Miracle-dz/UniFAR-FTUs huggingface dataset:
Split-kl and PAC-Bayes-split-kl Inequalities

We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality is particularly well-suited for ternary random variables, which naturally show up in a variety of problems, including analysis of excess losses in classification, analysis of weighted majority votes, and learning with abstention. We demonstrate that for ternary random variables the inequality is simultaneously competitive with the kl inequality, the Empirical Bernstein inequality, and the Unexpected Bernstein inequality, and in certain regimes outperforms all of them. It resolves an open question by Tolstikhin and Seldin [2013] and Mhammedi et al. [2019] on how to match simultaneously the combinatorial power of the kl inequality when the distribution happens to be close to binary and the power of Bernstein inequalities to exploit low variance when the probability mass is concentrated on the middle value. We also derive a PAC-Bayes-split-kl inequality and compare it with the PAC-Bayes-kl, PAC-Bayes-Empirical-Bennett, and PAC-Bayes-Unexpected-Bernstein inequalities in an analysis of excess losses and in an analysis of a weighted majority vote for several UCI datasets. Last but not least, our study provides the first direct comparison of the Empirical Bernstein and Unexpected Bernstein inequalities and their PAC-Bayes extensions.
Enriching a Fashion Knowledge Graph from Product Textual Descriptions

Knowledge Graphs offer a very useful and powerful structure for representing information, consequently, they have been adopted as the backbone for many applications in e-commerce scenarios. In this paper, we describe an application of existing techniques for enriching thelarge-scale Fashion Knowledge Graph (FKG) that we build at Farfetch. In particular, we apply techniques for named entity recognition (NER) and entity linking (EL) in order to extract and link rich metadata from product textual descriptions to entities in the FKG. Having a complete and enriched FKG as an e-commerce backbone can have a highly valuable impact on downstream applications such as search and recommendations. However, enriching a Knowledge Graph in the fashion domain has its own challenges. Data representation is different from a more generic KG, like Wikidata and Yago, as entities (e.g. product attributes) are too specific to the domain, and long textual descriptions are not readily available. Data itself is also scarce, as labelling datasets to train supervised models is a very laborious task. Even more, fashion products display a high variability and require an intricate ontology of attributes to link to. We use a transfer learning based approach to train an NER module on a small amount of manually labeled data, followed by an EL module that links the previously identified named entities to the appropriate entities within the FKG. Experiments using a pre-trained model show that it is possible to achieve 89.75% accuracy in NER even with a small manually labeled dataset. Moreover, the EL module, despite relying on simple rule-based or ML models (due to lack of training data), is able to link relevant attributes to products, thus automatically enriching the FKG.
Classification of (q, q)-Biprojective APN Functions

In this paper, we classify <inline-formula> <tex-math notation="LaTeX">$(q,q)$ </tex-math></inline-formula>-biprojective almost perfect nonlinear (APN) functions over <inline-formula> <tex-math notation="LaTeX">$\mathbb {L}\times \mathbb {L}$ </tex-math></inline-formula> under the natural left and right action of <inline-formula> <tex-math notation="LaTeX">$\mathop {\mathrm {GL}}\nolimits (2, \mathbb {L})$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$\mathbb {L}$ </tex-math></inline-formula> is a finite field of characteristic 2. This shows in particular that the only quadratic APN functions (up to <inline-formula> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula>-equivalence) over <inline-formula> <tex-math notation="LaTeX">$\mathbb {L}\times \mathbb {L}$ </tex-math></inline-formula> that satisfy the so-called subfield property are the Gold functions and the function <inline-formula> <tex-math notation="LaTeX">$\kappa: \mathbb {F}_{64} \to \mathbb {F}_{64}$ </tex-math></inline-formula> which is the only known APN function that is equivalent to a permutation over <inline-formula> <tex-math notation="LaTeX">$\mathbb {L}\times \mathbb {L}$ </tex-math></inline-formula> up to <inline-formula> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula>-equivalence as shown in Browning et al. (2010). Deciding whether there exist other quadratic APN functions <inline-formula> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula>-equivalent to permutations that satisfy subfield property or equivalently, generalizing <inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> to higher dimensions was an open problem listed for instance in Carlet (2015) as one of the interesting open problems on cryptographic functions.
Federated Learning under Distributed Concept Drift

Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for FL, because drifts arise staggered in time and space (across clients). To the best of our knowledge, this work is the first to explicitly study data heterogeneity in both dimensions. We first demonstrate that prior solutions to drift adaptation that use a single global model are ill-suited to staggered drifts, necessitating multiple-model solutions. We identify the problem of drift adaptation as a time-varying clustering problem, and we propose two new clustering algorithms for reacting to drifts based on local drift detection and hierarchical clustering. Empirical evaluation shows that our solutions achieve significantly higher accuracy than existing baselines, and are comparable to an idealized algorithm with oracle knowledge of the ground-truth clustering of clients to concepts at each time step.
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization

We study the problem of approximating stationary points of Lipschitz and smooth functions under $(\varepsilon,\delta)$-differential privacy (DP) in both the finite-sum and stochastic settings. A point $\widehat{w}$ is called an $\alpha$-stationary point of a function $F:\mathbb{R}^d\rightarrow\mathbb{R}$ if $\|\nabla F(\widehat{w})\|\leq \alpha$. We provide a new efficient algorithm that finds an $\tilde{O}\big(\big[\frac{\sqrt{d}}{n\varepsilon}\big]^{2/3}\big)$-stationary point in the finite-sum setting, where $n$ is the number of samples. This improves on the previous best rate of $\tilde{O}\big(\big[\frac{\sqrt{d}}{n\varepsilon}\big]^{1/2}\big)$. We also give a new construction that improves over the existing rates in the stochastic optimization setting, where the goal is to find approximate stationary points of the population risk. Our construction finds a $\tilde{O}\big(\frac{1}{n^{1/3}} + \big[\frac{\sqrt{d}}{n\varepsilon}\big]^{1/2}\big)$-stationary point of the population risk in time linear in $n$. Furthermore, under the additional assumption of convexity, we completely characterize the sample complexity of finding stationary points of the population risk (up to polylog factors) and show that the optimal rate on population stationarity is $\tilde \Theta\big(\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n\varepsilon}\big)$. Finally, we show that our methods can be used to provide dimension-independent rates of $O\big(\frac{1}{\sqrt{n}}+\min\big(\big[\frac{\sqrt{rank}}{n\varepsilon}\big]^{2/3},\frac{1}{(n\varepsilon)^{2/5}}\big)\big)$ on population stationarity for Generalized Linear Models (GLM), where $rank$ is the rank of the design matrix, which improves upon the previous best known rate.
Polyhedral approximation and uniformization for non-length surfaces

We prove that any metric surface (that is, metric space homeomorphic to a 2-manifold with boundary) with locally finite Hausdorff 2-measure is the Gromov-Hausdorff limit of polyhedral surfaces with controlled geometry. We use this result, together with the classical uniformization theorem, to prove that any metric surface homeomorphic to the 2-sphere with finite Hausdorff 2-measure admits a weakly quasiconformal parametrization by the Riemann sphere, answering a question of Rajala-Wenger. These results have been previously established by the authors under the assumption that the metric surface carries a length metric. As a corollary, we obtain new proofs of the uniformization theorems of Bonk-Kleiner for quasispheres and of Rajala for reciprocal surfaces. Another corollary is a simplification of the definition of a reciprocal surface, which answers a question of Rajala concerning minimal hypotheses under which a metric surface is quasiconformally equivalent to a Euclidean domain.
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization

Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization owing to their parameter-agnostic ability -- requiring no a priori knowledge about problem-specific parameters nor tuning of learning rates. However, when it comes to nonconvex minimax optimization, direct extensions of such adaptive optimizers without proper time-scale separation may fail to work in practice. We provide such an example proving that the simple combination of Gradient Descent Ascent (GDA) with adaptive stepsizes can diverge if the primal-dual stepsize ratio is not carefully chosen; hence, a fortiori, such adaptive extensions are not parameter-agnostic. To address the issue, we formally introduce a Nested Adaptive framework, NeAda for short, that carries an inner loop for adaptively maximizing the dual variable with controllable stopping criteria and an outer loop for adaptively minimizing the primal variable. Such mechanism can be equipped with off-the-shelf adaptive optimizers and automatically balance the progress in the primal and dual variables. Theoretically, for nonconvex-strongly-concave minimax problems, we show that NeAda can achieve the near-optimal $\tilde{O}(\epsilon^{-2})$ and $\tilde{O}(\epsilon^{-4})$ gradient complexities respectively in the deterministic and stochastic settings, without prior information on the problem's smoothness and strong concavity parameters. To the best of our knowledge, this is the first algorithm that simultaneously achieves near-optimal convergence rates and parameter-agnostic adaptation in the nonconvex minimax setting. Numerically, we further illustrate the robustness of the NeAda family with experiments on simple test functions and a real-world application.
PENGARUH PELATIHAN, PENGEMBANGAN DAN PENGALAMAN KERJA TERHADAP KOMPETENSI GURU

As one of the human resources investment efforts in the globalization era of science and technology, education has become a necessity. The fulfillment process of education and technology requires teachers to have a competency which is suitable with the determined standard. To achieve the goal, the school organization management has to choose experienced teachers. Moreover, it also gives training and development facilities in order to improve the teacher’s competency. The purpose of this research is to find out the partial, simultaneous, and dominant influence among training, development and working experience variables to the teachers’ competency at SMA Muhammadiyah 2 Surabaya. The result of research is F test shows 30.918 > 2.98 which means simultaneously there is strong significant influence among training, development and working experience variables to the competency. Partially, training and working experience variables have influence to the competency. The result also shows that working experience has dominant influence to the working competency. Keywords: Training, Developing, Working Experience, Competency
QoS‐aware IoT networks and protocols: A comprehensive survey

The vast number of smart cloud applications that communicate with the “things” over a variety of physical networks and communication protocols contribute to the rise of complexity in Internet of Things (IoT) systems. The diversity of end‐user requirements related to the volume of generated data, its variety, and the velocity of its transmission makes quality of service (QoS) provisioning even more crucial and challenging in IoT. This paper provides a comprehensive and up‐to‐date survey of QoS support in IoT networks and communication protocols. An analysis of the QoS‐aware IoT architectures, layer‐dependent QoS metrics, and network resource optimization methods utilized in IoT systems are given. The limitations of the current state‐of‐the‐art studies for efficient delivery of QoS metrics are discussed. The paper concludes with future research directions on end‐to‐end QoS provisioning in IoT.
Semi-Automated Identification of News Story Chains: A New Dataset and Entity-based Labeling Method

Automatically deciding if two or more news articles cover the same event—thereby building a story chain —is an important problem in news analytics, and knowledge about such story chains can be used, for example, in recommendation scenarios to suggest follow-up news articles. While content analysis on the level of individual news articles and on general news topics is well-studied, research on news story chains is still limited, partly due to the difficulty of manually labeling or automatically detecting story chains in larger collections of news articles. In this work, we present a novel (semi-)automated method based on clustering and Named Entity Recognition for creating a dataset for news story chains. An experimental analysis of our method shows that it is highly effective in correctly detecting unrelated stories and identifying candidates for related stories. Thus it helps to reduce manual labeling efforts by 80% without affecting the quality of the dataset. It can even improve the quality of the dataset as manual work is put into only the potentially relevant cases. As an additional result of our work, we publish a new dataset of Business Energy News which was created with the help of our method to foster research in this area.
DETERMINANTS OF DOMESTIC TOURIST EXPENDITURE IN BANYUWANGI SCIENTIFIC JOURNALS Written by: FEBRI SUCI ADICAYANA 125020107121003 INTERNATIONAL UNDERGRADUATE PROGRAM FACULTY OF ECONOMICS AND BUSINESS UNIVERSITY OF BRAWIJAYA MALANG

In period of global economic downturn that effect to declining productivities of many sector in Banyuwangi. Actually, tourism raise as one sector that show positive trend. Domestic tourist continuously coming and consuming tourism product become new income generated for economy of Banyuwangi. This study aims to know what are determinant of Tourist Expenditure in term of domestic tourist who have visited Banyuwangi. So, the study used online survey method to tourist who have traveled to Banyuwangi. There were 120 respondents who participated. The survey data were analyzed using Partial Least Square (PLS). The results indicate that in variable of economic constraint only income that influence to tourist expenditure. Besides, only prices of tourism product, tourism facilities, ease of access, and public transport are become representative of travel-related characteristics variable. Then, travel experience, motivation and tpurism lifestyle are reflective of psychographics characteristic variable. Travel-related and psychographic are higher effect to tourist expenditure. In the other hand, socio-economic characteristics variable is not effect to the tourist expenditure.
Experimental investigation of wave-driven pore-water pressure and wave attenuation in a sandy seabed

Wave–seabed interaction has become a big concern of coastal researchers and engineers in the past decades as it may largely contribute to the seabed instability and failure of marine foundations. A series of laboratory experiments are carried out in a wave flume to study the wave-driven pore-water pressure in a sandy seabed and the attenuation of wave height. Waves propagating over a sandy seabed lead to oscillatory excess pore-water pressures within the porous seabed. Amplitude of pore-water pressure within the seabed decreases toward the bottom. A phase lag of pore-water pressure is clearly observed, and it contributes to net upward pressure related to seabed instability. Height of the incident wave is reduced as part of wave energy is dissipated by bottom friction, and a maximum attenuation of the incident wave height is up to 7.23% in the experiments. The influences of wave period and height of the incident wave on pore-water pressure and wave attenuation are also analyzed and discussed.
TQM and market orientation in care home services

Purpose The purpose of this paper is to operationalize the concepts of total quality management (TQM) and market orientation (MO) and to analyze the relationship between them in a major economic sector of activity, namely, the social services sector. Design/methodology/approach The paper analyzes the aforementioned relationships by means of a quantitative study carried out with information from managers of 137 Spanish organizations in the field of social service provision and, more specifically, in nursing homes for the elderly. Structural equation models were used to contrast the relationships. Findings Evidence is provided for the multi-dimensional structure of TQM and MO. The results confirm that TQM contributes to a greater degree of MO in the organizations analyzed, since a significant positive relationship between a set of components of the first concept (namely, processes, leadership and people) and the second is found. Practical implications Managers need to take into account the fact that the application of a management system in nursing homes that is based on TQM dimensions favors the development of an MO culture. The staff are the key factor in orientating the residential home toward the market. Originality/value The research covers a gap in the literature since these relationships have not been studied empirically in depth in service sectors such as social services and nursing homes, despite great and growing economic importance of this sector.
An Implementation of a Compact Smart Resistive Sensor Based on a Microcontroller with an Internal ADC

In the paper a new implementation of a compact smart resistive sensor based on a microcontroller with internal ADCs is proposed and analysed. The solution is based only on a (already existing in the system) microcontroller and a simple sensor interface circuit working as a voltage divider consisting of a reference resistor and the resistive sensor connected in parallel with an interference suppression capacitor. The measurement method is based on stimulation of the sensor interface circuit by a single square voltage pulse and on sampling the resulting voltage on the resistive sensor. The proposed solution is illustrated by a complete application of the compact smart resistive sensor used for temperature measurements, based on a 8-bit microcontroller ATxmega32A4 with a 12-bit ADC and a Pt100 resistive sensor. The results of experimental research confirm that the compact smart resistive sensor has 1 oC resolution of temperature measurement for the whole range of changes of measured temperatures.
Recent Advancements in Poor Graft Function Following Hematopoietic Stem Cell Transplantation

Poor graft function (PGF) is a life-threatening complication that occurs after transplantation and has a poor prognosis. With the rapid development of haploidentical hematopoietic stem cell transplantation, the pathogenesis of PGF has become an important issue. Studies of the pathogenesis of PGF have resulted in some success in CD34+-selected stem cell boosting. Mesenchymal stem cells, N-acetyl-l-cysteine, and eltrombopag have also been investigated as therapeutic strategies for PGF. However, predicting and preventing PGF remains challenging. Here, we propose that the seed, soil, and insect theories of aplastic anemia also apply to PGF; CD34+ cells are compared to seeds; the bone marrow microenvironment to soil; and virus infection, iron overload, and donor-specific anti-human leukocyte antigen antibodies to insects. From this perspective, we summarize the available information on the common risk factors of PGF, focusing on its potential mechanism. In addition, the safety and efficacy of new strategies for treating PGF are discussed to provide a foundation for preventing and treating this complex clinical problem.
A Recombinant Acetylcholine Receptor α1 Subunit Extracellular Domain Is a Promising New Drug Candidate for Treatment Of Myasthenia Gravis

Background and Aims Myasthenia gravis (MG) is a T-cell dependent antibody-mediated autoimmune disease in which the nicotinic acetylcholine receptor (AChR) is the major autoantigen, comprising several T and B cell auto-epitopes. We hypothesized that an efficacious drug candidate for antigen-specific therapy in MG should comprise a broad range of these auto-epitopes and be administered in a noninflammatory and tolerogenic context. Methods We used a soluble mutated form of the extracellular domain of the α1 chain of the AChR (α1-ECDm), which represents the major portion of auto-epitopes involved in MG, and investigated, in a well-characterized rat model of experimental autoimmune myasthenia gravis (EAMG) whether its intravenous administration could safely and efficiently treat the autoimmune disease. Results We demonstrated that intravenous administration of α1-ECDm abrogates established EAMG, in a dose and time dependent manner, as assessed by clinical symptoms, body weight, and compound muscle action potential (CMAP) decrement. Importantly, the effect was more pronounced compared to drugs representing current standard of care for MG. The protein had a short plasma half-life, most of what could be recovered was sequestered in the liver, kidneys and spleen. Further, we did not observe any signs of toxicity or intolerability in animals treated with α1-ECDm. Conclusion We conclude that intravenous treatment with α1-ECDm is safe and effective in suppressing EAMG. α1-ECDm is in preclinical development as a promising new drug candidate for MG.