Miracle-dz/UniFAR-FTUs huggingface dataset:
SYMPOSIUM: RECENT ADVANCES IN FOOT AND ANKLE SURGERY

Ankle arthroplasty is increasingly used to treat advanced ankle arthritis. Earlier prostheses have given way to second-generation implants, on which we are accumulating medium-term data. The Scandinavian Total Ankle Replacement (STAR) is a three-component uncemented implant in wide use in Europe and the only mobile-bearing prosthesis with conditional approval in the United States. We retrospectively reviewed 45 patients (52 ankles) who had primary total ankle replacements using STAR prostheses, in order to assess survivorship and add to the pool of clinical data provided by independent practitioners required to establish this treatment as a viable alternative to arthrodesis. The minimum followup was 60 months (range, 60–110 months). Clinical outcome was determined using the AOFAS score. We determined the rate of radiographic loosening and recorded complications and the need for further surgery. Survival was 90% (95% CI 76.8 to 95.5) at 5 years and 84% (95% CI 68.9 to 92.2) at 8 years. Six of 52 ankles (11%) had component revision and two were converted to fusion. The mean postoperative AOFAS score was 78. The complication rate was 21%. Subsequent surgery, excluding component revision, was performed in nine of 52 (17%) ankles. Level of Evidence: Level IV, therapeutic study. See the Guidelines for Authors for a complete description of levels of evidence.
Tetrahedral Geometry from Areas

We solve a very classical problem motivated by considerations in quantum gravity: providing a description of the geometry of a Euclidean tetrahedron from the initial data of the areas of the faces and the areas of the medial parallelograms of Yetter or equivalently of the pseudofaces of McConnell. In particular, we derive expressions for the dihedral angles, face angles and (an) edge length, the remaining parts being derivable by symmetry or by identities in the classic 1902 compendium of results on tetrahedral geometry of Richardson. We also provide an alternative proof using (bi)vectors of the result of Yetter that four times the sum of the squared areas of the medial parallelograms is equal to the sum of the squared areas of the faces.
Application of LS and EKF techniques to the identification of underwater vehicles

The modelling and identification of an open-frame underwater vehicle for marine applications is considered. The goal of this work is to demonstrate that modelling and identification of small underwater vehicles is feasible at low cost: the identification has been accomplished using only standard on-board devices. First, the selection of a model for such vehicles is discussed, as well as a suitable identification method. The parameters of the selected model are identified in two steps: based on least squares (LS) and extended Kalman filter (EKF) techniques. The results of the identification applied to experimental data are presented and discussed.
What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow

Modern software systems are increasingly including machine learning (ML) as an integral component. However, we do not yet understand the difficulties faced by software developers when learning about ML libraries and using them within their systems. To that end, this work reports on a detailed (manual) examination of 3,243 highly-rated Q&A posts related to ten ML libraries, namely Tensorflow, Keras, scikit-learn, Weka, Caffe, Theano, MLlib, Torch, Mahout, and H2O, on Stack Overflow, a popular online technical Q&A forum. We classify these questions into seven typical stages of an ML pipeline to understand the correlation between the library and the stage. Then we study the questions and perform statistical analysis to explore the answer to four research objectives (finding the most difficult stage, understanding the nature of problems, nature of libraries and studying whether the difficulties stayed consistent over time). Our findings reveal the urgent need for software engineering (SE) research in this area. Both static and dynamic analyses are mostly absent and badly needed to help developers find errors earlier. While there has been some early research on debugging, much more work is needed. API misuses are prevalent and API design improvements are sorely needed. Last and somewhat surprisingly, a tug of war between providing higher levels of abstractions and the need to understand the behavior of the trained model is prevalent.
Automated characterization of noise distributions in diffusion MRI data

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coefficients), which is usually not available. We introduce two new automated methods using moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer values of N is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.
Bidirectional Type Class Instances (Extended Version)

GADTs were introduced in Haskell's eco-system more than a decade ago, but their interaction with several mainstream features such as type classes and functional dependencies has a lot of room for improvement. More specifically, for some GADTs it can be surprisingly difficult to provide an instance for even the simplest of type classes. In this paper we identify the source of this shortcoming and address it by introducing a conservative extension to Haskell's type classes: Bidirectional Type Class Instances. In essence, under our interpretation class instances correspond to logical bi-implications, in contrast to their traditional unidirectional interpretation. We present a fully-fledged design of bidirectional instances, covering the specification of typing and elaboration into System FC, as well as an algorithm for type inference and elaboration. We provide a proof-of-concept implementation of our algorithm, and revisit the meta-theory of type classes in the presence of our extension.
Linear orderings of combinatorial cubes

We show that, for every linear ordering of $[2]^n$, there is a large subcube on which the ordering is lexicographic. We use this to deduce that every long sequence contains a long monotone subsequence supported on an affine cube. More generally, we prove an analogous result for linear orderings of $[k]^n$. We show that, for every such ordering, there is a large subcube on which the ordering agrees with one of approximately $\frac{(k-1)! }{2(\ln 2)^k}$ orderings.
Privacy-Preserving Distributed Learning with Secret Gradient Descent

In many important application domains of machine learning, data is a privacy-sensitive resource. In addition, due to the growing complexity of the models, single actors typically do not have sufficient data to train a model on their own. Motivated by these challenges, we propose Secret Gradient Descent (SecGD), a method for training machine learning models on data that is spread over different clients while preserving the privacy of the training data. We achieve this by letting each client add temporary noise to the information they send to the server during the training process. They also share this noise in separate messages with the server, which can then subtract it from the previously received values. By routing all data through an anonymization network such as Tor, we prevent the server from knowing which messages originate from the same client, which in turn allows us to show that breaking a client's privacy is computationally intractable as it would require solving a hard instance of the subset sum problem. This setup allows SecGD to work in the presence of only two honest clients and a malicious server, and without the need for peer-to-peer connections.
A two-path bandpass ΣΔ modulator for digital IF extraction at 20 MHz

Abstract — Oversampled bandpass A/D converters based on sigma-delta (Σ∆) modulation can be used to robustly digitize the types of narrowband intermediate frequency (IF) signals that arise in radios and cellular systems. This paper proposes a two-path architecture for a fourth-order, bandpass modulator that is more tolerant of analog circuit limitations at high sampling speeds than conventional implementations based on the use of switched-capacitor biquadratic filters. An experimental prototype employing the two-path topology has been integrated in a 0.6-μm, single-poly, triple-metal CMOS technology with capacitors synthesized from a stacked metal structure. Two interleaved paths clocked at 40 MHz digitize a 200 kHz bandwidth signal centered at 20 MHz with 75 dB of dynamic range while suppressing the undesired mirror image signal by 42 dB. At low input signal levels, the mixing of spurious tones at dc and fs/2 with the input appears to degrade the performance of the modulator; out-of-band sinusoidal dither is shown to be an effective means of avoiding this degradation. The experimental modulator dissipates 72 mW from a 3.3-V supply.
On the functional graph generated by a plane curve and its quadratic twist

Given a plane curve defined by $Y^2 = f(X)$ over a finite field $\mathbb{F}_q$ of odd characteristic and a non-square element $\lambda$ in $\mathbb{F}_q$, we define a functional graph by choosing the elements in $\mathbb{F}_q$ as vertices and drawing an edge from $x$ to $y$ if and only if $(x,y)$ is either a point on $Y^2 = f(X)$ or a point on $\lambda Y^2 = f(X)$. We show that if $f$ is a permutation polynomial over $\mathbb{F}_q$, then every connected component of the graph has a Hamiltonian cycle. Moreover, these Hamiltonian cycles can be used to construct balancing binary sequences. By making computations for permutation polynomials $f$ of low degree, it turns out that almost all these graphs are strongly connected, and there are many Hamiltonian cycles in such a graph if it is connected.
High‐dimensional principal component analysis with heterogeneous missingness

We study the problem of high‐dimensional Principal Component Analysis (PCA) with missing observations. In a simple, homogeneous observation model, we show that an existing observed‐proportion weighted (OPW) estimator of the leading principal components can (nearly) attain the minimax optimal rate of convergence, which exhibits an interesting phase transition. However, deeper investigation reveals that, particularly in more realistic settings where the observation probabilities are heterogeneous, the empirical performance of the OPW estimator can be unsatisfactory; moreover, in the noiseless case, it fails to provide exact recovery of the principal components. Our main contribution, then, is to introduce a new method, which we call primePCA, that is designed to cope with situations where observations may be missing in a heterogeneous manner. Starting from the OPW estimator, primePCA iteratively projects the observed entries of the data matrix onto the column space of our current estimate to impute the missing entries, and then updates our estimate by computing the leading right singular space of the imputed data matrix. We prove that the error of primePCA converges to zero at a geometric rate in the noiseless case, and when the signal strength is not too small. An important feature of our theoretical guarantees is that they depend on average, as opposed to worst‐case, properties of the missingness mechanism. Our numerical studies on both simulated and real data reveal that primePCA exhibits very encouraging performance across a wide range of scenarios, including settings where the data are not Missing Completely At Random.
Chirped temporal solitons in driven optical resonators

Temporal solitons in driven microresonator, fiber-resonator, and bulk enhancement cavities enable attractive optical sources for spectroscopy, communications, and metrology. Here we present theoretical and experimental observations of a new class of temporal optical soliton characterized by pulses with large and positive chirp in normal dispersion resonators with strong spectral filtering. Numerical simulations reveal stable waveforms over a wide new range of parameters including highly chirped pulses at large drive powers. Chirped temporal solitons matching predictions are observed in experiments with normal dispersion fiber resonators strongly driven with nanosecond pulses. Scaling laws are developed and provide simple design guidelines for generating chirped temporal solitons in bulk- and micro-resonator, in addition to fiber-resonator platforms. The relationship between the chirped solutions and other stable waveforms in normal and anomalous dispersion resonators is examined. Chirped temporal solitons represent a promising new resource for frequency-comb and ultrashort-pulse generation.
A Utility-Preserving GAN for Face Obscuration

From TV news to Google StreetView, face obscuration has been used for privacy protection. Due to recent advances in the field of deep learning, obscuration methods such as Gaussian blurring and pixelation are not guaranteed to conceal identity. In this paper, we propose a utility-preserving generative model, UP-GAN, that is able to provide an effective face obscuration, while preserving facial utility. By utility-preserving we mean preserving facial features that do not reveal identity, such as age, gender, skin tone, pose, and expression. We show that the proposed method achieves the best performance in terms of obscuration and utility preservation.
Teaching DNNs to design fast fashion

$ $"Fast Fashion" spearheads the biggest disruption in fashion that enabled to engineer resilient supply chains to quickly respond to changing fashion trends. The conventional design process in commercial manufacturing is often fed through "trends" or prevailing modes of dressing around the world that indicate sudden interest in a new form of expression, cyclic patterns, and popular modes of expression for a given time frame. In this work, we propose a fully automated system to explore, detect, and finally synthesize trends in fashion into design elements by designing representative prototypes of apparel given time series signals generated from social media feeds. Our system is envisioned to be the first step in design of Fast Fashion where the production cycle for clothes from design inception to manufacturing is meant to be rapid and responsive to current "trends". It also works to reduce wastage in fashion production by taking in customer feedback on sellability at the time of design generation. We also provide an interface wherein the designers can play with multiple trending styles in fashion and visualize designs as interpolations of elements of these styles. We aim to aid the creative process through generating interesting and inspiring combinations for a designer to mull by running them through her key customers.
Adversarial Representation Learning on Large-Scale Bipartite Graphs

Graph representation on large-scale bipartite graphs is central for a variety of applications, ranging from social network analysis to recommendation system development. Existing methods exhibit two key drawbacks: 1. unable to characterize the inconsistency of the node features within the bipartite-specific structure; 2. unfriendly to support large-scale bipartite graphs. To this end, we propose ABCGraph, a scalable model for unsupervised learning on large-scale bipartite graphs. At its heart, ABCGraph utilizes the proposed Bipartite Graph Convolutional Network (BGCN) as the encoder and adversarial learning as the training loss to learn representations from nodes in two different domains and bipartite structures, in an unsupervised manner. Moreover, we devise a cascaded architecture to capture the multi-hop relationship in bipartite structure and improves the scalability as well. Extensive experiments on multiple datasets of varying scales verify the effectiveness of ABCGraph compared to state-of-the-arts. For the experiment on a real-world large-scale bipartite graph system, fast training speed and low memory cost demonstrate the scalability of ABCGraph model.
Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

Spiking neural networks are well-suited for spatiotemporal feature detection and learning, and naturally involve dynamic delay mechanisms in the synapses, dendrites, and axons. Dedicated delay neurons and axonal delay circuits have been considered when implementing such pattern recognition networks in dynamic neuromorphic processors. Inspired by an auditory feature detection circuit in crickets, featuring a delayed excitation by post-inhibitory rebound, we investigate disynaptic delay elements formed by inhibitory–excitatory pairs of dynamic synapses. We configured such disynaptic delay elements in the DYNAP-SE neuromorphic processor and characterized the distribution of delayed excitations resulting from device mismatch. Interestingly, we found that the disynaptic delay elements can be configured such that the timing and magnitude of the delayed excitation depend mainly on the efficacy of the inhibitory and excitatory synapses, respectively, and that a neuron with multiple delay elements can be tuned to respond selectively to a specific pattern. Furthermore, we present a network with one disynaptic delay element that mimics the auditory feature detection circuit of crickets, and we demonstrate how varying synaptic weights, input noise and processor temperature affect the circuit. Dynamic delay elements of this kind open up for synapse level temporal feature tuning with configurable delays of up to 100 ms.
On spherical unitary representations of groups of spheromorphisms of Bruhat–Tits trees

Consider an infinite homogeneous tree $T_n$ of valence $n+1$, its group $Aut(T_n)$ of automorphisms, and the group $Hie(T_n)$ of its spheromorphisms (hierarchomorphisms), i. ~e. , the group of homeomorphisms of the boundary of $T_n$ that locally coincide with transformations defined by automorphisms. We show that the subgroup $Aut(T_n)$ is spherical in $Hie(T_n)$, i. ~e. , any irreducible unitary representation of $Hie(T_n)$ contains at most one $Aut(T_n)$-fixed vector. We present a combinatorial description of the space of double cosets of $Hie(T_n)$ with respect to $Aut(T_n)$ and construct a 'new' family of spherical representations of $Hie(T_n)$. We also show that the Thompson group has $PSL(2,\mathbb{Z})$-spherical unitary
Object Classification and Detection in High Dimensional Feature Space

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object positions and the appearance variations of object classes. This thesis improves upon several classical machine learning algorithms, enabling large computational gains in high dimensional feature space. A common trend in machine learning and computer vision research is to go large scale. In particular, the advent of huge datasets mined from the Internet, and the combination of multiple feature sources have considerably broadened the applications of computer vision. Tasks which were thought impossible a few years ago, such as human action recognition or pose estimation, automatic outdoor navigation, etc., now seem within reach. This dissertation is divided into two parts. The first one deals with the efficient training of a classifier or detector based on a large number of feature extractors, outside the control of the learning algorithm, and therefore of unknown suitability to the task at hand. More precisely, this part presents two kinds of strategies to accelerate the training of Boosting algorithms in such a context: (a) a method to better deal with the increasingly common case where features come from multiple sources (e.g. color, shape, texture, etc., in the case of images) and therefore can be partitioned into meaningful subsets; (b) new algorithms which balance at every Boosting iteration the number of weak learners and the number of training examples to look at in order to maximize the expected loss reduction. Experiments in image classification and object recognition on four standard computer vision datasets show that the adaptive techniques we propose outperform both basic sampling and state-of-the-art bandit methods. The second part deals with linear object detectors, currently the most popular class of detection systems, encompassing template matching, deformable part models, poselets, convolutional neural networks (which internally use linear filters), etc. The main bottleneck of many of those systems is the computational cost of the convolutions between the multiple rescalings of the image to process and the linear filters. We make use of properties of the Fourier transform and clever implementation strategies to obtain a speedup factor proportional to the filter size, both while training and at test time. We also introduce a few modifications to the original Deformable Part Model (DPM) of Felzenszwalb et al. improving its detection accuracy. The gains in performance are demonstrated on the well-known Pascal VOC benchmark, where an increase by one order of magnitude in the speed of said convolutions, and an average improvement of 15% in the accuracy of the detector are established.
Backward Euler-Maruyama method for a class of stochastic Markovian jump neural networks

Stability analysis of various neural networks have been successfully applied in many fields such as parallel computing and pattern recognition. This paper is concerned with a class of stochastic Markovian jump neural networks. The general mean-square stability of Backward Euler-Maruyama method for stochastic Markovian jump neural networks is discussed. The sufficient conditions to guarantee the general mean-square stability of Backward Euler-Maruyama method are given.