Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ROLE OF ACUPUNCTURE IN ARTHRITIS OF THE KNEE JOINT IN ADDITION TO LOCAL STEROID INJECTION

Objective: This study was conducted to know the effects of acupuncture on patients with osteoarthritis (OA) of the knee joint and to identify the trigger points in these patients. Material and Methods: This study was conducted on OA of knee joint at Pain & Plegia center Peshawar from 1999 to 2004. Patients with multiple small joints pain, rheumatoid arthritis, gout or in whom knee joint was distended with fluid were excluded. All patients were treated with acupuncture for pain relief. Trigger spots were identified by palpating the knee. These trigger spots were treated by local instillation of triamcinolone acetonide 40 mg. These patients were then treated with Transcutaneous Electro-Neuro Stimulator (TENS) to treat muscle weakness. Results: Out of 250, 225 cases (90 %) were symptoms free. Only 25 cases (10%) relapsed with in a monthafter completion of treatment. Pain knee was mainly due to tendonitis, in close proximity to the knee joint. Main trigger points were outside the knee joint at adductor tubercle possibly at the insertion of adductor magnus and on the lateral side at the origin of gastrocnemius lateral head. The third trigger spot was on the medial aspect probably in tibial collateral ligament. Conclusion: Pain knee joint attributed to OA Knee is in fact due to tendonitis and not due to osteoarthritic changes in the joint and it is very much amenable to complete recovery by using both acupuncture therapy and local steroid injections.
Audio-Conditioned U-Net for Position Estimation in Full Sheet Images

The goal of score following is to track a musical performance, usually in the form of audio, in a corresponding score representation. Established methods mainly rely on computer-readable scores in the form of MIDI or MusicXML and achieve robust and reliable tracking results. Recently, multimodal deep learning methods have been used to follow along musical performances in raw sheet images. Among the current limits of these systems is that they require a non trivial amount of preprocessing steps that unravel the raw sheet image into a single long system of staves. The current work is an attempt at removing this particular limitation. We propose an architecture capable of estimating matching score positions directly within entire unprocessed sheet images. We argue that this is a necessary first step towards a fully integrated score following system that does not rely on any preprocessing steps such as optical music recognition.
Sensor-Movement-Robust Angle Estimation for 3-DoF Lower Limb Joints Without Calibration.

Inertial measurement unit (IMU)-based 3-DoF angle estimation methods for lower limb joints have been studied for decades, however the calibration motions and/or careful sensor placement are still necessary due to challenges of real-time application. This study proposes a novel sensormovement-robust 3-DoF method for lower-limb joint angle estimation without calibration. A realtime optimization process, which is based on a feedback iteration progress to identify three joint axes of a 3-DoF joint, has been presented with a reference frame calibration algorithm, and a safe-guarded strategy is proposed to detect and compensate for the errors caused by sensor movements. The experimental results obtained from a 3-DoF gimbal and ten healthy subjects demonstrate a promising performance on 3-DoF angle estimation. Specially, the experiments on ten subjects are performed with three gait modes and a 2-min level walking. The root mean square error is below 2 deg for level walking and 5 deg for other two gait modes. The result of 2-min level walking shows our algorithms stability under long run. The robustness against sensor movement are demonstrated through data from multiple sets of IMUs. In addition, results from the 3-DoF gimbal indicate that the accuracy of 3-DoF angle estimation could be improved by 84.9% with our reference frame calibration algorithm. In conclusion, our study proposes and validates a sensor-movement-robust 3-DoF angle estimation for lowerlimb joints based on IMU. To the best of our knowledge, our approach is the first experimental implementation of IMUbased 3-DoF angle estimation for lower-limb joints without calibration.
Tutorial on NLP-Inspired Network Embedding

This tutorial covers a few recent papers in the field of network embedding. Network embedding is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction. The papers discussed develop methods for the online learning of such embeddings, and include DeepWalk, LINE, node2vec, struc2vec and megapath2vec. These new methods and developments in online learning of network embeddings have major applications for the analysis of graphs and networks, including online social networks.
On the interplay between exposure misclassification and informative cluster size

A recent multigenerational study of diethylstilbestrol and attention deficit hyperactivity disorder exhibited signs of both informative cluster size—the outcome was more prevalent in small families—and exposure misclassification—self‐report of familial diethylstilbestrol exposure was substantially mismeasured. Motivated by this, we study the effect of exposure misclassification when cluster size is potentially informative and, in particular, when misclassification is differential by cluster size. We find that: misclassification in an exposure that is related to cluster size induces informativeness when cluster size would otherwise be non‐informative; and misclassification that is differential by informative cluster size may attenuate, inflate or possibly reverse the sign of estimates. To mitigate these issues, we propose an observed likelihood correction for joint models of cluster size and outcomes, and an expected estimating equations correction. We evaluate these approaches in simulations and in application to the motivating data from the second Nurses Health Study, NHS II.
Transfer Learning for Algorithm Recommendation

Meta-Learning is a subarea of Machine Learning that aims to take advantage of prior knowledge to learn faster and with fewer data. There are different scenarios where meta-learning can be applied, and one of the most common is algorithm recommendation, where previous experience on applying machine learning algorithms for several datasets can be used to learn which algorithm, from a set of options, would be more suitable for a new dataset. Perhaps the most popular form of meta-learning is transfer learning, which consists of transferring knowledge acquired by a machine learning algorithm in a previous learning task to increase its performance faster in another and similar task. Transfer Learning has been widely applied in a variety of complex tasks such as image classification, machine translation and speech recognition, achieving remarkable results. Although transfer learning is very used in traditional or base-learning, it is still unknown if it is useful in a meta-learning setup. For that purpose, in this paper, we investigate the effects of transferring knowledge in the meta-level instead of base-level. Thus, we train a neural network on meta-datasets related to algorithm recommendation, and then using transfer learning, we reuse the knowledge learned by the neural network in other similar datasets from the same domain, to verify how transferable is the acquired meta-knowledge.
On Adversarial Patches: Real-World Attack on ArcFace-100 Face Recognition System

Recent works showed the vulnerability of image classifiers to adversarial attacks in the digital domain. However, the majority of attacks involve adding small perturbation to an image to fool the classifier. Unfortunately, such procedures can not be used to conduct a real-world attack, where adding an adversarial attribute to the photo is a more practical approach. In this paper, we study the problem of real-world attacks on face recognition systems. We examine security of one of the best public face recognition systems, LResNet100E-IR with ArcFace loss, and propose a simple method to attack it in the physical world. The method suggests creating an adversarial patch that can be printed, added as a face attribute and photographed; the photo of a person with such attribute is then passed to the classifier such that the classifier's recognized class changes from correct to the desired one. Proposed generating procedure allows projecting adversarial patches not only on different areas of the face, such as nose or forehead but also on some wearable accessory, such as eyeglasses.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

Online decision support for effective mitigation actions against propagating cascading outages in a power grid still poses a big challenge to grid operators. This paper proposes an online mitigation strategy against cascading outages using an optimal power flow model based on a Dynamic Interaction Graph. For a given power grid, its interaction graph, also called an interaction model, was proposed in recent literature, which is composed of components and links that have large contributions to outage propagation. Differing from a conventional interaction graph, a Dynamic Interaction Graph can adaptively update its parameters with changes of grid topology and hence is more accurate in online identification of the most vulnerable transmission lines and likely outage propagation paths for mitigation. Further, the paper introduces an optimal power flow model based on the proposed Dynamic Interaction Graph for determining the optimal control strategy to maintain transfer margins of vulnerable transmission lines and effectively mitigate outage propagation. The numerical results on the IEEE 118-bus system demonstrate the effectiveness of the proposed models and associated control strategy. INDEX TERMS Cascading outages, Dynamic Interaction Graph, optimal power flow, mitigation actions.
A new method for flow-based network intrusion detection using inverse statistical physics

—Network Intrusion Detection Systems (NIDS) play an important role as tools for identifying potential network threats. In the context of ever-increasing trafﬁc volume on computer networks, ﬂow-based NIDS arise as good solutions for real-time trafﬁc classiﬁcation. In recent years, different ﬂow-based classiﬁers have been proposed based on both shallow and deep learning. Nevertheless, these classical machine learning algorithms have some limitations. For instance, they require large amounts of labeled data, which might be difﬁcult to obtain. Additionally, most machine learning models are not general enough to be applied in different contexts. To overcome these limitations, we propose a new ﬂow-based classiﬁer, called Energy-based Flow Classiﬁer (EFC). This anomaly-based classiﬁer uses inverse statistics to infer a model based on labeled benign examples. We show that EFC is capable to accurately perform a two-class ﬂow classiﬁcation and is resilient to context change. Given the positive results obtained, we consider EFC o be a promising algorithm to perform ﬂow-based trafﬁc classiﬁcation.
Rule based Approach for Word Normalization by resolving Transcription Ambiguity in Transliterated Search Queries

Query term matching with document term matching is the basic function of any best effort Information Retrieval models like Vector Space Model. In our problem of SMS based Information Systems we expect common people to participate in information search. Our system allows mobile users to formulate their queries in their own words, own transliteration style and spelling formation. To achieve this flexibility we have resolved the term level ambiguity due to inherent transcription noise in user query terms. We have developed a rule based approach to select most relevantly close standard term for each noisy term in the user query. We have used four different versions of the rule based algorithm with variation in the rule set. We have formulated this rule set including the basic Levenshtein minimum edit distance algorithm for term matching. This paper presents the experiments and corresponding results of Marathi and Hindi language literature information system. We have experimented on Marathi and Hindi literature which include songs, gazals, powadas, bharud and other types in a standard transliteration form like ITRANS.
Controllable Enhancements of Wi-Fi Signals at Desired Locations Without Extra Energy Using Programmable Metasurface.

We present for the first time an experimental demonstration on the energy allocation of commodity Wi-Fi signals in a programmable and inexpensive way. To that end, we design an electronically-programmable phase-binary coding metasurface, working at the 2.4GHz Wi-Fi frequency band, to manipulate dynamically and arbitrarily the spatial distribution of commodity Wi-Fi signals. Meanwhile, an efficient algorithm is developed to find the optimal coding sequence of the programmable metasurface such that the spatial energy of commodity Wi-Fi signals can be instantly controlled in a desirable way. Selected experimental results based on an IEEE 802.11n commercial Wi-Fi protocol have been provided to demonstrate the performance of the developed proof-of-concept system in enhancing the commodity Wi-Fi signals dynamically and arbitrarily. It could be expected that the proposed strategy will pave a promising way for wireless communications, future smart home, and so on.
IRLS for Sparse Recovery Revisited: Examples of Failure and a Remedy

Compressed sensing is a central topic in signal processing with myriad applications, where the goal is to recover a signal from as few observations as possible. Iterative re-weighting is one of the fundamental tools to achieve this goal. This paper re-examines the iteratively reweighted least squares (IRLS) algorithm for sparse recovery proposed by Daubechies, Devore, Fornasier, and Gunturk in \emph{Iteratively reweighted least squares minimization for sparse recovery}, {\sf Communications on Pure and Applied Mathematics}, {\bf 63}(2010) 1--38. Under the null space property of order $K$, the authors show that their algorithm converges to the unique $k$-sparse solution for $k$ strictly bounded above by a value strictly less than $K$, and this $k$-sparse solution coincides with the unique $\ell_1$ solution. On the other hand, it is known that, for $k$ less than or equal to $K$, the $k$-sparse and $\ell_1$ solutions are unique and coincide. The authors emphasize that their proof method does not apply for $k$ sufficiently close to $K$, and remark that they were unsuccessful in finding an example where the algorithm fails for these values of $k$. In this note we construct a family of examples where the Daubechies-Devore-Fornasier-Gunturk IRLS algorithm fails for $k=K$, and provide a modification to their algorithm that provably converges to the unique $k$-sparse solution for $k$ less than or equal to $K$ while preserving the local linear rate. The paper includes numerical studies of this family as well as the modified IRLS algorithm, testing their robustness under perturbations and to parameter selection.
Redshift inference from the combination of galaxy colours and clustering in a hierarchical Bayesian model – Application to realistic N-body simulations

Photometric galaxy surveys constitute a powerful cosmological probe but rely on the accurate characterization of their redshift distributions using only broadband imaging, and can be very sensitive to incomplete or biased priors used for redshift calibration. Sanchez & Bernstein (2019) presented a hierarchical Bayesian model which estimates those from the robust combination of prior information, photometry of single galaxies and the information contained in the galaxy clustering against a well-characterized tracer population. In this work, we extend the method so that it can be applied to real data, developing some necessary new extensions to it, especially in the treatment of galaxy clustering information, and we test it on realistic simulations. After marginalizing over the mapping between the clustering estimator and the actual density distribution of the sample galaxies, and using prior information from a small patch of the survey, we find the incorporation of clustering information with photo-$z$'s to tighten the redshift posteriors, and to overcome biases in the prior that mimic those happening in spectroscopic samples. The method presented here uses all the information at hand to reduce prior biases and incompleteness. Even in cases where we artificially bias the spectroscopic sample to induce a shift in mean redshift of $\Delta \bar z \approx 0.05,$ the final biases in the posterior are $\Delta \bar z \lesssim0.003. $ This robustness to flaws in the redshift prior or training samples would constitute a milestone for the control of redshift systematic uncertainties in future weak lensing analyses.
Optimising Individual-Treatment-Effect Using Bandits

Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the individual-treatment-effect (ITE) in settings such as precision medicine and targeted advertising has peaked in application. Optimising this ITE under the strong-ignorability-assumption -- meaning all confounders expressing influence on the outcome of a treatment are registered in the data -- is often referred to as uplift modeling (UM). While these techniques have proven useful in many settings, they suffer vividly in a dynamic environment due to concept drift. Take for example the negative influence on a marketing campaign when a competitor product is released. To counter this, we propose the uplifted contextual multi-armed bandit (U-CMAB), a novel approach to optimise the ITE by drawing upon bandit literature. Experiments on real and simulated data indicate that our proposed approach compares favourably against the state-of-the-art. All our code can be found online at this https URL.
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings

The meaning of a word often varies depending on its usage in different domains. The standard word embedding models struggle to represent this variation, as they learn a single global representation for a word. We propose a method to learn domain-specific word embeddings, from text organized into hierarchical domains, such as reviews in an e-commerce website, where products follow a taxonomy. Our structured probabilistic model allows vector representations for the same word to drift away from each other for distant domains in the taxonomy, to accommodate its domain-specific meanings. By learning sets of domain-specific word representations jointly, our model can leverage domain relationships, and it scales well with the number of domains. Using large real-world review datasets, we demonstrate the effectiveness of our model compared to state-of-the-art approaches, in learning domain-specific word embeddings that are both intuitive to humans and benefit downstream NLP tasks.
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music

Quantitative analysis of commonalities and differences between recorded music performances is an increasingly common task in computational musicology. A typical scenario involves manual annotation of different recordings of the same piece along the time dimension, for comparative analysis of, e.g., the musical tempo, or for mapping other performance-related information between performances. This can be done by manually annotating one reference performance, and then automatically synchronizing other performances, using audio-to-audio alignment algorithms. In this paper we address several questions related to those tasks. First, we analyze different annotations of the same musical piece, quantifying timing deviations between the respective human annotators. A statistical evaluation of the marker time stamps will provide (a) an estimate of the expected timing precision of human annotations and (b) a ground truth for subsequent automatic alignment experiments. We then carry out a systematic evaluation of different audio features for audio-to-audio alignment, quantifying the degree of alignment accuracy that can be achieved, and relate this to the results from the annotation study.
The construction of Electromagnetism

We examine the construction of electromagnetism in its current form, and in an alternative form, from a point of view that combines a minimal realism with strict demands of reason that we first introduce. We follow the historical development as presented in the record of original publications, the underlying epistemology (often explained by the authors) and the mathematical constructions. The historical construction develops along socio-political disputes (mainly, the reunification of Germany and the second industrial revolution), epistemic disputes (at least two demarcations of science in conflict) and several theories of electromagnetism. Such disputes resulted in the militant adoption of the ether by some, a position that expanded in parallel with the expansion of Prussia, but was facilitated by the earlier adoption of a standpoint that required physical hypothesis in the form of analogies as condition for understanding, antithetic to Newton's "\emph{hypotheses non fingo}".
Adaptive Exploration in Linear Contextual Bandit

Contextual bandits serve as a fundamental model for many sequential decision making tasks. The most popular theoretically justified approaches are based on the optimism principle. While these algorithms can be practical, they are known to be suboptimal asymptotically (Lattimore and Szepesvari, 2017). On the other hand, existing asymptotically optimal algorithms for this problem do not exploit the linear structure in an optimal way and suffer from lower-order terms that dominate the regret in all practically interesting regimes. We start to bridge the gap by designing an algorithm that is asymptotically optimal and has good finite-time empirical performance. At the same time, we make connections to the recent literature on when exploration-free methods are effective. Indeed, if the distribution of contexts is well behaved, then our algorithm acts mostly greedily and enjoys sub-logarithmic regret. Furthermore, our approach is adaptive in the sense that it automatically detects the nice case. Numerical results demonstrate significant regret reductions by our method relative to several baselines.