Miracle-dz/UniFAR-FTUs huggingface dataset:
M-estimation with the Trimmed l1 Penalty

We study high-dimensional M-estimators with the trimmed $\ell_1$ penalty. While standard $\ell_1$ penalty incurs bias (shrinkage), trimmed $\ell_1$ leaves the $h$ largest entries penalty-free. This family of estimators include the Trimmed Lasso for sparse linear regression and its counterpart for sparse graphical model estimation. The trimmed $\ell_1$ penalty is non-convex, but unlike other non-convex regularizers such as SCAD and MCP, it is not amenable and therefore prior analyzes cannot be applied. We characterize the support recovery of the estimates as a function of the trimming parameter $h$. Under certain conditions, we show that for any local optimum, (i) if the trimming parameter $h$ is smaller than the true support size, all zero entries of the true parameter vector are successfully estimated as zero, and (ii) if $h$ is larger than the true support size, the non-relevant parameters of the local optimum have smaller absolute values than relevant parameters and hence relevant parameters are not penalized. We then bound the $\ell_2$ error of any local optimum. These bounds are asymptotically comparable to those for non-convex amenable penalties such as SCAD or MCP, but enjoy better constants. We specialize our main results to linear regression and graphical model estimation. Finally, we develop a fast provably convergent optimization algorithm for the trimmed regularizer problem. The algorithm has the same rate of convergence as difference of convex (DC)-based approaches, but is faster in practice and finds better objective values than recently proposed algorithms for DC optimization. Empirical results further demonstrate the value of $\ell_1$ trimming.
A comprehensive numerical model for double-layered porous air journal bearing at higher bearing numbers

Modeling air/gas lubricated double-layered porous journal bearing requires the solution of compressible Reynolds equation. It is observed that at higher bearing numbers, treatment of Reynolds equation with finite difference method using second-order central difference exhibits instabilities due to convective term dominance. To address such instabilities, Reynolds equation is discretized using finite volume and a third-order interpolation scheme to obtain variable values at grid point centers. Multistage Runge–Kutta method and biconjugate gradient stabilized method are used for solving governing equations at film and porous regions, respectively. Steady state and stability characteristics of finite double-layered porous air journal bearings considering Beavers–Joseph velocity slip at porous-film interface are obtained. Numerically stable results are obtained for bearing numbers up to 150 and feeding parameter values ranging from 0.01 to 10.
Learning Steerable Filters for Rotation Equivariant CNNs

The first layer maps an image I : R → R to a feature map ζ : R o Θ → R by first convolving it with multiple rotated versions ρθΨ of a filter Ψ : R → R and subsequently adding a bias β and applying a nonlinearity σ. Both steps are equivariant under rotations of the image by angles α ∈ Θ. This means that ραI(x) is mapped to Rαζ(x, θ) = ραζ(x, θ − α) where Rα is the group action on functions on the group. To see that the first step performs an equivariant mapping, simply insert a rotated image,
Involvement of the Renin‐Angiotensin System in Atherosclerosis

The renin-angiotensin system (RAS) is a well known for its role in the regulation of the blood pressure (BP). Angiotensin II (Ang II), the main mediator of the RAS, may act either, as a systemic molecule or a locally produced factor. Within the vessel wall it has significant proinflammatory role by inducing the oxidative stress, secretion of inflammatory cytokines and adhesion molecules. Ang II could trigger proliferation of vascular smooth muscle cells (VSMC) and its migration to the outer layer of the vessel wall. It could induce the release of matrix metalloproteinase (MMPs), from human VSMC and thus increase susceptibility to rupture of atherosclerotic lesions. Binding of Ang II to AT1R/AT2R could have opposing actions in vascular injury. The ACE2/Ang (1-7)/Mas axis of the RAS also opposes the unfavourable actions of ACE/Ang II/ATR1 axis. Inhibition of RAS could reduce inflammation-associated processes in vasculature, indepen- dently of lowering BP. RAS is significantly modulated by the genes coding for this system. Certain genetic variants (SNPs) in the RAS genes have been denoted as the functional ones and have been associated with hypertension, cardiovascular phenotypes and atherosclerosis. Also, the genetic components of the RAS interfere with the regula- tors of gene expression by microRNAs (miRs).
Differential Geometry of Special Mappings

The monograph deals with the theory of conformal, geodesic, holomorphically projective, F-planar and others mappings and transformations of manifolds with affine connection, Riemannian, Kahler and Riemann-Finsler manifolds. Concretely, the monograph treats the following: basic concepts of topological spaces, the theory of manifolds with affine connection (particularly, the problem of semigeodesic coordinates), Riemannian and Kahler manifolds (reconstruction of a metric, equidistant spaces, variational problems in Riemannian spaces, SO(3)-structure as a model of statistical manifolds, decomposition of tensors), the theory of differentiable mappings and transformations of manifolds (the problem of metrization of affine connection, harmonic diffeomorphisms), conformal mappings and transformations (especially conformal mappings onto Einstein spaces, conformal transformations of Riemannian manifolds), geodesic mappings (GM; especially geodesic equivalence of a manifold with affine connection to an equiaffine manifold), GM onto Riemannian manifolds, GM between Riemannian manifolds (GM of equidistant spaces, GM of Vn(B) spaces, its field of symmetric linear endomorphisms), GM of special spaces, particularly Einstein, Kahler, pseudosymmetric manifolds and their generalizations, global geodesic mappings and deformations, GM between Riemannian manifolds of different dimensions, global GM, geodesic deformations of hypersurfaces in Riemannian spaces, some applications of GM to general relativity, namely three invariant classes of the Einstein equations and geodesic mappings, F-planar mappings of spaces with affine connection, holomorphically projective mappings (HPM) of Kahler manifolds (fundamental equations of HPM, HPM of special Kahler manifolds, HPM of parabolic Kahler manifolds, almost geodesic mappings, which generalize geodesic mappings, Riemann-Finsler spaces and their geodesic mappings, geodesic mappings of Berwald spaces onto Riemannian spaces.
Flots transversalement affines et tissus feuilletés

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Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization

Analyzing usability test videos is arduous. Although recent research showed the promise of AI in assisting with such tasks, it remains largely unknown how AI should be designed to facilitate effective collaboration between user experience (UX) evaluators and AI. Inspired by the concepts of agency and work context in human and AI collaboration literature, we studied two corresponding design factors for AI-assisted UX evaluation: explanations and synchronization. Explanations allow AI to further inform humans how it identifies UX problems from a usability test session; synchronization refers to the two ways humans and AI collaborate: synchronously and asynchronously. We iteratively designed a tool, AI Assistant, with four versions of UIs corresponding to the two levels of explanations (with/without) and synchronization (sync/async). By adopting a hybrid wizard-of-oz approach to simulating an AI with reasonable performance, we conducted a mixed-method study with 24 UX evaluators identifying UX problems from usability test videos using AI Assistant. Our quantitative and qualitative results show that AI with explanations, regardless of being presented synchronously or asynchronously, provided better support for UX evaluators' analysis and was perceived more positively; when without explanations, synchronous AI better improved UX evaluators' performance and engagement compared to the asynchronous AI. Lastly, we present the design implications for AI-assisted UX evaluation and facilitating more effective human-AI collaboration.
On perfect powers that are sums of cubes of a three term arithmetic progression

Using only elementary arguments, Cassels and Uchiyama (independently) determined all squares that are sums of three consecutive cubes. Zhongfeng Zhang extended this result and determined all perfect powers that are sums of three consecutive cubes. Recently, the equation $(x-r)^k + x^k + (x+r)^k$ has been studied for $k=4$ by Zhongfeng Zhang and for $k=2$ by Koutsianas. In this paper, we complement the work of Cassels, Koutsianas and Zhang by considering the case when $k=3$ and showing that the equation $(x-r)^3+x^3+(x+r)^3=y^n$ with $n\geq 5$ a prime and $0 < r \leq 10^6$ only has trivial solutions $(x,y,n)$ which satisfy $xy=0$.
Overview of the Ugglan Entity Discovery and Linking System

Ugglan is a system designed to discover named entities and link them to unique identifiers in a knowledge base. It is based on a combination of a name and nominal dictionary derived from Wikipedia and Wikidata, a named entity recognition module (NER) using fixed ordinallyforgetting encoding (FOFE) trained on the TAC EDL data from 2014-2016, a candidate generation module from the Wikipedia link graph across multiple editions, a PageRank link and cooccurrence graph disambiguator, and finally a reranker trained on the TAC EDL 2015-2016 data. In our first participation in the TAC trilingual entity discovery and linking task, we obtained a strong typed mention match of 0.701 (Ugglan2), a strong typed all match of 0.592 (Ugglan4), and typed mention ceaf of 0.595 (Ugglan1).
Illumination 1 ) Sample design space 2 ) Construct model 3 ) Maximize acquisition function 4 ) Sample acquisition map 5 ) Maximize performance estimation

Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a 2-dimensional airfoil optimization problem SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic 3-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.
CoT: Cooperative Training for Generative Modeling of Discrete Data

In this paper, we study the generative models of sequential discrete data. To tackle the exposure bias problem inherent in maximum likelihood estimation (MLE), generative adversarial networks (GANs) are introduced to penalize the unrealistic generated samples. To exploit the supervision signal from the discriminator, most previous models leverage REINFORCE to address the non-differentiable problem of sequential discrete data. However, because of the unstable property of the training signal during the dynamic process of adversarial training, the effectiveness of REINFORCE, in this case, is hardly guaranteed. To deal with such a problem, we propose a novel approach called Cooperative Training (CoT) to improve the training of sequence generative models. CoT transforms the min-max game of GANs into a joint maximization framework and manages to explicitly estimate and optimize Jensen-Shannon divergence. Moreover, CoT works without the necessity of pre-training via MLE, which is crucial to the success of previous methods. In the experiments, compared to existing state-of-the-art methods, CoT shows superior or at least competitive performance on sample quality, diversity, as well as training stability.
Topology in Fundamental Physics

In this thesis I present a mathematical tool for understanding the spin networks that arise from the study of the loop states of quantum gravity. The spin networks that arise in quantum gravity possess more information than the original spin networks of Penrose: they are embedded within a manifold and thus possess topological information. There are limited tools available for the study of this information. To remedy this I introduce a slightly modified mathematical object Braided Ribbon Networks and demonstrate that they can be related to spin networks in a consistent manner which preserves the diffeomorphism invariant character of the loop states of quantum gravity. Given a consistent definition of Braided Ribbon Networks I then relate them back to previous trinion based versions of Braided Ribbon Networks. Next, I introduce a consistent evolution for these networks based upon the duality of these networks to simplicial complexes. From here I demonstrate that there exists an invariant of this evolution and smooth deformations of the networks, which captures some of the topological information of the networks. The principle result of this program is presented next: that the invariants of the Braided Ribbon Networks can be transferred over to the original spin network states of loop quantum gravity. From here we represent other advances in the study of braided ribbon networks, accompanied by comments of their context given the consistent framework developed earlier including: the meaning of isolatable substructures, the particular structure of the capped three braids in trivalent braided ribbon networks and their application towards emergent particle physics, and the implications of the existence of microlocal topological structures in spin networks. Lastly we describe the current state of research in braided ribbon networks, the implications of this study on quantum gravity as a whole and future directions of research in the area.
Im 2 Flow : Motion Hallucination from Static Images for Action Recognition ( Supplementary Materials )

Our Im2Flow network architecture is adapted from those in [3, 5] with some modifications. Let Ck denote a Convolution-BatchNorm-ReLU layer with k filters. CDk denotes a Convolution-BatchNorm-Dropout-ReLU layer with a dropout rate of 50%. The encoder uses dilatedconvolutions [7], and the decoder uses up-convolutions. All dilated-convolutions and up-convolutions use 4 x 4 spatial filters applied with stride 2. After the last layer in the decoder, an up-convolution is followed by a Tanh layer to produce the flow image. BatchNorm is not applied to the first layer in the encoder. The encoder uses leaky ReLUs with a slope of 0.2, while ReLUs in the decoder are not leaky. Skip connections are added between each layer i in the encoder and layer n− i in the decoder, where n is the total number of layers. The skip connections concatenate activations from layer i to layer n− i. Encoder: C64-C128-C256-C512-C512-C512-C512-C512 Decoder: CD512-CD1024-CD1024-C1024-C1024-C512-C256-C128 The motion content loss network is the first two residual blocks of ResNet18 [2], which produces an activation map of size 128×28×28. B. Details of the metrics we use for flow prediction evaluation
Faster response in bounded-update-rate, discrete-time linear networks using delayed self-reinforcement

ABSTRACT The response speed of a network impacts the efficacy of its actions to external stimuli. However, for a given bound on the update rate, the network-response speed is limited by the need to maintain stability. The main contributions of this work are (i) to use delayed self-reinforcement (DSR), where each agent uses its current and previously available information to strengthen its update, (ii) develop conditions for stability with the new update, and (iii) to show that such self-reinforcement can increase the network speed without the need to increase the individual agent's update rate. Example simulation results are presented that show more than an order of magnitude improvement in the response speed (quantified using the settling time) with the proposed DSR approach.
Typing of Lymphogranuloma Venereum Chlamydia trachomatis Strains

We analyzed by multilocus sequence typing 77 lymphogranuloma venereum Chlamydia trachomatis strains from men who have sex with men in Europe and the United States. Specimens from an outbreak in 2003 in Europe were monoclonal. In contrast, several strains were in the United States in the 1980s, including a variant from Europe.
the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation.

We propose T OPO O PT , a novel direct-connect fabric for deep neural network (DNN) training workloads. T OPO O PT co-optimizes the distributed training process across three dimensions: computation, communication, and network topology. We demonstrate the mutability of AllReduce trafﬁc, and leverage this property to construct efﬁcient network topologies for DNN training jobs. T OPO O PT then uses an alternating optimization technique and a group theory-inspired algorithm called TotientPerms to ﬁnd the best network topology and routing plan, together with a parallelization strategy. We build a fully functional 12-node direct-connect prototype with remote direct memory access (RDMA) forwarding at 100 Gbps. Large-scale simulations on real distributed training models show that, compared to similar-cost Fat-tree interconnects, T OPO O PT reduces DNN training time by up to 3.4 × .
Isolating Sources of Disentanglement in Variational Autoencoders

We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.
Solving String Constraints With Regex-Dependent Functions Through Transducers With Priorities And Variables (Technical Report)

TAOLUE CHEN, Birkbeck, University of London, United Kingdom MATTHEW HAGUE, Royal Holloway, University of London, United Kingdom ZHILEI HAN, Tsinghua University, China DENGHANG HU, Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, China ALEJANDRO FLORES-LAMAS, Royal Holloway, University of London, United Kingdom ANTHONY W. LIN, University of Kaiserslautern & Max-Planck Institute, Germany SHUANGLONG KAN, University of Kaiserslautern, Germany PHILIPP RÜMMER, Uppsala University, Sweden ZHILIN WU, Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, China
High-order Foldy-Wouthuysen transformations of the Dirac and Dirac-Pauli Hamiltonians in the weak-field limit

The low-energy and weak-field limit of Dirac equation can be obtained by an order-by-order block diagonalization approach to any desired order in the parameter $\pi/mc$ ($\pi$ is the kinetic momentum and $m$ is the mass of the particle). In the previous work, it has been shown that, up to the order of $(\pi/mc)^8$, the Dirac-Pauli Hamiltonian in the Foldy-Wouthuysen (FW) representation may be expressed as a closed form and consistent with the classical Hamiltonian, which is the sum of the classical relativistic Hamiltonian for orbital motion and the Thomas-Bargmann-Michel-Telegdi (T-BMT) Hamiltonian for spin precession. In order to investigate the exact validity of the correspondence between classical and Dirac-Pauli spinors, it is necessary to proceed to higher orders. In this paper, we investigate the FW representation of the Dirac and Dirac-Pauli Hamiltonians by using Kutzelnigg's diagonalization method. We show that the Kutzelnigg diagonalization method can be further simplified if nonlinear effects of static and homogeneous electromagnetic fields are neglected (in the weak-field limit). Up to the order of $(\pi/mc)^{14}$, we find that the FW transformation for both Dirac and Dirac-Pauli Hamiltonians is in agreement with the classical Hamiltonian with the gyromagnetic ratio given by $g=2$ and $g\neq2$ respectively. Furthermore, with higher-order terms at hand, it is demonstrated that the unitary FW transformation admits a closed form in the low-energy and weak-field limit.
h: A Plank for Higher-order Attribute Contraction Schemes

We present and formalize h, a core (or "plank") calculus that can serve as the foundation for several compiler specification languages, notably CRSX (Combinatory Reductions Systems with eXtensions), HACS (Higher-order Attribute Contraction Schemes), and TransScript. We discuss how the h typing and formation rules introduce the necessary restrictions to ensure that rewriting is well-defined, even in the presence of h's powerful extensions for manipulating free variables and environments as first class elements (including in pattern matching).
FOURIER-MUKAI TRANSFORMS OF SLOPE STABLE TORSION-FREE SHEAVES AND STABLE 1-DIMENSIONAL SHEAVES ON WEIERSTRASS ELLIPTIC THREEFOLDS

We focus on a class of Weierstraß elliptic threefolds that allows the base of the fibration to be a Fano surface or a numerically K-trivial surface. In the first half of this article, we define the notion of limit tilt stability, which is closely related to Bayer’s polynomial stability. We show that the Fourier-Mukai transform of a slope stable torsion-free sheaf satisfying a vanishing condition in codimension 2 (e.g. a reflexive sheaf) is a limit stable object. We also show that the inverse Fourier-Mukai transform of a limit tilt semistable object of nonzero fiber degree is a slope semistable torsion-free sheaf, up to modification in codimension 2. In the second half of this article, we define a limit stability for complexes that vanish on the generic fiber of the fibration. We show that one-dimensional stable sheaves with positive twisted ch3 correspond to such limit stable complexes under a Fourier-Mukai transform. When the elliptic fibration has a numerically K-trivial base, we show that these limit stable complexes are the stable objects with respect to a Bridgeland stability on a triangulated subcategory of the derived category of coherent sheaves on the threefold.