Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfiguration Problems on Submodular Functions

\emphReconfiguration problems require finding a step-by-step transformation between a pair of feasible solutions for a particular problem. The primary concern in Theoretical Computer Science has been revealing their computational complexity for classical problems. This paper presents an initial study on reconfiguration problems derived from a submodular function, which has more of a flavor of Data Mining. Our submodular reconfiguration problems request to find a solution sequence connecting two input solutions such that each solution has an objective value above a threshold in a submodular function $f: 2^[n] \to \mathbbR _+$ and is obtained from the previous one by applying a simple transformation rule. We formulate three reconfiguration problems: Monotone Submodular Reconfiguration (MSReco), which applies to influence maximization, and two versions of Unconstrained Submodular Reconfiguration (USReco), which apply to determinantal point processes. Our contributions are summarized as follows: \beginitemize \item We prove that MSReco and USReco are both PSPACE-complete. \item We design a $\frac1 2 $-approximation algorithm for MSReco and a $\frac1 n $-approximation algorithm for (one version of) USReco. \item We devise inapproximability results that approximating the optimum value of MSReco within a $(1-\frac1+ε n^2 )$-factor is PSPACE-hard, and we cannot find a $(\frac5 6 +ε)$-approximation for USReco. \item We conduct numerical study on the reconfiguration version of influence maximization and determinantal point processes using real-world social network and movie rating data. \enditemize
Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes

We consider the product of determinantal point processes (DPPs), a point process whose probability mass is proportional to the product of principal minors of multiple matrices, as a natural, promising generalization of DPPs. We study the computational complexity of computing its normalizing constant, which is among the most essential probabilistic inference tasks. Our complexity-theoretic results (almost) rule out the existence of efficient algorithms for this task unless the input matrices are forced to have favorable structures. In particular, we prove the following: (1) Computing $\sum_S\det({\bf A}_{S,S})^p$ exactly for every (fixed) positive even integer $p$ is UP-hard and Mod$_3$P-hard, which gives a negative answer to an open question posed by Kulesza and Taskar. (2) $\sum_S\det({\bf A}_{S,S})\det({\bf B}_{S,S})\det({\bf C}_{S,S})$ is NP-hard to approximate within a factor of $2^{O(|I|^{1-\epsilon})}$ or $2^{O(n^{1/\epsilon})}$ for any $\epsilon>0$, where $|I|$ is the input size and $n$ is the order of the input matrix. This result is stronger than the #P-hardness for the case of two matrices derived by Gillenwater. (3) There exists a $k^{O(k)}n^{O(1)}$-time algorithm for computing $\sum_S\det({\bf A}_{S,S})\det({\bf B}_{S,S})$, where $k$ is the maximum rank of $\bf A$ and $\bf B$ or the treewidth of the graph formed by nonzero entries of $\bf A$ and $\bf B$. Such parameterized algorithms are said to be fixed-parameter tractable. These results can be extended to the fixed-size case. Further, we present two applications of fixed-parameter tractable algorithms given a matrix $\bf A$ of treewidth $w$: (4) We can compute a $2^{\frac{n}{2p-1}}$-approximation to $\sum_S\det({\bf A}_{S,S})^p$ for any fractional number $p>1$ in $w^{O(wp)}n^{O(1)}$ time. (5) We can find a $2^{\sqrt n}$-approximation to unconstrained MAP inference in $w^{O(w\sqrt n)}n^{O(1)}$ time.
Test Data Compression Based on Threshold Method for Power Reduction

Testing a given circuit is a very important issue in todayOs situation due to high test cost. Tester mainly focuses on power dissipation and test data volume. Compression technique is implemented to reduce test power which causes the chip failure and is also used to reduce test data size. This study discusses reduction of test data and scan chain shift-in power. Proposed method on compressing test data is done by threshold method for unspecified test patterns. Appropriate indexing and encoding is done in this method. An analysis of shift in power is carried out for encoded compressed pattern by considering its switching activity. Experimental results prove that the proposed method can produce rapid reduction in test data volume and shift- in power.
Customizing an Affective Tutoring System Based on Facial Expression and Head Pose Estimation

In recent years, the main problem in e-learning has shifted from analyzing content to personalization of learning environment by Intelligence Tutoring Systems (ITSs). Therefore, by designing personalized teaching models, learners are able to have a successful and satisfying experience in achieving their learning goals. Affective Tutoring Systems (ATSs) are some kinds of ITS that can recognize and respond to affective states of learner. In this study, we designed, implemented, and evaluated a system to personalize the learning environment based on the facial emotions recognition, head pose estimation, and cognitive style of learners. First, a unit called Intelligent Analyzer (AI) created which was responsible for recognizing facial expression and head angles of learners. Next, the ATS was built which mainly made of two units: ITS, IA. Results indicated that with the ATS, participants needed less efforts to pass the tests. In other words, we observed when the IA unit was activated, learners could pass the final tests in fewer attempts than those for whom the IA unit was deactivated. Additionally, they showed an improvement in terms of the mean passing score and academic satisfaction.
Symmetrically Colored Gaussian Graphical Models with Toric Vanishing Ideals

A colored Gaussian graphical model is a linear concentration model in which equalities among the concentrations are specified by a coloring of an underlying graph. The model is called RCOP if this coloring is given by the edge and vertex orbits of a subgroup of the automorphism group of the graph. We show that RCOP Gaussian graphical models on block graphs are toric in the space of covariance matrices and we describe Markov bases for them. To this end, we learn more about the combinatorial structure of these models and their connection with Jordan algebras.
Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning

In this paper, we consider both firstand second-order techniques to address continuous optimization problems arising in machine learning. In the firstorder case, we propose a framework of transition from deterministic or semideterministic to stochastic quadratic regularization methods. We leverage the twophase nature of stochastic optimization to propose a novel first-order algorithm with adaptive sampling and adaptive step size. In the second-order case, we propose a novel stochastic damped L-BFGS method that improves on previous algorithms in the highly nonconvex context of deep learning. Both algorithms are evaluated on well-known deep learning datasets and exhibit promising performance.
CFD Analysis of Oscillating Blades for Small Horizontal Axis Wind Turbines in Dynamic Stall Condition

Unsteady aerodynamics and specifically the dynamic stall phenomenon significantly affect the aerodynamic performance of the wind turbine blades. This paper presents a 2D computational investigation on the aerodynamic characteristics of three specific airfoils for small horizontal axis wind turbines subjected to unsteady viscous flow. The unsteady incompressible Navier-Stokes equations are considered, and ANSYS-Fluent CFD code is used for the flow simulation. The simulation method is validated by calculating the aerodynamic coefficients of a pitching NACA23012 airfoil and comparing the results with the corresponding published experimental data. A complete set of dynamic simulations is then performed to find lift and drag forces acting on the airfoils in different unsteady conditions such as various Reynolds numbers and different amplitude and frequency of pitching oscillations. The results of this study emphasize that the curvature of leading edge and the thickness of the airfoil have major roles in early flow separation and dynamic stall of a pitching airfoil. The dynamic stall has wide negative effects on the performance of the blades such as lift reduction, drag increment and delay in the flow reattachment that are discussed in this paper.
Natural language processing in-and-for design research

Abstract We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.
Cycloconverter operation under a new scalar control algorithm

A thorough analysis is conducted of a novel scalar control method for the operation of direct frequency changers using bidirectional switches that operate at high switching frequencies. The method uses the instantaneous voltage ratio of specific input phase voltages to generate the active and zero states of the various switches. A voltage transfer ratio as high as 0.87 is obtained under synchronous and asynchronous operation with a conventionally connected three-phase load. Moreover, the converter operation can be set to produce almost any power displacement factor, independently of load power factor. <<ETX>>
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation

Off-policy policy evaluation methods for sequential decision making can be used to help identify if a proposed decision policy is better than a current baseline policy. However, a new decision policy may be better than a baseline policy for some individuals but not others. This has motivated a push towards personalization and accurate per-state estimates of heterogeneous treatment effects (HTEs). Given the limited data present in many important applications, individual predictions can come at a cost to accuracy and confidence in such predictions. We develop a method to balance the need for personalization with confident predictions by identifying subgroups where it is possible to confidently estimate the expected difference in a new decision policy relative to a baseline. We propose a novel loss function that accounts for uncertainty during the subgroup partitioning phase. In experiments, we show that our method can be used to form accurate predictions of HTEs where other methods struggle.
Microstrip bandpass single-pole quadruple-throw switch and independently switchable quadruplexer

Two switchable microstrip bandpass circuits are investigated. Owing to the low loading effect from the distributed coupling technique, many channels are obtained without a complicated matching network. Each passband can be turned on and off independently. Both the bandpass single-pole quadruple-throw switch and the switchable quadruplexer consisting of asymmetrical quarter-wavelength resonators, shunt open stubs and p-i-n diodes feature compactness, high isolation and wide stopband. The isolation is better than 35 dB, and the 20-dB stopband extends up to 13 times of the lowest operation centre frequency. To validate this design concept, theory as well as experiment are presented and compared.
An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation

Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representations. For example, in a human-face dataset, if an image contains a hat on a head, the hat should be removed or the importance of its corresponding attributes should be decreased during matrix factorization. This article proposes a new type of NMF called entropy weighted NMF (EWNMF), which uses an optimizable weight for each attribute of each data point to emphasize their importance. This process is achieved by adding an entropy regularizer to the cost function and then using the Lagrange multiplier method to solve the problem. Experimental results with several datasets demonstrate the feasibility and effectiveness of the proposed method. The code developed in this study is available at https://github.com/Poisson-EM/Entropy-weighted-NMF.
Towards Autonomous Driving of Personal Mobility with Small and Noisy Dataset using Tsallis-statistics-based Behavioral Cloning

Autonomous driving has made great progress and been introduced in practical use step by step. On the other hand, the concept of personal mobility is also getting popular, and its autonomous driving specialized for individual drivers is expected for a new step. However, it is difficult to collect a large driving dataset, which is basically required for the learning of autonomous driving, from the individual driver of the personal mobility. In addition, when the driver is not familiar with the operation of the personal mobility, the dataset will contain non-optimal data. This study therefore focuses on an autonomous driving method for the personal mobility with such a small and noisy, so-called personal, dataset. Specifically, we introduce a new loss function based on Tsallis statistics that weights gradients depending on the original loss function and allows us to exclude noisy data in the optimization phase. In addition, we improve the visualization technique to verify whether the driver and the controller have the same region of interest. From the experimental results, we found that the conventional autonomous driving failed to drive properly due to the wrong operations in the personal dataset, and the region of interest was different from that of the driver. In contrast, the proposed method learned robustly against the errors and successfully drove automatically while paying attention to the similar region to the driver. Attached video is also uploaded on youtube: https://youtu.be/KEq8-bOxYQA
Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion

In this work we examine the performance enhancement in classification of medical imaging data when image features are combined with associated non-image data. We compare the performance of eight state-of-the-art deep neural networks in classification tasks when using only image features, compared to when these are combined with patient metadata. We utilise transfer learning with networks pretrained on ImageNet used directly as feature extractors and fine tuned on the target domain. Our experiments show that performance can be significantly enhanced with the inclusion of metadata and use interpretability methods to identify which features lead to these enhancements. Furthermore, our results indicate that the performance enhancement for natural medical imaging (e.g. optical images) benefit most from direct use of pre-trained models, whereas non natural images (e.g. representations of non imaging data) benefit most from fine tuning pre-trained networks. These enhancements come at a negligible additional cost in computation time, and therefore is a practical method for other applications.
Attosecond correlated electron dynamics at C$_{60}$ giant plasmon resonance

1 Max Planck Institute of Quantum Optics, Hans-Kopfermann-Straße 1, D-85748 Garching, Germany 2 Department of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, D-85748 Garching, Germany 3 Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany 4 Science Department, Cabrini University, Radnor, Pennsylvania 19087, USA 5 Department of Natural Sciences, D.L. Hubbard Center for Innovation, Northwest Missouri State University, Maryville, Missouri 64468, USA 6 Bremen Center for Computational Materials Science, University of Bremen, Bremen, Germany 7 Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, D-22761 Hamburg, Germany 8 Institute for Photonics and Nanotechnologies CNR-IFN, P.za Leonardo da Vinci 32, 20133 Milano, Italy 9 Istituto Struttura della Materia, ISM-CNR, 00016 Monterotondo Scalo, Roma, Italy 10 Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy 11 Institut National de la Recherche Scientifique, 1650 Blvd. Lionel-Boulet, J3X 1S2, Varennes (Qc), Canada 12Center for Computational Quantum Physics (CCQ), The Flatiron Institute, New York, NY, USA 13SLAC National Laboratory, 2575 Sand Hill Rd, Menlo Park, CA 94025, USA 14Applied Physics Department, Stanford University, 348 Via Pueblo, Stanford, CA 94305, USA 15 The Hamburg Centre for Ultrafast Imaging, Universität Hamburg, 149 Luruper Chaussee, 22761 Hamburg, Germany 16 Physics Department, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities

Deep Learning (DL) models have achieved superior performance in many application domains, including vision, language, medical, commercial ads, entertainment, etc. With the fast development, both DL applications and the underlying serving hardware have demonstrated strong scaling trends, i.e., Model Scaling and Compute Scaling, for example, the recent pre-trained model with hundreds of billions of parameters with ~TB level memory consumption, as well as the newest GPU accelerators providing hundreds of TFLOPS. With both scaling trends, new problems and challenges emerge in DL inference serving systems, which gradually trends towards Large-scale Deep learning Serving systems (LDS). This survey aims to summarize and categorize the emerging challenges and optimization opportunities for large-scale deep learning serving systems. By providing a novel taxonomy, summarizing the computing paradigms, and elaborating the recent technique advances, we hope that this survey could shed light on new optimization perspectives and motivate novel works in large-scale deep learning system optimization.
P4AI: Approaching AI Ethics through Principlism

The field of computer vision is rapidly evolving, particularly in the context of new methods of neural architecture design. These models contribute to (1) the Climate Crisis - increased CO2 emissions and (2) the Privacy Crisis - data leakage concerns. To address the often overlooked impact the Computer Vision (CV) community has on these crises, we outline a novel ethical framework, \textit{P4AI}: Principlism for AI, an augmented principlistic view of ethical dilemmas within AI. We then suggest using P4AI to make concrete recommendations to the community to mitigate the climate and privacy crises.
Graph Embedding via High Dimensional Model Representation for Hyperspectral Images

Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as encapsulating the high dimensionality in a reduced set of informative features for subsequent classification, regression, or unmixing. Manifold learning methods have shown excellent performance when dealing with hyperspectral image (HSI) analysis, but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample (OOS) data. A common assumption to deal with the problem is that the transformation between the high-dimensional input space and the latent space (typically low) is linear. This is a particularly strong assumption, especially when dealing with HSIs due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on high-dimensional model representation (HDMR) is proposed, which enables a nonlinear embedding function to project OOS samples into the latent space. The proposed method is compared to manifold learning methods along with their linear counterparts and achieves promising performance in terms of classification accuracy for a representative set of HSIs.
Unsupervised MKL in Multi-layer Kernel Machines

Kernel based Deep Learning using multi-layer kernel machines(MKMs) was proposed by Y.Cho and L.K. Saul in \cite{saul}. In MKMs they used only one kernel(arc-cosine kernel) at a layer for the kernel PCA-based feature extraction. We propose to use multiple kernels in each layer by taking a convex combination of many kernels following an unsupervised learning strategy. Empirical study is conducted on \textit{mnist-back-rand}, \textit{mnist-back-image} and \textit{mnist-rot-back-image} datasets generated by adding random noise in the image background of MNIST dataset. Experimental results indicate that using MKL in MKMs earns a better representation of the raw data and improves the classifier performance.
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings

An ensuing challenge in Artificial Intelligence (AI) is the perceived difficulty in interpreting sophisticated machine learning models, whose ever-increasing complexity makes it hard for such models to be understood, trusted and thus accepted by human beings. The lack, if not complete absence, of interpretability for these so-called black-box models can lead to serious economic and ethical consequences, thereby hindering the development and deployment of AI in wider fields, particularly in those involving critical and regulatory applications. Yet, the building services industry is a highly-regulated domain requiring transparency and decision-making processes that can be understood and trusted by humans. To this end, the design and implementation of autonomous Heating, Ventilation and Air Conditioning systems for the automatic but concurrently interpretable optimisation of energy efficiency and room thermal comfort is of topical interest. This work therefore presents an interpretable machine learning model aimed at predicting room temperature in non-domestic buildings, for the purpose of optimising the use of the installed HVAC system. We demonstrate experimentally that the proposed model can accurately forecast room temperatures eight hours ahead in real-time by taking into account historical RT information, as well as additional environmental and time-series features. In this paper, an enhanced feature engineering process is conducted based on the Exploratory Data Analysis results. Furthermore, beyond the commonly used Interpretable Machine Learning techniques, we propose a Permutation Feature-based Frequency Response Analysis (PF-FRA) method for quantifying the contributions of the different predictors in the frequency domain. Based on the generated reason codes, we find that the historical RT feature is the dominant factor that has most impact on the model prediction.