Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjuvant treatment of non-malignant active and aggressive bone tumors

Aggressive but nonmalignant bone tumors often pose a difficult therapeutic dilemma for orthopaedic oncologists. Intralesional surgery usually is the modality of choice to lessen the morbidity associated with a marginal or wide excision. With more aggressive tumors, such as giant cell tumor of bone, chondroblastoma or aneurysmal bone cysts, high recurrence rates after curettage alone present a challenge to the surgeon. Adjuvant therapies provide an additional means of clearing the cavity of tumor cells after aggressive intralesional resection. The addition of adjuvants then is able to extend the margins beyond that which is visible to a marginal excision, removing microscopic disease. Clinically, this yields lower recurrence rates, with different methods having better efficacy. With the less aggressive yet still active lesions, injections and percutaneous methods may be preferred. This article discusses some of the more common as well as emerging trends in adjuvant therapy for active and aggressive nonmalignant bone tumors. Broad categories included are mechanical (burring), hypothermic or hyperthermic (cryotherapy, argon beam, cementation), chemical (phenol, hydrogen peroxide), and biologic modalities (bisphosphonates). Injectable materials also are discussed. Current literature regarding these adjuvants (focusing on the past 2 years) is presented, with an eye on clinical recommendations.
Assessment of the possibility of recycling backwashing water from the swimming pool water treatment system

The paper presents the physicochemical analysis and toxicological assessment of backwashing samples taken after the process of washing filter beds in a raw condition after the process of their aeration and dechlorination. The backwash water under investigation originated from circulation systems existing in two indoor swimming pool facilities. The backwash water, as used at the preliminary and the main stages, was characterized by different physicochemical properties. For toxicological assessment, the Mictorox bioluminescence inhibition test, the Chaoborus sp. insect larva survival test and the phyto test using Lemna minor fine cilium were involved. The investigation presented in the paper included a preliminary phase focusing on the ecotoxic characterization of backwash water subjected to aeration and dechlorination processes. In turn, at the main stage, the effect of aeration duration on the quality of backwash water in terms of its physicochemical parameters was analyzed. The results of the preliminary stage investigation indicate that backwash water, both in a raw condition and after 30 minutes’ aeration, could not be discharged directly to the environment due to the threat to living organisms caused by its high toxicity. Whereas, using 160 minutes’ aeration duration contributed to a significant improvement in the quality of the backwash water and elimination of its toxic properties with respect to the indicator organisms used. The chemical dechlorination process brought about varying effects. In the case of the Microtox test, a stimulation of bacterial bioluminescence was noted, but, at the same time, the death of individual insect larva specimens was observed. In spite of the high biomass increase in the Lemna minor test, a gradual discolouration of fronds under the influence of backwash water was observed. Because of the presence of numerous compounds being disinfection by-products, as well as coagulant residues in backwash water deriving from swimming pool systems, it is necessary to seek further solutions that will allow them to be recycled, which will result in a reduction of water consumption and effluent discharges.
A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification

One of the main motivations for classifying knee kinematic signals, namely the variation during a locomotion gait cycle of the angles the knee makes with respect to the three-dimensional (3D) planes of flexion/extension, abduction/adduction, and internal/external rotation, is to assist diagnosis of knee pathologies. These signals are informative but high dimensional, and highly variable, which has posed difficulties that have been addressed by machine learning algorithms. The purpose of this study is to investigate classification of knee kinematic signals through the entire gait using deep neural networks. The signals are first pre-processed to identify representative patterns, which are then used for deep learning of discriminative classifiers. This paper describes an efficient means of distinguishing between knee osteoarthrisis patients and asymptomatic participants, and our methods and experiments which validate it.
Project : Metric Diophantine Approximation for Formal Laurent Series

In a recent paper, Deligero and Nakada devised a new approach to investigate the number of coprime solutions of the diophantine approximation problem for formal Laurent series over a finite base field. In this project, we further developed their method and proved almost sure and distributional type invariance principles. One of the consequences of our results is a generalization of the analogue of a famous theorem of Khintchine. Despite a lot of previous research efforts, the corresponding result in the classical case has not been established yet. 1 General This is the final report on the National Science Council project “Metric Diophantine Approximation for Formal Laurent Series” with grant number NSC-94-2115-M-009-011 and term from August 1st, 2005 to July 31st, 2006. Before going into details, we shortly summarize the main achievements. • The paper [1] contains the main findings of this project. It was submitted and will be published in one of the forthcoming issues of Finite Fields and Their Applications . • A three week research visit at the Vienna University of Technology from July 7, 2006 to July 29, 2006 was partially financed by the project. A report on this research visit will be handled in separately. 2 Results Subsequently, we shortly describe some of our main results and their significance. For details, we refer the reader to [1]. Let Fq((T)) denote the field of formal Laurent series over a finite field Fq with q elements equipped with the usual standard evaluation. Fix an f ∈ Fq((T)) with |f | < 1. We are interested in the number of solutions P, Q ∈ Fq[T ], Q 6= 0 of the diophantine approximation problem ∣∣∣∣f − P Q ∣∣∣∣ < 1 q2n+ln , deg Q = n, (P, Q) = 1, (1) where (ln) is a sequence of positive integers. Subsequently, we denote by m the unique, translation-invariant probability measure on the measure space consisting of {f ∈ Fq((T))||f | < 1} and its set of Borel sets. The following result was proved by deMathan in [3] (see [4] for a different method of proof) and is the analogue of a famous theorem of Khintchine.
The Design of Optimal Collateralized Contracts*

This paper presents a two-period optimal contracting model of collateral. A borrower values a capital good and a composite non-capital good. He privately observes an income shock in the composite good in the second period. Collateralization of both goods occurs in the optimal contract, whereas it does not under full information. Relative to full information, the capital good in the optimal contract is over-consumed in the initial loan period and under-consumed in the repayment period. The relation between forfeiture of assets and contractual distortion is summarized by a formula showing higher distortions associated with larger increases in forfeited collateral. Forfeiture is decreasing in income at the tails of the income distribution, and low income types forfeit more than high income types. In some parametric cases, forfeited collateral is globally decreasing in income with pooling at the bottom when the borrower’s initial wealth is low, or when income shock is sufficiently diffuse, resembling defaults to real world collateralized contracts. JEL Codes: D82, D86, D53, D14, G21, G22
Trade credit use by shrimp farmers in Ca Mau province

Purpose The purpose of this paper is to investigate the determinants of the amount of trade credit granted to shrimp farmers in Ca Mau. Design/methodology/approach Based on the literature review, the authors proposed six hypotheses on the determinants of the amount of trade credit granted to shrimp farmers. Data collected from 120 shrimp farmers in Ca Mau were used to test the proposed hypotheses. Findings Two out of six determinants, i.e. the size of input order (a pulling factor) and the competition among input suppliers (a pushing factor), are significantly positively associated with the amount of trade credit granted to shrimp farmers. No impact of the other determinants was found. The findings imply that shrimp farmers should join cooperatives to enhance access to trade credit and mitigate the risk for input suppliers. Originality/value This paper sheds light on the fact that trade credit is still granted to such risky buyers as shrimp farmers, which has not been explored by previous studies.
Performance Effects of WPA 2 Encryption and Topology on IEEE 802 . 11 a Laboratory Links

The increasing importance of wireless communications, involving electronic devices, has been worldly recognized. Performance is a crucial issue, leading to more reliable and efficient communications. Security is also critically important. Laboratory measurements were performed about several performance aspects of Wi-Fi IEEE 802.11a WPA2 point-to-multipoint links. Our study contributes to the performance evaluation of this technology, using available equipments (HP V-M200 access points and Linksys WPC600N adapters). New detailed results are presented and discussed, namely at OSI levels 4 and 7, from TCP, UDP and FTP experiments: TCP throughput, jitter, percentage datagram loss and FTP transfer rate. Comparisons are made to corresponding results obtained for WPA2 point-to-point and Open links. Conclusions are drawn about the comparative performance of the links.
The effect of practicing traditional dances on selected psychological variables in high school students in Ethiopia

The purpose of the present investigation was to evaluate the effect of practicing Tigray Regional State Traditional Dances on selected psychological variables, i.e. anxiety and self-esteem, in a sample of one 100 adolescent boys and girls (age 15 – 17 years) who volunteered. The subjects were divided into two groups i.e., the experimental group (n=80) and the control group (n=20). Furthermore, the experimental group was divided into four subgroups (n= 4X20). Each experimental subgroup was practicing for 16 weeks one of the four Traditional Dances of Awris, Hura, Kuda and Shediva. On the contrary, the participants in the control group were counseled to follow their usual daily routine activity. Additionally, all participants were tested on selected criterion measures on anxiety and self-esteem prior to and after the investigation period which lasted for 16-weeks. All data were statistically examined using ANOVA for each one of the studied variables separately. Whenever ‘F’ ratio of adjusted post-test was found to be significant the Scheffe’s test was used to determine the paired mean differences. The level of significance was set at p<0.05. Following the 16-weeks traditional dance practicing, significant positive changes were observed on anxiety and self-esteem variables in the four experimental groups compared with the control group. It can be, therefore, concluded, that Tigray traditional dance practicing can notably improve the psychological wellbeing of high school students.
Synergistic Use of Synthetic Aperture Radar and Optical Imagery to Monitor Surface Accumulation of Cyanobacteria in the Curonian Lagoon

Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A “ready to use” approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon.
Analysis of Inventory Turnover as a Performance Measure in Manufacturing Industry

Using an appropriate measure to assess firms’ performance is essential. We analyzed inventory turnover (IT) as a performance measure in manufacturing processes because IT ratios are critical in the manufacturing industry and publicly available objective measures. Using the data of 421 manufacturing companies in Korea from 2010 to 2018, we conducted an extensive analysis of the factors affecting IT by segment and its correlation with other financial ratios. Then, we compare performances between the top and bottom companies determined by Altman’s Z score approach. We found that, for the overall manufacturing industry, IT ratios were negatively correlated with gross margin and debt cost, but positively correlated with capital intensity, although the results varied by segment. Moreover, IT ratios did not show significant correlations with other financial ratios categorized for growth, profitability, stability, productivity, and value of companies. However, adjusted IT (AIT) can be a good indicator of firms’ performance in terms of financial sustainability. Results also revealed that the top 10% companies showed higher AIT ratios than the bottom 10% in most segments of the manufacturing industry. The analysis of this study can be a starting point to search for a composite index to evaluate manufacturing processes comprehensively.
A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems

In this paper, we propose a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations. The PhaseDNN makes use of the fact that common DNNs often achieve convergence in the low frequency range first, and a series of moderately-sized DNNs are constructed and trained for selected high frequency ranges. With the help of phase shifts in the frequency domain, each of the DNNs will be trained to approximate the function's higher frequency content over a specific range at the the speed of convergence as in the low frequency range. As a result, the proposed PhaseDNN is able to convert high frequency learning to low frequency one, allowing a uniform learning to wideband functions. The PhaseDNN will then be applied to find the solution of high frequency wave equations in inhomogeneous media through both differential and integral equation formulations with least square residual loss functions. Numerical results have demonstrated the capability of the PhaseDNN in learning high frequency functions and oscillatory solutions of interior and exterior Helmholtz equations.
Stability conditions and moduli spaces for
Kuznetsov components of Gushel–Mukai varieties

We prove the existence of Bridgeland stability conditions on the Kuznetsov components of Gushel-Mukai varieties, and describe the structure of moduli spaces of Bridgeland semistable objects in these categories in the even-dimensional case. As applications, we construct a new infinite series of unirational locally complete families of polarized hyperkahler varieties of K3 type, and characterize Hodge-theoretically when the Kuznetsov component of an even-dimensional Gushel-Mukai variety is equivalent to the derived category of a K3 surface.
Serum Sodium Levels in Children with Lung Infections

Objective: Serum sodium levels change in patients with lung infections. This study aimed to evaluate serum sodium levels in patients who were admitted to the hospital with a lower respiratory tract infection (LRTI) and to assess its association with C-reactive protein (CRP) levels. Material and Methods: A cross-sectional study was designed. A total of 549 inpatients (207 females, 37.7%; 342 males, 62.3%) aged 1–15 years who were diagnosed with LRTI (pneumonia, bronchitis, and bronchiolitis) at the Kars State Hospital’s pediatric ward between 01.01. 2011 and 30.06. 2014 were included in the study. The records of these patients were retrospectively reviewed. Age, gender, white blood cell (WBC) count, absolute neutrophil count (ANC), sodium levels, and CRP levels were recorded. The patients were classified into five groups according to serum sodium levels as severe hyponatremia, moderate hyponatremia, mild hyponatremia, normonatremia, and hypernatremia. Results: There was no significant difference for age or gender between the hyponatremia and normonatremia groups. There was one patient with severe hyponatremia (≤125 mmol/L), nine with moderate hyponatremia (126–130 mmol/L), 90 with mild hyponatremia (131–134 mmol/L), 446 with normonatremia (135–145 mmol/L), and three with hypernatremia (≥146 mmol/L). There was a negative correlation between serum sodium levels and CRP levels (r=0.178; p<0.001), WBC counts (r=-0.121; p=0.004), and ANC (r=-0.100; p=0.048). Conclusion: There was a statistically significant negative correlation between serum sodium levels and acutephase reactants in patients with pneumonia. Serum sodium levels must be closely monitored in these patients, particularly when acute-phase reactant levels are high. (J Pediatr Inf 2016; 10: 10-3)
Spectrum-Wide Quantum Criticality at the Surface of Class AIII Topological Phases: An “Energy Stack” of Integer Quantum Hall Plateau Transitions

In the absence of spin-orbit coupling, the conventional dogma of Anderson localization asserts that all states localize in two dimensions, with a glaring exception: the quantum Hall plateau transition (QHPT). In that case, the localization length diverges and interference-induced quantum-critical spatial fluctuations appear at all length scales. Normally QHPT states occur only at isolated energies; accessing them therefore requires fine-tuning of the electron density or magnetic field. In this paper we show that QHPT states can be realized throughout an energy continuum, i.e. as an "energy stack" of critical states wherein each state in the stack exhibits QHPT phenomenology. The stacking occurs without fine-tuning at the surface of a class AIII topological phase, where it is protected by U(1) and (anomalous) chiral or time-reversal symmetries. Spectrum-wide criticality is diagnosed by comparing numerics to universal results for the longitudinal Landauer conductance and wave function multifractality at the QHPT. Results are obtained from an effective 2D surface field theory and from a bulk 3D lattice model. We demonstrate that the stacking of quantum-critical QHPT states is a robust phenomenon that occurs for AIII topological phases with both odd and even winding numbers. The latter conclusion may have important implications for the still poorly-understood logarithmic conformal field theory believed to describe the QHPT.
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for less popular items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.
Connes’s trace theorem for curved noncommutative tori: Application to scalar curvature

In this paper we prove a version of Connes' trace theorem for noncommutative tori of any dimension~$n\geq 2$. This allows us to recover and improve earlier versions of this result in dimension $n=2$ and $n=4$ by Fathizadeh-Khalkhali. We also recover the Connes integration formula for flat noncommutative tori of McDonald-Sukochev-Zanin. As a further application we prove a curved version of this integration formula in terms of the Laplace-Beltrami operator defined by an arbitrary Riemannian metric. For the class of so-called self-compatible Riemannian metrics (including the conformally flat metrics of Connes-Tretkoff) this shows that Connes' noncommutative integral allows us to recover the Riemannian density. This exhibits a neat link between this notion of noncommutative integral and noncommutative measure theory in the sense of operator algebras. As an application of these results, we setup a natural notion of scalar curvature for curved noncommutative tori.
Multilingual is not enough: BERT for Finnish

Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with recent approaches such as the transformer-based BERT model advancing the state of the art across a variety of tasks. While most work on these models has focused on high-resource languages, in particular English, a number of recent efforts have introduced multilingual models that can be fine-tuned to address tasks in a large number of different languages. However, we still lack a thorough understanding of the capabilities of these models, in particular for lower-resourced languages. In this paper, we focus on Finnish and thoroughly evaluate the multilingual BERT model on a range of tasks, comparing it with a new Finnish BERT model trained from scratch. The new language-specific model is shown to systematically and clearly outperform the multilingual. While the multilingual model largely fails to reach the performance of previously proposed methods, the custom Finnish BERT model establishes new state-of-the-art results on all corpora for all reference tasks: part-of-speech tagging, named entity recognition, and dependency parsing. We release the model and all related resources created for this study with open licenses at this https URL .