Miracle-dz/UniFAR-FTUs huggingface dataset:
Otomycosis in Western Iran: Clinical and Mycological Aspects

Background: Otomycosis is a globally distributed superficial infection of the auricle and external auditory canal. Its incidence differs in various geographical regions due to the different climatic conditions. Objectives: The aim of this study was to determine the incidence of otomycosis in Khorramabad, Lorestan province, western Iran, using mycological besides clinical criteria. Methods: In this cross-sectional descriptive study, a total of 79 patients clinically suspected to otomycosis were recruited from April 2014 to April 2015. Specimens were collected using sterile swabs. All the specimens were subjected to direct examination using 10% KOH, and culture in Sabouraud dextrose agar containing chloramphenicol, malt-extract agar, and Czapek Dox agar. CHROMagar candida and carbohydrate assimilation profile in API 32 C were used for the identification of yeasts. Results: Among 79 patients, 15 (19%) were confirmed for otomycosis. The most common agent was Candida albicans (5, 33.33%), followed by Aspergillus flavus (4, 26.64%), Penicillium spp. (3, 19.98%), A. niger (2, 13.32%), and Alternaria spp. (1, 6.66%). Females were the dominant involved group (11, 73.33%) and itching was the most frequent clinical compliant in 100% of cases with otomycosis. Conclusions: Regarding the dissimilarity in fungal spectrum in our study and other investigations and unspecificity of clinical signs and symptoms for otomycosis, mycological examination could be a beneficial measure for accurate diagnosis and treatment of otomycosis.
Decoherence and the Transactional Interpretation

This paper presents an analysis of decoherence resulting from the physically real non-unitarity, or 'objective reduction,' that occurs in the Transactional Interpretation (TI). Two distinct aspects of the decoherence process are identified and disambiguated; specifically, (i) the resolution of the basic measurement interaction with respect to the observable under study, and (ii) the effect on the measured system of repetition of the measurement interaction. It is shown that the measurement interaction as described in TI leads naturally to the same quantitative expression for the decoherence function as in the standard unitary-only account. However, unlike in the unitary-only approach, under TI, the reduced density operator for the measured system can legitimately be interpreted as representing the occurrence of an actual measurement result.
This PIN Can Be Easily Guessed: Analyzing the Security of Smartphone Unlock PINs

In this paper, we provide the first comprehensive study of user-chosen 4- and 6-digit PINs (n = 1220) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using 6-digit PINs instead of 4-digit PINs provides little to no increase in security, and surprisingly may even decrease security. We also study the effects of blacklists, where a set of "easy to guess" PINs is disallowed during selection. Two such blacklists are in use today by iOS, for 4-digits (274 PINs) as well as 6-digits (2910 PINs). We extracted both blacklists compared them with four other blacklists, including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN. We find that relatively small blacklists in use today by iOS offer little or no benefit against a throttled guessing attack. Security gains are only observed when the blacklists are much larger, which in turn comes at the cost of increased user frustration. Our analysis suggests that a blacklist at about 10 % of the PIN space may provide the best balance between usability and security.
Learning to Cache and Caching to Learn: Regret Analysis of Caching Algorithms

Crucial performance metrics of a caching algorithm include its ability to quickly and accurately learn a popularity distribution of requests. However, a majority of work on analytical performance analysis focuses on hit probability after an asymptotically large time has elapsed. We consider an online learning viewpoint, and characterize the “regret” in terms of the finite time difference between the hits achieved by a candidate caching algorithm with respect to a genie-aided scheme that places the most popular items in the cache. We first consider the Full Observation regime wherein all requests are seen by the cache. We show that the Least Frequently Used (LFU) algorithm is able to achieve order optimal regret, which is matched by an efficient counting algorithm design that we call LFU-Lite. We then consider the Partial Observation regime wherein only requests for items currently cached are seen by the cache, making it similar to an online learning problem related to the multi-armed bandit problem. We show how approaching this “caching bandit” using traditional approaches yields either high complexity or regret, but a simple algorithm design that exploits the structure of the distribution can ensure order optimal regret. We conclude by illustrating our insights using numerical simulations.
PENGETAHUAN, SIKAP, TINDAKAN DAN LINGKUNGAN RUMAH PENDERITA TB-PARU DI WILAYAH KERJA PUSKESMAS PEGIRIAN TAHUN 2008 – 2009

Tuberkulosis masih merupakan masalah kesehatan baik di Indonesia maupun di dunia dan merupakan penyebab utama kematian. Tuberkulosis didefinisikan sebagai penyakit menular langsung yang disebabkan oleh kuman TB (Mycobacterium Tuberculosis). Sebagian besar kuman TB menyerang paru, yaitu disebut dengan tuberkulosis paru (TB-Paru). Pengetahuan, sikap, tindakan dan lingkungan rumah dirasa mempengaruhi status penyakit seseorang. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana hubungan pengetahuan, sikap, tindakan dan lingkungan rumah penderita terhadap kejadian TB-Paru. Jenis penelitian ini adalah observasional dengan rancang bangun kasus kontrol. Pada kelompok kasus menggunakan 30 penderita TB-Paru di wilayah kerja Puskesmas Pegirian. Kontrol sebanyak 30 orang diambil dari tetangga penderita untuk mengeliminasi faktor perancu dengan proporsi 1:1. Variabel independen dalam penelitian ini adalah pengetahuan, sikap, tindakan dan lingkungan rumah yang meliputi jenis dinding rumah, jenis lantai, kepadatan penghuni, ventilasi dan luas kamar sedangkan variabel dependent adalah kejadian TB-Paru. Kedua variabel tersebut akan diuji menggunakan uji Chi Square (X2). Hasil yang didapatkan adalah tidak adanya hubungan antara jenis dinding, jenis lantai, kepadatan penghuni, dan ventilasi dengan kejadian TB-Paru. Hubungan yang signifikan didapatkan pada variabel kejadian TB-Paru dengan pengetahuan (P = 0,002; OR = 6,58) yang artinya responden dengan pengetahuan rendah berhubungan dengan kejadian TB-Paru dengan risiko 6,58 kali, sikap (P = 0,004; OR = 5,49) yang artinya responden dengan sikap negatif berhubungan dengan kejadian TB-Paru dengan risiko 5,49 kali, tindakan (P = 0,03; OR = 4)yang artinya responden dengan tindakan kurang berhubungan dengan kejadian TB-Paru dengan risiko 4 kali, dan luas kamar (P = 0,000; OR = 16). yang artinya responden dengan luas kamar < 9 m2 berhubungan dengan kejadian TB-Paru dengan risiko 16 kali. Berdasarkan hasil penelitian, diperlukan pengetahuan yang benar bagi responden tentang TB-Paru. Hal ini diperlukan untuk membentuk perilaku yang baik agar keluarga dapat melakukan upaya pencegahan. Oleh karena itu diharapkan bagi para tenaga kesehatan untuk meningkatkan penyuluhan tentang TB-Paru.
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints

We present a new approach to the type inference problem for dynamic languages. Our goal is to combine logical constraints, that is, deterministic information from a type system, with natural constraints, that is, uncertain statistical information about types learnt from sources like identifier names. To this end, we introduce a framework for probabilistic type inference that combines logic and learning: logical constraints on the types are extracted from the program, and deep learning is applied to predict types from surface-level code properties that are statistically associated, such as variable names. The foremost insight of our method is to constrain the predictions from the learning procedure to respect the logical constraints, which we achieve by relaxing the logical inference problem of type prediction into a continuous optimisation problem. As proof of concept, we build a tool called OptTyper to predict missing types for TypeScript files. OptTyper combines a continuous interpretation of logical constraints derived by a simple program transformation and static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase. By evaluating OptTyper, we show that the combination of logical and natural constraints yields a large improvement in performance over either kind of information individually and achieves a 3% improvement over the state-of-the-art.
GRADIENT FLOWS OF HIGHER ORDER YANG–MILLS–HIGGS FUNCTIONALS

Abstract In this paper, we define a family of functionals generalizing the Yang–Mills–Higgs functionals on a closed Riemannian manifold. Then we prove the short-time existence of the corresponding gradient flow by a gauge-fixing technique. The lack of a maximum principle for the higher order operator brings us a lot of inconvenience during the estimates for the Higgs field. We observe that the $L^2$ -bound of the Higgs field is enough for energy estimates in four dimensions and we show that, provided the order of derivatives appearing in the higher order Yang–Mills–Higgs functionals is strictly greater than one, solutions to the gradient flow do not hit any finite-time singularities. As for the Yang–Mills–Higgs k-functional with Higgs self-interaction, we show that, provided $\dim (M)<2(k+1)$ , for every smooth initial data the associated gradient flow admits long-time existence. The proof depends on local $L^2$ -derivative estimates, energy estimates and blow-up analysis.
PROTECTIVE EFFECT OF TROPICAL FRUIT JUICE ON HISTOPATHOLOGICAL IMAGE OF RATS LUNG EXPOSED TO CIGARETTE SMOKE

Indonesia is a tropical country that has diversity of fruit. Fruit contain an array of antioxidant substances that give benefit for human health. This research purpose is to analyse the protective effect of antioxidant-rich tropical fruits juice on the lung of Wistar rats that exposed to cigarette smoke. Adult male Wistar rats (n 35) were divided into five groups of seven each, as follows: control group; cigarrette smoke group; cigarette strawbery,papaya juice; cigarette+strawbery,red guava juice; cigarette+strawbery, apple juice. Rats exposed to cigarette smoke 20 minutes/day for 40 days. Three ml of mixed fruit juice was given orally one hour after exposed. We analyse the structural damage of alveolus lumen and membrane, along with connection between alveolus. The results show that the structural damage of alveolus lumen and connection between alveolus of all fruit juice groups were significantly decrease (P<0,05) compared with cigarette smoke without fruit juice group. Strawbery & apple juice shows the best protective effect on the lung of Wistar rats that exposed to cigarette smoke.
A clustering framework for lexical normalization of Roman Urdu

Abstract Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content. It lacks standard spelling and hence poses several normalization challenges during automatic language processing. In this article, we present a feature-based clustering framework for the lexical normalization of Roman Urdu corpora, which includes a phonetic algorithm UrduPhone, a string matching component, a feature-based similarity function, and a clustering algorithm Lex-Var. UrduPhone encodes Roman Urdu strings to their pronunciation-based representations. The string matching component handles character-level variations that occur when writing Urdu using Roman script. The similarity function incorporates various phonetic-based, string-based, and contextual features of words. The Lex-Var algorithm is a variant of the k-medoids clustering algorithm that groups lexical variations of words. It contains a similarity threshold to balance the number of clusters and their maximum similarity. The framework allows feature learning and optimization in addition to the use of predefined features and weights. We evaluate our framework extensively on four real-world datasets and show an F-measure gain of up to 15% from baseline methods. We also demonstrate the superiority of UrduPhone and Lex-Var in comparison to respective alternate algorithms in our clustering framework for the lexical normalization of Roman Urdu.
Scale symmetry, the Higgs and the Cosmos

I review the Higgs-Dilaton model: a scale-invariant extension of the Standard Model and gravity able to support inflation and dark energy with just an additional degree of freedom on top of the Standard Model content. Potential extensions of the simplest realization on the basis of transverse diffeomorphisms are also discussed.
A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection

In this paper, we reveal that artificial neural network (ANN) assisted multiple-input multiple-output (MIMO) signal detection can be modeled as ANN-assisted lossy vector quantization (VQ), named MIMO-VQ, which is basically a joint statistical channel quantization and signal quantization procedure. It is found that the quantization loss increases linearly with the number of transmit antennas, and thus MIMO-VQ scales poorly with the size of MIMO. Motivated by this finding, we propose a novel modular neural network based approach, termed MNNet, where the whole network is formed by a set of pre-defined ANN modules. The key of ANN module design lies in the integration of parallel interference cancellation in the MNNet, which linearly reduces the interference (or equivalently the number of transmit-antennas) along the feed-forward propagation; and so as the quantization loss. Our simulation results show that the MNNet approach largely improves the deep-learning capacity with near-optimal performance in various cases. Provided that MNNet is well modularized, the learning procedure does not need to be applied on the entire network as a whole, but rather at the modular level. Due to this reason, MNNet has the advantage of much lower learning complexity than other deep-learning based MIMO detection approaches.
Lithium intercalation in 
MoS2
 bilayers and implications for moiré flat bands

Intercalation of lithium atoms between layers of 2D materials can alter their atomic and electronic structure. We investigate effects of Li intercalation in twisted bilayers of the transition metal dichalcogenide MoS$_2$ through first-principles calculations, tight-binding parameterization based on the Wannier transformation, and analysis of moire band structures through an effective continuum model. The energetic stability of different intercalation sites for Li between layers of MoS$_2$ are classified according to the local coordination type and the number of vertically aligned Mo atoms, suggesting that the Li atoms will cluster in certain regions of the moire superlattice. The proximity of a Li atom has a dramatic influence on the interlayer interaction between sulfur atoms, deepening the moire potential well and leading to better isolation of the flat bands in the energy spectrum. These results point to the usefulness for the use of chemical intercalation as a powerful means for controlling moire flat-band physics in 2D semiconductors.
The fourth moment of Dirichlet L-functions

In this paper, we study the fourth moment of Dirichlet $L$-functions averaged over primitive characters to modulus $q$ and over $t\in [0,T]$, for general $q$ and $T$. When $T\gg q^\varepsilon$ is not too small with respect to $q$, we obtain an asymptotic formula with a power savings in the error term. In addition, when $q$ is a prime, the weak condition $T\gg q^\varepsilon$ is unnecessary, as well as a more sharp error has been obtained. These improvements also benefit from an extension of Young's classical work [The fourth moment of Dirichlet L-functions, Ann. of Math. (2) 173 (2011), no. 1, 1-50] for prime $q$.
Image Demoireing with Learnable Bandpass Filters

Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration, we propose a learnable bandpass filter (LBF) to learn the frequency prior for moire texture removal. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, then performs local fine tuning of the color per pixel. Through an ablation study, we demonstrate the effectiveness of the different components of MBCNN. Experimental results on two public datasets show that our method outperforms state-of-the-art methods by a large margin (more than 2dB in terms of PSNR).
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage

Model selection when designing deep learning systems for specific use-cases can be a challenging task as many options exist and it can be difficult to know the trade-off between them. Therefore, we investigate a number of state of the art CNN models for the task of measuring kernel fragmentation in harvested corn silage. The models are evaluated across a number of feature extractors and image sizes in order to determine optimal model design choices based upon the trade-off between model complexity, accuracy and speed. We show that accuracy improvements can be made with more complex meta-architectures and speed can be optimised by decreasing the image size with only slight losses in accuracy. Additionally, we show improvements in Average Precision at an Intersection over Union of 0.5 of up to 20 percentage points while also decreasing inference time in comparison to previously published work. This result for better model selection enables opportunities for creating systems that can aid farmers in improving their silage quality while harvesting.
Beurling type invariant subspaces of composition operators

The aim of this paper is to answer the following question concerning invariant subspaces of composition operators: characterize φ, holomorphic self maps of D, and inner functions θ∈H∞(D) such that the Beurling type invariant subspace θH2 is an invariant subspace for Cφ. We prove the following result: Cφ(θH2)⊆θH2 if and only if θ∘φθ∈S(D). This classification also allows us to recover or improve some known results on Beurling type invariant subspaces of composition operators.
Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes

Machine learning-based multi-label medical text classifications can be used to enhance the understanding of the human body and aid the need for patient care. We present a broad study on clinical natural language processing techniques to maximise a feature representing text when predicting medical codes on patients with multi-morbidity. We present results of multi-label medical text classification problems with 18, 50 and 155 labels. We compare several variations to embeddings, text tagging, and pre-processing. For imbalanced data we show that labels which occur infrequently, benefit the most from additional features incorporated in embeddings. We also show that high dimensional embeddings pre-trained using health-related data present a significant improvement in a multi-label setting, similarly to the way they improve performance for binary classification. High dimensional embeddings from this research are made available for public use.
One-shot path planning for multi-agent systems using fully convolutional neural network

Path planning plays a crucial role in robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated iteratively by predicting the next state based on the current state. Moreover, in case of multi-agent systems, paths are planned for each agent separately. In contrast to that, we propose a novel method by utilising fully convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. We demonstrate that our method is able to successfully generate optimal or close to optimal paths in more than 98\% of the cases for single path predictions. Moreover, we show that although the network has never been trained on multi-path planning it is also able to generate optimal or close to optimal paths in 85.7\% and 65.4\% of the cases when generating two and three paths, respectively.
A theory of independent mechanisms for extrapolation in generative models

Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating process, such that one can intervene on each node independently of the others. However, the nodes of this graph are usually unobserved, leading to overparameterization and lack of identifiability of the causal structure. We develop a theoretical framework to address this challenging situation by defining a weaker form of identifiability, based on the principle of independence of mechanisms. We demonstrate on toy examples that classical stochastic gradient descent can hinder the model's extrapolation capabilities, suggesting independence of mechanisms should be enforced explicitly during training. Experiments on deep generative models trained on real world data support these insights and illustrate how the extrapolation capabilities of such models can be leveraged.
Higgs-mode resonance in third harmonic generation in NbN superconductors: Multiband electron-phonon coupling, impurity scattering, and polarization-angle dependence

We theoretically investigate the resonance of third harmonic generation (THG) that has been observed at frequency being half of the superconducting gap in a multiband disordered superconductor NbN. The central question is whether the dominant contribution to the THG resonance comes from the Higgs mode (the collective amplitude mode of the superconducting order parameter) or quasiparticle excitations. To resolve this issue, we analyze a realistic three-band model with effective intraband and interband phonon-mediated interactions estimated from first-principles together with nonmagnetic impurity scatterings. Using the estimated pairing interactions with multiband impurity scattering rates being varied from clean to dirty regimes, we calculate the THG susceptibility for NbN in a channel-resolved manner by means of the BCS and self-consistent Born approximations. In the dirty regime, which is close to the experimental situation, the leading contribution is given by the paramagnetic channel of the Higgs mode having almost no polarization-angle dependence, while the second leading contribution comes from the paramagnetic channel of quasiparticles generally showing significant polarization-angle dependence. The result is consistent with the recent experimental observation of no polarization-angle dependence of THG, giving firm evidence that the Higgs mode dominantly contributes to the THG resonance in NbN superconductors.