Miracle-dz/UniFAR-FTUs huggingface dataset:
Controversies in shoulder arthroplasty

Areas of current controversy concerning shoulder arthroplasty include management of glenoid bone loss, the role of patientspecific instrumentation, differing design features of reverse shoulder arthroplasty, and options for humeral fixation in primary and revision procedures. At the 2015 meeting of the American Academy of Orthopaedic Surgeons, shoulder arthroplasty specialists presented a symposium on these controversial topics. In each area, surgeons presented their case for a given position on these issues. This article is a summary of those proceedings.
Sentence Analogies: Exploring Linguistic Relationships and Regularities in Sentence Embeddings

While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities. We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences. Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings. We find that different models differ substantially in their ability to reflect such regularities.
Knowledge distillation via adaptive instance normalization

This paper addresses the problem of model compression via knowledge distillation. To this end, we propose a new knowledge distillation method based on transferring feature statistics, specifically the channel-wise mean and variance, from the teacher to the student. Our method goes beyond the standard way of enforcing the mean and variance of the student to be similar to those of the teacher through an $L_2$ loss, which we found it to be of limited effectiveness. Specifically, we propose a new loss based on adaptive instance normalization to effectively transfer the feature statistics. The main idea is to transfer the learned statistics back to the teacher via adaptive instance normalization (conditioned on the student) and let the teacher network "evaluate" via a loss whether the statistics learned by the student are reliably transferred. We show that our distillation method outperforms other state-of-the-art distillation methods over a large set of experimental settings including different (a) network architectures, (b) teacher-student capacities, (c) datasets, and (d) domains.
Invariants for tame parametrised chain complexes

We set the foundations for a new approach to Topological Data Analysis (TDA) based on homotopical methods at chain complexes level. We present the category of tame parametrised chain complexes as a comprehensive environment that includes several cases that usually TDA handles separately, such as persistence modules, zigzag modules, and commutative ladders. We extract new invariants in this category using a model structure and various minimal cofibrant approximations. Such approximations and their invariants retain some of the topological, and not just homological, aspects of the objects they approximate.
Stochastic Recursive Momentum for Policy Gradient Methods

In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced policy gradient in an exponential moving average fashion. STORM-PG enjoys a provably sharp $O(1/\epsilon^3)$ sample complexity bound for STORM-PG, matching the best-known convergence rate for policy gradient algorithm. In the mean time, STORM-PG avoids the alternations between large batches and small batches which persists in comparable variance-reduced policy gradient methods, allowing considerably simpler parameter tuning. Numerical experiments depicts the superiority of our algorithm over comparative policy gradient algorithms.
On the Robustness of Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward. We analyze the robustness of c-MARL to adversaries capable of attacking one of the agents on a team. Through the ability to manipulate this agent's observations, the adversary seeks to decrease the total team reward. Attacking c-MARL is challenging for three reasons: first, it is difficult to estimate team rewards or how they are impacted by an agent mispredicting; second, models are non-differentiable; and third, the feature space is low-dimensional. Thus, we introduce a novel attack. The attacker first trains a policy network with reinforcement learning to find a wrong action it should encourage the victim agent to take. Then, the adversary uses targeted adversarial examples to force the victim to take this action. Our results on the StartCraft II multi-agent benchmark demonstrate that c-MARL teams are highly vulnerable to perturbations applied to one of their agent's observations. By attacking a single agent, our attack method has highly negative impact on the overall team reward, reducing it from 20 to 9.4. This results in the team's winning rate to go down from 98.9% to 0%.
Metric Learning for Ordered Labeled Trees with pq-grams

Computing the similarity between two data points plays a vital role in many machine learning algorithms. Metric learning has the aim of learning a good metric automatically from data. Most existing studies on metric learning for tree-structured data have adopted the approach of learning the tree edit distance. However, the edit distance is not amenable for big data analysis because it incurs high computation cost. In this paper, we propose a new metric learning approach for tree-structured data with pq-grams. The pq-gram distance is a distance for ordered labeled trees, and has much lower computation cost than the tree edit distance. In order to perform metric learning based on pq-grams, we propose a new differentiable parameterized distance, weighted pq-gram distance. We also propose a way to learn the proposed distance based on Large Margin Nearest Neighbors (LMNN), which is a well-studied and practical metric learning scheme. We formulate the metric learning problem as an optimization problem and use the gradient descent technique to perform metric learning. We empirically show that the proposed approach not only achieves competitive results with the state-of-the-art edit distance-based methods in various classification problems, but also solves the classification problems much more rapidly than the edit distance-based methods.
Algorithms to enumerate superspecial Howe curves of genus 4

A Howe curve is a curve of genus $4$ obtained as the fiber product over $\mathbf{P}^1$ of two elliptic curves. Any Howe curve is canonical. This paper provides an efficient algorithm to find superspecial Howe curves and that to enumerate their isomorphism classes. We discuss not only an algorithm to test the superspeciality but also an algorithm to test isomorphisms for Howe curves. Our algorithms are much more efficient than conventional ones proposed by the authors so far for general canonical curves. We show the existence of a superspecial Howe curve in characteristic $7<p\le 331$ and enumerate the isomorphism classes of superspecial Howe curves in characteristic $p\le 53$, by executing our algorithms over the computer algebra system Magma.
Multitask Learning-Based Neural Bridging Reference Resolution

We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p.
The prime geodesic theorem for PSL2(ℤ[i]) and
spectral exponential sums

We shall ponder the Prime Geodesic Theorem for the Picard manifold $\mathcal{M} = \mathrm{PSL}_{2}(\mathbb{Z}[i]) \backslash \mathfrak{h}^{3}$, which asks about the asymptotic behaviour of a counting function for the closed geodesics on $\mathcal{M}$. Let $E_{\Gamma}(X)$ be the error term arising from counting prime geodesics, we then prove the bound $E_{\Gamma}(X) \ll X^{3/2+\epsilon}$ on average, as well as various versions of pointwise bounds. The second moment bound is the pure counterpart of work of Balog et al. for $\Gamma = \mathrm{PSL}_{2}(\mathbb{Z})$, and the main innovation entails the delicate analysis of sums of Kloosterman sums with an explicit evaluation of oscillatory integrals. Our pointwise bounds concern Weyl-type subconvex bounds for quadratic Dirichlet $L$-functions over $\mathbb{Q}(i)$. Interestingly, we are also able to establish an asymptotic law for the spectral exponential sum in the spectral aspect for a cofinite Kleinian group $\Gamma$. Finally, we produce numerical experiments of its behaviour, visualising that $E_{\Gamma}(X)$ obeys a conjectural bound of the size $O(X^{1+\epsilon})$.
Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space

We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores the state-cost space and has been shown to efficiently obtain high-quality solutions in practice without relying on the availability of a computationally-intensive two-point boundary-value solver. Our main contribution is an optimality proof for the single-tree version of the algorithm—a variant that was not analyzed before. Our proof only requires a mild and easily-verifiable set of assumptions on the problem and system: Lipschitz-continuity of the cost function and the dynamics. In particular, we prove that for any system satisfying these assumptions, any trajectory having a piecewise-constant control function and positive clearance from the obstacles can be approximated arbitrarily well by a trajectory found by AORRT. We also discuss practical aspects of AORRT and present experimental comparisons of variants of the algorithm.
A local self-tuning mechanism for the cosmological constant

Recently the global variation of the Planck mass in the General Relativistic Einstein-Hilbert action was proposed as a self-tuning mechanism of the cosmological constant preventing vacuum energy from freely gravitating. We show that this global mechanism emerges for generic local scalar-tensor theories with additional coupling of the scalar field to the field strength of a three-form gauge field that turns the scalar field constant on the domain of the action. Evaluation of the resulting integral constraint equation over the observable Universe yields a self-consistent framework with General Relativistic field equations and arbitrary radiatively stable residual cosmological constant. We argue that the expectation value for this residual is in good agreement with the magnitude of the observed cosmic acceleration.
Spectroscopy of Broad Absorption Line Quasars at 3 ≲ Z ≲ 5. I. Evidence for Quasar Winds Shaping Broad/Narrow Emission Line Regions

We present an observational study of 22 broad absorption line quasars (BAL QSOs) at based on optical/near-IR spectroscopy, aiming to investigate quasar winds and their effects. The near-IR spectroscopy covers the Hβ and/or Mg ii broad emission lines (BELs) for these quasars, allowing us to estimate their central black hole (BH) masses in a robust way. We found that our BAL QSOs, on average, do not have a higher Eddington ratio than that from non-BAL QSOs matched in redshift and/or luminosity. In a subset consisting of seven strong BAL QSOs possessing subrelativistic BAL outflows, we see the prevalence of large C iv BEL blueshift (∼3100 km s−1) and weak [O iii] emission (particularly the narrow [O iii] λ5007 component), indicative of nuclear outflows affecting the narrow emission line (NEL) regions. In another subset consisting of 13 BAL QSOs having simultaneous observations of Mg ii and Hβ, we found a strong correlation between 3000 and 5000 Å monochromatic luminosity, consistent with that from non-BAL QSOs matched in redshift and luminosity; however, there is no correlation between Mg ii and Hβ in FWHM, likely due to nuclear outflows influencing the BEL regions. Our spectroscopic investigations offer strong evidence that the presence of nuclear outflows plays an important role in shaping the BEL/NEL regions of these quasars and, possibly, regulating the growth of central supermassive BHs. We propose that BEL blueshift and BALs could be different manifestations of the same outflow system viewed at different sight lines and/or phases.
Multi-UAVs Target Tracking in Urban Environment Based on Distributed Model Predictive Control and Levy Flight-Salp Swarm Algorithm

To address the 3D target tracking problem in urban environment, a multi-unmanned aerial vehicles (UAVs) coordinated framework based on distributed model predictive control (DMPC) and a novel Levy flight-Salp Swarm Algorithm (LSSA) is proposed in this paper. First, the related models for target tracking are established, including the UAV kinematic model, urban obstacle environment, and their corresponding constraints. Second, the DMPC framework for multi-UAVs target tracking is proposed. Third, a recently proposed intelligent searching algorithm: SSA, is selected as the solver of the DMPC problem. To further improve the global searching performance of SSA, the Levy flight strategy is introduced to SSA, and the novel modified SSA is LSSA. Finally, a series of simulations are carried out to show the effectiveness of our proposed framework.
Effect of Finite Terms on the Truncation Error of Addition Theorems for Spherical Vector Wave Function

A finite sum adopted for the translational Addition Theorem (AT) of the spherical vector wave functions is a key-point for addressing a well known concept regarding the classical electromagnetic scattering of an incoming wave across a generic dispositions of spheres. The AT is essential for the translation of the vector spherical wave function expressed with respect to a coordinates system toward a different reference. According to its general formulation, the dependence of the translation coefficients on the relative direction of displacement linking two distinct references is the core study to be addressed. The inherent analytical aspects have been intensively studied during the last decades, but the respective numerical encoding has been limited to few basic configurations which provided a partial validity of the theory with quite approximate solutions. In the literature there are many authors reporting different sets of the vector translation coefficients, among which we mention those calculated by Stein, Cruzan and Mackowski as the most prominent of them. We have selected the Cruzan formulation of the vector translation coefficients for its structure based on the Wigner 3-j function. We have developed the criteria of truncating the inherent infinite series to achieve convergence with a finite version of the same, which leads to an extremely negligible error. During our numerical tests, we have deeply investigated generic truncation errors and outlined a repeatable procedure to get an acceptable convergence.
Experimental Study on the Performance Change of YBCO Tapes Under Repeated Overcurrent

Superconducting cables or fault current limiters may encounter short-circuit currents during operation. When experiencing multiple overcurrent cycles, the critical current degradation of yttrium barium copper oxide (YBCO) tapes may occur. The performance degradation of YBCO tapes under overcurrent is a gradual process. It may take dozens or hundreds of overcurrent or overheating pulses. To get a better understanding of this process, we explored the performance change of YBCO tapes under repeated overcurrent. A life characteristics test platform was established. The electromagnetic performance degradation of YBCO tape under 50 Hz sinusoidal ac operating conditions was investigated, taking the effect of the overcurrent amplitude and duration into account. Meanwhile, the relationship between the number of overcurrent cycles and the <inline-formula><tex-math notation="LaTeX">$I_{c}$</tex-math></inline-formula> degradation is proposed. The reason for this performance change is analyzed by comparing the 2-D current density distribution of the YBCO film before and after experiencing overcurrent. In addition, the <italic>U–I</italic> property and resistance variations of these tapes are presented. It is inferred that the cracking of the superconducting layer is the main cause of <inline-formula><tex-math notation="LaTeX">$I_{c}$</tex-math></inline-formula> degradation. The experimental results provide meaningful references for the performance degradation of the YBCO tapes and related devices.
Adsorption Behavior of γ-Al2O3 Toward Heptafluoroisobutyronitrile and its Decompositions: Theoretical and Experimental Insights

Heptafluoroisobutyronitrile (C<sub>3</sub>F<sub>7</sub>CN) has received extensive attention for replacing SF<sub>6</sub> in high voltage electrical equipment. It is necessary to assess the suitability of traditional common adsorbent activated alumina (<inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-Al<sub>2</sub>O<sub>3</sub>) used in C<sub>3</sub>F<sub>7</sub>CN based equipment as adsorbent. In this paper, C<sub>4</sub>F<sub>7</sub> N/N<sub>2</sub> hybrid gas discharge test was carried out on the gas insulation performance test platform, and the decomposition products mainly included CF<sub>4</sub>, C<sub>2</sub>F<sub>6</sub>, C<sub>3</sub>F<sub>8</sub>, CF<sub>3</sub>CN, C<sub>2</sub>F<sub>4</sub>, C<sub>3</sub>F<sub>6</sub> and C<sub>2</sub>F<sub>5</sub>CN. Secondly, gas samples were collected at different periods, and the gas before and after being adsorbed by <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-Al<sub>2</sub>O<sub>3</sub> was analyzed and compared by GC-MS. Finally, based on the density functional theory, the adsorption of gas molecular on the surface of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-Al<sub>2</sub>O<sub>3</sub> was studied. The adsorption effect of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-Al<sub>2</sub>O<sub>3</sub> to C<sub>3</sub>F<sub>7</sub>CN, C<sub>2</sub>F<sub>5</sub>CN and CF<sub>3</sub>CN is very strong, so it is difficult to be used as adsorbing material for C<sub>3</sub>F<sub>7</sub>CN type gas insulated equipment. However, due to the low cost of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-Al<sub>2</sub>O<sub>3</sub>, it can be used in waste gas treatment for new environment-friendly insulating gas.
Particle Swarm-Based Cell Range Expansion for Heterogeneous Mobile Networks

Traffic demand in wireless communication systems has emerged as a key issue over recent decades. An ever-increasing trend is projected for the next few years, with explosive data traffic expected to materialize and mobile users imposing new quality of service requirements. This growing traffic demand, combined with increasingly complex heterogeneous network (HetNet) scenarios, has presented ever more challenges for mobile network operators in terms of service, coverage, load balancing, and quality of service. Considering the traditional association mechanism based on maximum power received, HetNets tend to remain unbalanced, making it challenging to satisfy mobile users’ traffic requirements. In this paper, instead of trying to maximize the achievable downlink rate per user, we couple the cell range expansion (CRE) technique with a particle swarm optimization (PSO) algorithm to maximize the number of users whose downlink requirements are met. The proposed scheme considers both the loads of base stations and the signal-to-interference-plus-noise ratio (SINR) of user equipment to model an objective function that seeks to compute specific CRE bias values per small base station. The proposed scheme is also compared with some classical PSO implementations. Numerical results validate the performance of the proposed schemes, which effectively fulfill users’ data traffic requirements by reducing network imbalance.
Immunization Status of Rural Children in Vadodara District (Gujarat)

Introduction: Immunization is very important and cost effective measure in preventing many infectious diseases in children. Complete immunization coverage is still less than 60% in India. Incomplete or no immunization renders the child prone to various infections and undernutrition as well. In this study we aimed to know the immunization status of rural children of Vadodara district of Gujarat. Material methods: this was a cross sectional study conducted from December 2010 till November 2011 in different Anganwadi centers of rural areas in Vadodara. Children’s immunization and nutritional status were evaluated and statistically analysed. Results: total 250 children were included and 91% of them were fully immunized as per national immunization program till age. Conclusion : Although complete immunization rate is very good, some children are still not immunized at all. These unimmunized children are more at risk for severe undernutrition.
Calculation of Electric Potentials Generated by a Dipole Source in an Elliptical Conducting Cylinder of a Finite Length

In this paper we present an analytical method for computation of electric potential of the point current dipole, placed inside homogeneous conducting elliptical cylinder. We use elliptical cylinder as a model of human torso. Current dipole is a model for current sources in the heart. So we solve the forward electrocardiographic problem. We calculate electric potential maps on the body surface by two methods: Analytical and numerical. An analytical method is based on equations involving Mathieu functions. Calculation of such functions is performed by usage of Mathieu functions toolbox, developed by authors of the current work in the Scilab computer language. A numerical approach is based on the finite element method (FEM). After the comparison of the potential distributions obtained by two methods we can see that the results are very close for various sets of source and medium parameters. In this article, an analytical method has been implemented for calculating the electric potentials created by a current dipole in an elliptical conducting cylinder of finite length. The convergence of the used series is shown, as well as the convergence of the solution during the transformation of an elliptic cylinder into a circular cylinder. The presented materials illustrate one of the possible applications of the Mathieu functions.