Miracle-dz/UniFAR-FTUs huggingface dataset:
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment

Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding. It plays an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. However, existing meth-ods extract features from the whole video and article and use fusion methods to select the representative one, thus usually ignoring the critical structure and varying semantics with video/document. In this work, we propose a Semantics-Consistent Cross-domain Summa-rization (SCCS) model based on optimal transport alignment with visual and textual segmentation. Our method first decomposes both videos and articles into segments in order to capture the structural semantics, and then follows a cross-domain alignment objective with optimal transport distance, which leverages multimodal interaction to match and select the visual and textual summary. We evaluated our method on three MSMO datasets, and achieved performance improvement by 8% & 6% of textual and 6.6% &5.7% of video summarization, respectively, which demonstrated the effectiveness of our method in producing high-quality multimodal summaries.
Field theory analysis and design of circular waveguide components

R F / microwave terrestria l point-to-point and satellite com m unication systems em ­ ploy a large num ber of waveguide com ponents operating a t microwave and m illim eter wave frequencies, . \ccu ra te design of these com ponents for optim um perform ance of the overall system is critical. To achieve this, com putationally efficient and ac­ cu ra te num erical m ethods are indispensable tools in the design and optim ization of com ponents and subsystem s. Among th e large variety of potentially suitable num erical m ethods, the m ode m atching m ethod in conjunction with the generalized scattering m atrix technique has been found to be one of the most reliable and straightforw ard techniques to design waveguide com ponents. In the past the m ethod has been m ainly applied to eigenvalue an d scattering problems in rectangular waveguides. In this thesis, the m ode m atching m ethod is extended to ridge waveguide problem s in circular waveguides and thus closes a gap in the lite ra tu re th a t has existed for a long time. T he thesis begins with a study of the basic principles of the mode m atching m ethod as it is known from rectangular waveguides. These principles are then applied to the analysis of th e rectangular ridged and coaxial waveguide, followed by an eigenvalue analysis of ridged circular waveguide. R ather than rectangular ridges, ridges of uni­ form angular thickness(conically shaped) are used in the circular waveguide to avoid a m ixed coord inate system which would render the m ode m atching m ethod com pu­ ta tionally very inefficient. On the o ther hand, conically shaped ridges are as easy to fabricate as rectangular ridges and are not detrim ental to the electrical perform ance of th e com ponent. The thesis then continues to trea t the discontinuity problem at the interface be­ tween the em p ty circular waveguide and ridged circular waveguide. To verify the com puted sca tte rin g param eters, m easurem ents were perform ed and good agreement
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, (cid:96) 2 -consistency and Neuroscience Applications

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, `2-consistency and Neuroscience Applications (Supplementary Material)
Non-equilibrium dynamics of Chern insulators

Topological states of matter exhibit a wealth of novel properties including the exact quantisation of macroscopic observables and the presence of edge states. In this thesis, we study the non-equilibrium dynamics of a class of topological phases, known as Chern insulators. By focusing on the Haldane model, we study quenches between the topological and non-topological phases, and the dynamics induced on physical observables. A notable feature is that the Chern number, calculated for an infinite system, is unchanged under the dynamics following such a quench. However, in finite-size geometries, the initial and final Hamiltonians are distinguished by the presence or absence of edge states. We study the edge excitations and describe their impact on the dynamics of the edge currents and the magnetisation. We show that, following a quantum quench, the edge currents relax towards new steady-state values, and that there is light-cone spreading of the currents into the interior of the sample. The late-time behaviour of the edge currents, after multiple traversals of the sample, is captured by a Generalised Gibbs Ensemble. We further provide an analysis of the Hall response following a quantum quench in an isolated system, with explicit results for the Haldane model. We show that the Hall conductance is no longer related to the Chern number in the post-quench state, in contrast to the equilibrium case. We also discuss the effects of generic open boundary conditions and confinement potentials. Finally, we discuss the impact of disorder on the phases of the Haldane model, both in and out of equilibrium. We conclude with a discussion of ongoing work on the non-equilibrium dynamics of the entanglement spectrum of the Haldane model, and with prospects for further research. The results presented in Chapters 2 and 3 are published in Refs [1, 2]. The results in Chapter 4 are currently in preparation for publication [3].
Robust Comparison in Population Protocols

There has recently been a surge of interest in the computational and complexity properties of the population model, which assumes $n$ anonymous, computationally-bounded nodes, interacting at random, and attempting to jointly compute global predicates. In particular, a significant amount of work, has gone towards investigating majority and consensus dynamics in this model: assuming that each node is initially in one of two states $X$ or $Y$, determine which state had higher initial count. In this paper, we consider a natural generalization of majority/consensus, which we call comparison. We are given two baseline states, $X_0$ and $Y_0$, present in any initial configuration in fixed, possibly small counts. Importantly, one of these states has higher count than the other: we will assume $|X_0| \ge C |Y_0|$ for some constant $C$. The challenge is to design a protocol which can quickly and reliably decide on which of the baseline states $X_0$ and $Y_0$ has higher initial count. We propose a simple algorithm solving comparison: the baseline algorithm uses $O(\log n)$ states per node, and converges in $O(\log n)$ (parallel) time, with high probability, to a state where whole population votes on opinions $X$ or $Y$ at rates proportional to initial $|X_0|$ vs. $|Y_0|$ concentrations. We then describe how such output can be then used to solve comparison. The algorithm is self-stabilizing, in the sense that it converges to the correct decision even if the relative counts of baseline states $X_0$ and $Y_0$ change dynamically during the execution, and leak-robust, in the sense that it can withstand spurious faulty reactions. Our analysis relies on a new martingale concentration result which relates the evolution of a population protocol to its expected (steady-state) analysis, which should be broadly applicable in the context of population protocols and opinion dynamics.
Nonlinear Acceleration of Stochastic Algorithms

A BSTRACT . Extrapolation methods use the last few iterates of an optimization algorithm to produce a better estimate of the optimum. They were shown to achieve optimal convergence rates in a deterministic setting using simple gradient iterates. Here, we study extrapolation methods in a stochastic setting, where the iterates are produced by either a simple or an accelerated stochastic gradient algorithm. We ﬁrst derive convergence bounds for arbitrary, potentially biased perturbations, then produce asymptotic bounds using the ratio between the variance of the noise and the accuracy of the current point. Finally, we apply this acceleration technique to stochastic algorithms such as SGD, SAGA, SVRG and Katyusha in different settings, and show signiﬁcant performance gains.
Semiparametric Causal Sufﬁcient Dimension Reduction of Multidimensional Treatments (Supplementary Material)

For clearer presentation of materials and equations in this supplement, we switch to a single-column format. Appendix A contains a brief overview of inference in semiparametric models. Appendix B contains additional discussion on how to ensure the nuisance models are trained in a congenial manner. Appendix C contains additional results with simulated data and real data application. Appendix D contains all the proofs.
Parameter-Free Style Projection for Arbitrary Style Transfer

Arbitrary image style transfer is a challenging task which aims to stylize a content image conditioned on an arbitrary style image. In this task the content-style feature transformation is a critical component for a proper fusion of features. Existing feature transformation algorithms often suffer from unstable learning, loss of content and style details, and non-natural stroke patterns. To mitigate these issues, this paper proposes a parameter-free algorithm, Style Projection, for fast yet effective content-style transformation. To leverage the proposed Style Projection~component, this paper further presents a real-time feed-forward model for arbitrary style transfer, including a regularization for matching the content semantics between inputs and outputs. Extensive experiments have demonstrated the effectiveness and efficiency of the proposed method in terms of qualitative analysis, quantitative evaluation, and user study.
Generative Adversarial Networks: What Are They and Why We Should Be Afraid

Machine Learning is an incredibly useful tool when it comes to cybersecurity, allowing for advance detection and protection mechanisms for securing our data. One particularly potent machine learning concept is the Generative Adversarial Network (GAN) which is the key focus of this paper. The GAN has many applications related to cyber security including strengthening existing attacks to levels beyond what can be handled by a basic detection system. As GANs rise in popularity, the need to defend against and recognize GAN attacks also becomes increasingly urgent. This paper will detail the methods of GAN attacks and attempt to answer the question of how can we defend against these attacks.
HMANet: Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding. Most of the current approaches are based on deep convolutional neural networks (DCNNs). However, standard convolution with local receptive fields fails in modeling global dependencies. Prior researches have indicated that attention-based methods can capture long-range dependencies and further reconstruct the feature maps for better representation. Nevertheless, limited by the mere perspective of spacial and channel attention and huge computation complexity of self-attention mechanism, it is unlikely to model the effective semantic interdependencies between each pixel-pair of remote sensing data of complex spectra. In this work, we propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations from the perspective of space, channel and category in a more effective and efficient manner. Concretely, a class augmented attention (CAA) module embedded with a class channel attention (CCA) module can be used to compute category-based correlation and recalibrate the class-level information. Additionally, we introduce a simple yet effective region shuffle attention (RSA) module to reduce feature redundant and improve the efficiency of self-attention mechanism via region-wise representations. Extensive experimental results on the ISPRS Vaihingen and Potsdam benchmark demonstrate the effectiveness and efficiency of our HMANet over other state-of-the-art methods.
Geoacoustic inversion for bottom parameters in the deep-water area of the South China Sea

The properties of bottom acoustic parameters are greatly influenced on sound field prediction. Acoustic parameters in deep water are not well understood. We will discuss experience with geoacoustic inversion of transmission-loss (TL) data from 100 to 600 Hz from deep water regions. Bottom acoustic parameters are sensitive to the TL data in the shadow zone of deep water. Multiple steps TL inversion method is proposed to invertsound speed, density and attenuation in deep water. Both a uniform liquid half-space and a two-layer geoacoustic profile are assumed. Sound speeds of bottom are inverted by using TL data in the shadow zones at low frequency obtained in an acoustic propagation experiment conducted in the South China Sea (SCS) in summer 2014. Meanwhile, bottom densities are estimated combining with the Hamilton sediment empirical relationship. Attenuation coefficients at different frequencies are then estimated from the long range TL data by using the known sound speeds and densities as a constraint condition. The nonlinear relationship between attenuation coefficient and frequency is given in the end. The inverted bottom parameters can be used to forecast the transmission loss in deep water area of SCS very well.The properties of bottom acoustic parameters are greatly influenced on sound field prediction. Acoustic parameters in deep water are not well understood. We will discuss experience with geoacoustic inversion of transmission-loss (TL) data from 100 to 600 Hz from deep water regions. Bottom acoustic parameters are sensitive to the TL data in the shadow zone of deep water. Multiple steps TL inversion method is proposed to invertsound speed, density and attenuation in deep water. Both a uniform liquid half-space and a two-layer geoacoustic profile are assumed. Sound speeds of bottom are inverted by using TL data in the shadow zones at low frequency obtained in an acoustic propagation experiment conducted in the South China Sea (SCS) in summer 2014. Meanwhile, bottom densities are estimated combining with the Hamilton sediment empirical relationship. Attenuation coefficients at different frequencies are then estimated from the long range TL data by using the known sound speeds and densities as a constraint con.
Title : Democratization in a passive dendritic tree : an analytical

One way to achieve amplification of distal synaptic inputs on a dendritic tree is to scale the amplitude and/or duration of the synaptic conductance with its distance from the soma. This is an example of what is often referred to as “dendritic democracy”. Although well studied experimentally, to date this phenomenon has not been thoroughly explored from a mathematical perspective. In this paper we adopt a passive model of a dendritic tree with distributed excitatory synaptic conductances and analyze a number of key measures of democracy. In particular, via moment methods we derive laws for the transport, from synapse to soma, of strength, characteristic time, and dispersion. These laws lead immediately to synaptic scalings that overcome attenuation with distance. We follow this with a Neumann approximation of Green’s representation that readily produces the synaptic scaling that democratizes the peak somatic voltage response. Results are obtained for both idealized geometries and for the more realistic geometry of a rat CA1 pyramidal cell. For each measure of democratization we produce and contrast the synaptic scaling associated with treating the synapse as either a conductance change or a current injection. We find that our respective scalings agree up Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK. email: y.timofeeva@warwick.ac. uk Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005 , USA. email: cox@rice.edu School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK. email: stephen.coombes@nottingham.ac. uk Department of Mathematics, University of Houston, Houston, TX 77204-3008, USA. email: josic@math.uh. edu
Investigation of compact broadband quasi-planar rectangular ring cavity power-combining amplifier

ABSTRACT A compact broadband quasi-planar rectangular ring cavity (RRC) power-combining amplifier is presented. To realize the broadband impedance matching, high assembly precision, and the strong coupling between the RRC and the output ports, the novel inserted ground waveguide probe structure was proposed. The measured insertion loss of the passive power combiner is increased from 0.6 to 1.7 dB at the bandwidth 6.5–14.2 GHz. The measured small-signal gain of the four-device power-combining amplifier is 22.2–26.7 dB over a broadband from 8 to 14 GHz. The measured maximum output power at 1-dB compression is 37.7 dBm at 11.4 GHz, with a power-combining efficiency of about 89%. The measured results of the passive power combiner agree with the simulated ones closely.
Joule-Thomson expansion of five-dimensional Einstein-Maxwell-Gauss-Bonnet-AdS black holes

We investigate the Joule-Thomson expansion for five-dimensional Einstein-Maxwell-Gauss-Bonnet-AdS black holes. Here, we derive the expressions for the Joule-Thomson coefficient μ in two different approaches and show that both of these approaches are consistent with each other. Further, we analyze both inversion and isenthalpic curves in the plane. Then we determine the cooling-heating regions. Finally, we compute the ratio between minimum inversion temperature and critical temperature Tc for the said black holes and show that this ratio has two values unlike the charged AdS black holes and van der Waals' fluids as we achieve two critical temperatures from two equations. We also show graphically as well as by computing the critical points that the Gauss-Bonnet coupling constant α can have only positive values if the black hole is small. This paper is dedicated to Prof. Subenoy Chakraborty, Department of Mathematics, Jadavpur University, India, to tribute him on his 60th birth year.
Performance limits analysis of linear predictive control systems with input saturation

This paper proposes a novel method to analyze performance features of linear model predictive control (MPC) systems with input saturation. The performance of MPC is compared with that of saturated control to find out how well MPC can perform under input saturation case. By exploring the properties of Riccati difference equation (RDE) and geometric structure of linear MPC, upper and lower bounds of the ratio between performance of MPC and saturated control are obtained. The establishment of such relationship provides a feasible approach to quantify the performance enhancement by introducing receding horizon optimization method into input saturated systems.
Skeletonization: a Technique for Trimming the Fat from a Network via Relevance Assessment, 6.3.2 Other Pruning Methods

Stock performance modeling using neural networks: A comparative study with regression models', Neural Networks. fully connected network, it is not sufficient to stop removing weights when b P() first increases. This is particularly true given that both SBP and OBD are greedy algorithms, and they neglect interactions between multiple inputs and weights. Thus, removing additional inputs or weights even after b P() first increases can lead to a further reduction of the prediction risk and thus yield a smaller final network. A number of other pruning methods besides those we have described here are potentially effective and should be considered when constructing neural network models. These include the irrelevant hidden unit and irrelevant input hypothesis tests (White, 1989), pruning of units via skeletonization (Mozer and Smolensky, 1990), optimal brain surgeon OBS (Hassibi and Stork, 1993), and principal components pruning PCP (Levin et al., 1994). It is important to note that all these methods, along with OBD and our method of input pruning via SBP, are closely related to the Wald hypothesis testing procedure (see for example Buse (1982)). In fact, the saliencies used in OBD, OBS, and SBP are special cases of the Wald test statistic. Acknowledgements The author thanks Joachim Utans for help in preparing this manuscript and Steve Rehfuss and Eric Wan for careful proof readings. The author is responsible for any remaining errors.Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation', Numer. OBD is designed to select those weights in the network whose removal will have a small effect on the training average squared error (ASE). Assuming that the original network is too large, removing these weights and retraining the now smaller network should improve the generalization performance.
Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model

Wind farm ﬂow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm ﬂows have been essential for exploring control strategies such as wake redirection and dynamic induction control. Free vortex methods can provide a computationally eﬃcient way to model wind turbine wake dynamics for control optimisation. We present a control-oriented free-vortex wake model of a 2D and 3D actuator disc to represent wind turbine wakes. The novel derivation of the discrete adjoint equations allows eﬃcient gradient evaluation for gradient-based optimisation in an economic model-predictive control algorithm. Initial results are presented for mean power maximisation in a two-turbine case study. An induction control signal is found using the 2D model that is roughly periodic and supports previous results on dynamic induction control to stimulate wake mixing. The 3D model formulation eﬀectively models a curled wake under yaw misalignment. Under time-varying wind direction, the optimisation ﬁnds solutions demonstrating both wake steering and a smooth transition to greedy control. The free-vortex wake model with gradient information shows potential for eﬃcient optimisation and provides a promising way to further explore dynamic wind farm ﬂow control.