPIRICALLY using properties …
Learnability, Stability and Uniform Convergence

The problem of characterizing learnability is the most basic question of statistical learning theory. A fundamental and long-standing answer, at least for the case of supervised classification and regression, is that learnability is equivalent to uniform convergence of the empirical risk to the population risk, and that if a problem is learnable, it is learnable via empirical risk minimization. In this paper, we consider the General Learning Setting (introduced by Vapnik), which includes most statistical learning problems as special cases. We show that in this setting, there are non-trivial learning problems where uniform convergence does not hold, empirical risk minimization fails, and yet they are learnable using alternative mechanisms. Instead of uniform convergence, we identify stability as the key necessary and sufficient condition for learnability. Moreover, we show that the conditions for learnability in the general setting are significantly more complex than in supervised classification and regression.
Affective Characterization of Movie Scenes Based on Multimedia Content Analysis and User's Physiological Emotional Responses

In this paper, we propose an approach for affective representation of movie scenes based on the emotions that are actually felt by spectators. Such a representation can be used for characterizing the emotional content of video clips for e.g. affective video indexing and retrieval, neuromarketing studies, etc. A dataset of 64 different scenes from eight movies was shown to eight participants. While watching these clips, their physiological responses were recorded. The participants were also asked to self-assess their felt emotional arousal and valence for each scene. In addition, content-based audio- and video-based features were extracted from the movie scenes in order to characterize each one. Degrees of arousal and valence were estimated by a linear combination of features from physiological signals, as well as by a linear combination of content-based features. We showed that a significant correlation exists between arousal/valence provided by the spectator's self-assessments, and affective grades obtained automatically from either physiological responses or from audio-video features. This demonstrates the ability of using multimedia features and physiological responses to predict the expected affect of the user in response to the emotional video content.
Color Theory in Experience Design

Outside the visual arts, color is rarely discussed by professionals in systematic ways; among UX and LX designers, color is generally approached in a strange give-and-take between technical prescription and intuitive preference. For instance, the color system outlined in Google’s (n.d.) material design visual language provides precise guidance on how to generate a color palette from your primary color, what to use secondary colors for, and what colors are typical for specific elements (such as error screens), but it does not provide designers any guidance on what primary color to pick in the first place, when to use different types of color palettes (e.g., analogous, complementary, triadic), and why. This is likely because, when designing for a corporate client, designers are generally constrained by the preexisting branding requirements of the client (e.g., “our brand is periwinkle”) and must work from a particular color starting point when making designs.
Tutorial: Towards Robust Deep Learning against Poisoning Atacks

Deep Learning (DL) has been increasingly deployed in various real-world applications due to its unprecedented performance and automated capability of learning hidden representations. While DL can achieve high task performance, the training process of a DL model is both time and resource-consuming. Therefore, current supply chains of the DL models assume the customers obtain pre-trained Deep Neural Networks (DNNs) from the third-party providers that have suicient computing power. In the centralized setting, the model designer trains the DL model using the local dataset. However, the collected training data may contain erroneous or poisoned data points. The model designer might craft malicious training samples and inject a backdoor in the DL model distributed to the users. As the result, the user’s model will malfunction. In the federated learning setting, the cloud server aggregates local models trained on individual local datasets and updates the global model. In this scenario, the local client could poison the local training set and/or arbitrarily manipulate the local update. If the cloud server incorporates the malicious local gradients in model aggregation, the resulting global model will have degraded performance or backdoor behaviors. In this paper, we present a comprehensive overview of contemporary data poisoning and model poisoning attacks against DL models in both centralized and federated learning scenarios. In addition, we review existing detection and defense techniques against various poisoning attacks.
Dynamic Path-Based Equilibrium Assignment With Microscopic Traffic Simulation

This report summarizes research work conducted under TO4158 at the California ATMS Testbed of the Institute of Transportation Studies at the University of California, Irvine. Under this task order, the California ATMS testbed hosted two full-time PATH research postdocs (Henry Liu and Lianyu Chu) whose general responsibilities are focused on applications of ATMS in the Testbed environment. They are generally responsible for ensuring that the functional capabilities of the Testbed are designed, developed and maintained in a manner that complements and enhances the ATMS research objectives of the PATH program. Under the direction of PATH faculty researchers at UCI and the Testbed management team, they are generally responsible for software enhancements to the Laboratory "bench-top" system for modeling and evaluating ATMS, particularly with the microscopic simulation model Paramics. They also provided Caltrans the on-call support and technical guidance on various Caltrans micro-simulation projects related to the Paramics plugin modules developed at UCI. In addition to the general responsibilities outlined above, the PATH Researchers have specific research projects. The specific research project conducted under this task order is to develop a path-based equilibrium assignment model with micro-simulator Paramics. Since off-the-shlef Paramics can only do the link-based assignment, no path information during the traffic assignment process is stored nor provided. This will bring difficulties in the ATMS evaluation particularly related to the route diversion because partial or full path information is needed to conduct this type of evaluation with Paramics. To overcome this difficulty, a path-based assignment model is developed using Paramics Application Programming Interfaces (API) functions. The model comprises the advantages of the analytical traffic assignment model and the good properties of the simulation tools, which can represented the real world more properly. Some key techniques, route choice plug-in, turn penalty consideration and methods of successive average, and etc., were applied in the model. The Paramics V4 was selected as the demonstration simulation tool. A real grid network in Tucson, Arizona, was designed to test the performance of the model, and the results showed that the model converged to the user equilibrium as expected.
Non-linear CNN-based Read Channel for Hard Disk Drive with 30% Error Rate Reduction and Sequential 200Mbits/second Throughput in 28nm CMOS

In this work, we present the first ASIC implementation of CNN-based data detection channel for HDDs with 30.3% error rate reduction than the SOTA detection channel. Our chip demonstrates the first methodology of systematically embedding non-linear capacity into HDD read channel with the following features (1) a fully unrolled CNN with dedicated silicon for each convolution layer to produce fast sequential time series data detection at 200 Mbits/s, (2) in total 6 depthwise-separable convolution layers implemented with 2 types of systolic arrays and 100% PE utilization for continuous data flow and high pipelining, (3) integer-only convolutions for improved efficiency at 0.86nJ/bit and 3.99TOPS/W, and (4) pipelined (QReLU) to maintain low-precision feature maps and recover accuracy loss from model quantization.