Reviewerly/exHarmony huggingface dataset:
Tight Bounds for Quantum State Certification with Incoherent Measurements

We consider the problem of quantum state certification, where we are given the description of a mixed state $\sigma \in \mathbb{C}^{d \times d}$, $n$ copies of a mixed state $\rho \in \mathbb{C}^{d \times d}$, and $\varepsilon > 0$, and we are asked to determine whether $\rho = \sigma$ or whether $\| \rho - \sigma \|_1 > \varepsilon$. When $\sigma$ is the maximally mixed state $\frac{1}{d} I_d$, this is known as mixedness testing. We focus on algorithms which use incoherent measurements, i.e. which only measure one copy of $\rho$ at a time. Unlike those that use entangled, multi-copy measurements, these can be implemented without persistent quantum memory and thus represent a large class of protocols that can be run on current or near-term devices. For mixedness testing, there is a folklore algorithm which uses incoherent measurements and only needs $O(d^{3/2} / \varepsilon^2)$ copies. The algorithm is non-adaptive, that is, its measurements are fixed ahead of time, and is known to be optimal for non-adaptive algorithms. However, when the algorithm can make arbitrary incoherent measurements, the best known lower bound is only $\Omega (d^{4/3} / \varepsilon^2)$ [Bubeck-Chen-Li '20], and it has been an outstanding open problem to close this polynomial gap. In this work, 1) we settle the copy complexity of mixedness testing with incoherent measurements and show that $\Omega (d^{3/2} / \varepsilon^2)$ copies are necessary, and 2) we show the instance-optimal bounds for state certification to general $\sigma$ first derived by [Chen-Li-O'Donnell '21] for non-adaptive measurements also hold for arbitrary incoherent measurements. Qualitatively, our results say that adaptivity does not help at all for these problems. Our results are based on new techniques that allow us to reduce the problem to understanding certain matrix martingales, which we believe may be of independent interest.
Towards Metrical Reconstruction of Human Faces

Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely on a metrically correct prediction of the shape, especially, when the reconstructed subject is put into a metrical context (i.e., when there is a reference object of known size). A metrical reconstruction is also needed for any application that measures distances and dimensions of the subject (e.g., to virtually fit a glasses frame). State-of-the-art methods for face reconstruction from a single image are trained on large 2D image datasets in a self-supervised fashion. However, due to the nature of a perspective projection they are not able to reconstruct the actual face dimensions, and even predicting the average human face outperforms some of these methods in a metrical sense. To learn the actual shape of a face, we argue for a supervised training scheme. Since there exists no large-scale 3D dataset for this task, we annotated and unified small- and medium-scale databases. The resulting unified dataset is still a medium-scale dataset with more than 2k identities and training purely on it would lead to overfitting. To this end, we take advantage of a face recognition network pretrained on a large-scale 2D image dataset, which provides distinct features for different faces and is robust to expression, illumination, and camera changes. Using these features, we train our face shape estimator in a supervised fashion, inheriting the robustness and generalization of the face recognition network. Our method, which we call MICA (MetrIC fAce), outperforms the state-of-the-art reconstruction methods by a large margin, both on current non-metric benchmarks as well as on our metric benchmarks (15% and 24% lower average error on NoW, respectively).
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts

The incorporation of cutting planes within the branch-and-bound algorithm, known as branch-and-cut, forms the backbone of modern integer programming solvers. These solvers are the foremost method for solving discrete optimization problems and thus have a vast array of applications in machine learning, operations research, and many other fields. Choosing cutting planes effectively is a major research topic in the theory and practice of integer programming. We conduct a novel structural analysis of branch-and-cut that pins down how every step of the algorithm is affected by changes in the parameters defining the cutting planes added to the input integer program. Our main application of this analysis is to derive sample complexity guarantees for using machine learning to determine which cutting planes to apply during branch-and-cut. These guarantees apply to infinite families of cutting planes, such as the family of Gomory mixed integer cuts, which are responsible for the main breakthrough speedups of integer programming solvers. We exploit geometric and combinatorial structure of branch-and-cut in our analysis, which provides a key missing piece for the recent generalization theory of branch-and-cut.
Kesesuaian computed tomography scan-base scoring system dengan hasil sitologi cairan pleura dalam membedakan efusi pleura maligna dan benigna

Abstract: The research aims at assessing the congruity CT scan-base scoring system according to Porcel and the pleural fluid cytology result in distinguishing the malignant from benign pleural effusions. The research used the diagnostic test by assessing the congruity CT scan-base scoring system according to Porcel with pleural fluid cytology result in distinguishing the malignant from benign pleural effusions through the Chi-square test and assessing the sensitivity and specificity of the pleural effusion score. The research result indicates that there are 71 samples with the pleural effusion undergoing the examinations of the chest MSCT scan and pleural fluid cytology in General Central Hospital dr. Wahidin Sudirohusodo, Makassar, from September 2019 to August 2020, in which there are 26 samples with the malignant pleural effusion and 45 samples with benign pleural effusion. There is significant correlation between CT scan-base scoring system according to Porcel and the pleural fluid cytology result, in which the score &lt; 7 indicates the benign pleural effusion, while the score ≥ 7 indicates the malignant pleural effusion, with the sensitivity of 88.4% (73.1 – 88.5 %) and specificity of 75.5% (73.1 – 88.5 %). The positive predictive value 67.6%, negative predictive value is 91.8%.Keywords: pleural effusion; malignant pleural effusion; CT scan-base scoring system; pleural metastasisAbstrak: Penelitian ini bertujuan untuk menilai kesesuaian CT scan-base scoring system menurut Porcel dengan hasil sitologi cairan pleura dalam membedakan efusi pleura maligna dan benigna. Penelitian ini merupakan uji diagnostic, dengan menilai kesesuaian CT scan base scoring system menurut Porcel dengan hasil sitology cairan pleura dalam membedakan EPM dan EPB melalui Chi Sqaretest serta menilai sensitivitas dan spesifisitas dari skor efusi pleura. Hasil penelitian menunjukan terdapat 71 sampel dengan efusi pleura yang menjalani pemeriksaan MSCT-scan thorax dan sitology cairan pleura di RSUP dr. Wahidin Sudirohusodo, Makassar, periode September 2019 sampai Agustus 2020 yang masuk dalam penelitian kami, dimana terdapat 26 sampel EPM dan 45 sampel EPB. Terdapat hubungan bermakna antara CT scan base scoring system menurut Porcel dengan hasil sitology cairan pleura, dengan skor &lt; 7, yang menunjukan EPB, sedangkan skor ≥ 7 yang menunjukan EPM, dengan sensitivitas 88.4% (73.1 – 88.5 %) dan spesifisitas 75.5% (73.1 – 88.5 %); nilai prediksi positif 67.6%; nilai prediksi negatif 91.8%.Kata kunci: efusi pleura; efusi pleura maligna; CT scan base scoring system; metastasis pleura.
Uncertainty of the image of consumer behavioral disorders in the representations of students

Our time is called the era of consumption. Consumerism as a phenomenon is rapidly developing in the space of Russia and is one of the psychological characteristics of modern youth, including students. That is why this work was devoted to the study of the uncertainties of the image of violations of consumer behavior in students perceptions. To achieve the goal of the study, an overview of studies on the characteristics of consumer behavior in modern Russian humanitarian publications over the past five years is presented. The most relevant aspects of cognition of the psychology of consumer behavior are revealed: phenomenology, definition of the concept, deviations in consumer behavior, etc. Consumerism was found to lead to a disease affluence. The aim of the study was to identify students perceptions of consumer behavior disorders. It was assumed that the students ideas about violations of consumer behavior are characterized by blurry and uncertainty due to the fact that this aspect is not the subject of their professional training. 72 students aged 20 to 24 years of the Pedagogical University were engaged in a diagnostic conversation. The data obtained during the survey were interpreted using the Wu Xing pentagram method. The analysis of students ideas about psychological disorders of consumer behavior is given. The anti-motives of consumption are revealed. They were modeled into a reflected pyramid of needs on the basis of A. Maslows theory of needs. In the conclusion, it is indicated that the hypothesis put forward has not been confirmed.
Macro-level Inference in Collaborative Learning

With increasing data collection, also efforts to extract the underlying knowledge increase. Among these, collaborative learning efforts become more important, where multiple organisations want to jointly learn a common predictive model, e.g. to detect anomalies or learn how to improve a production process. Instead of learning only from their own data, a collaborative approach enables the participants to learn a more generalising model, also capable to predict settings not yet encountered by their own organisation, but some of the others. However, in many cases, the participants would not want to directly share and disclose their data, for regulatory reasons, or because the data constitute a business asset. Approaches such as federated learning allow to train a collaborative model without exposing the data itself. However, federated learning still requires exchanging intermediate models from each participant. Information that can be inferred from these models is thus a concern. Threats to individual data points and defences have been studied e.g. in membership inference attacks. However, we argue that in many use cases, also global properties are of interest -- not only to outsiders, but specifically also to the other participants, which might be competitors. In a production process, e.g. knowing which types of steps a company performs frequently, or obtaining information on quantities of a specific product or material a company processes, could reveal business secrets, without needing to know details of individual data points.
Enhancing low cost camera laparoscopic system based on embedded systems

A laparoscopic camera is a device used to view internal organs in the abdomen when a laparoscopic camera is inserted through a small incision in the abdomen.We use an embedded system to develop a low-cost laparoscopic camera and refine and evaluate the whole system. Small size production using various parameters to evaluate the usefulness of a portable, low-cost, camera-less laparoscopic simulator for training laparoscopy in abdominal surgery and the inexpensively of hospital environment constraints. Characterization of Raspberry Pi Embedded Compact system with perfect camera and image processing system. Design and compact, low-cost laparoscopic camera system and performance evaluation by hospital staff.
PENGEMBANGAN MEDIA BIG BOOK PADA MATERI KONDUKTOR SISWA KELAS V SEKOLAH DASAR

This research is a research and development that aims to develop the big book medium of conductor material in primary school with the 4D model (define, design, development and dissemination) proposed by Thiagarajan in 1974. This research uses descriptive quantitative method. The subjects of this study were 6 students from a public primary school in Pekanbaru. Research and development involves media experts as validators of the developed media. The data collection technique in this study was in the form of a validity and practicality questionnaire. The data analysis technique used in this study is to calculate the average percentage score seen from the aspects of content feasibility, presentation feasibility, language assessment, practicality of teacher and student responses. The results obtained from the media validity test show that the big book media conductor material is in the "very valid" category with a percentage score of 89.92%. While the results obtained from the practical test of teacher and student responses show that the big book media conductor material is "very practical" with a percentage score of 96.56%.
Exploiting complex pattern features for interactive pattern mining

Recent years have seen a shift from a pattern mining process that has users define constraints before-hand, and sift through the results afterwards, to an interactive one. This new framework depends on exploiting user feedback to learn a quality function for patterns. Existing approaches have a weakness in that they use static pre-defined low-level features, and attempt to learn independent weights representing their importance to the user. As an alternative, we propose to work with more complex features that are derived directly from the pattern ranking imposed by the user. Learned weights are then aggregated onto lower-level features and help to drive the quality function in the right direction. We explore the effect of different parameter choices experimentally and find that using higher-complexity features leads to the selection of patterns that are better aligned with a hidden quality function while not adding significantly to the run times of the method. Getting good user feedback requires to quickly present diverse patterns, something that we achieve but pushing an existing diversity constraint into the sampling component of the interactive mining system LetSip. Resulting patterns allow in most cases to converge to a good solution more quickly. Combining the two improvements, finally, leads to an algorithm showing clear advantages over the existing state-of-the-art.
Political Communities on Twitter: Case Study of the 2022 French Presidential Election

With the significant increase in users on social media platforms, a new means of political campaigning has appeared. Twitter and Facebook are now notable campaigning tools during elections. Indeed, the candidates and their parties now take to the internet to interact and spread their ideas. In this paper, we aim to identify political communities formed on Twitter during the 2022 French presidential election and analyze each respective community. We create a large-scale Twitter dataset containing 1.2 million users and 62.6 million tweets that mention keywords relevant to the election. We perform community detection on a retweet graph of users and propose an in-depth analysis of the stance of each community. Finally, we attempt to detect offensive tweets and automatic bots, comparing across communities in order to gain insight into each candidate's supporter demographics and online campaign strategy.
A learner-centred approach to writing education aimed at developing soft skills and a vision for a writing feedback system during the pandemic, crisis of public health

This study discusses the need to cultivate soft skills to avoid being marginalized in a rapidly changing data explosion era and implements new teaching and learning methods that meet digital natives' needs. It investigates the instructional effectiveness of writing education that embodies the innovation of the online writing centers through the case study of the online writing feedback system for university students in Korea. The online writing center was first launched to cope with the situation of COVID-19, but many universities in Korea are trying to make it an official center for new education after the pandemic. This paper utilized the Students Satisfaction Survey, which surveyed the students who participated in online writing counseling programs on the second semester 2020 to determine students' needs and satisfaction and analyzed their soft skills-focused on writing grading rubric used by operating online writing centers. The results were as follows. Writing centers should provide learner-centered and personalized counseling on writing and interpretation rather than mechanical correction to develop soft skills. Writing counseling can deliver mentoring, which is lacking in current university education. Therefore, non-face-to-face learning from online writing centers for soft skills development is suggested.
Solving multiobjective functions of dynamics optimization based on constraint and unconstraint non-linear programming

To better understand the connection between nonlinear optimization problems and differential equations, this study uses the mathematical models with one or more objective functions that are constrained by restrictions and therefore take the form of differential equations called dynamical optimization systems, which mean a decision-making method that uses differential and algebraic equation mathematical models to design-wise policies based on forecasts of future events. This paper focuses on solving mathematical models systems using the Python programming language.
Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system

Corona virus disease (COVID-19) have become a world health problem because they have attacked many people worldwide. Because this virus has spread massively in almost all countries, including Indonesia, the Indonesian government made some policies and rules to close down the hotels and restaurants to avoid the spread of COVID-19. Because of that, estimation of the number of closed down restaurants and hotels in Jakarta is vital for avoiding COVID-19 spreads further to other people, either domestic or foreign. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is chosen as the estimation method. In estimating the number of closed restaurants and hotels using ANFIS, supporting variables such as the amount of casualties in Jakarta, the amount of casualties in Indonesia, and the amount of casualties in the world is required. As a result, ANFIS can estimate the amount of closed down restaurants and hotels approaching the target. The simulations are organized by partitioning the dataset into two parts: data of (80%) and data of testing (20%). According to ANFIS simulations, ANFIS can estimate the number of closed down restaurants and hotels in training data with optimal RMSE equals 0.5324 and testing data with optimal RMSE equals 5.3198.
On Validating Attack Trees with Attack Effects: An Approach from Barwise-Seligman's Channel Theory

In security analysis, attack trees are a major tool for showing the structural decomposition of attacks and for supporting the evaluation of the quantitative properties (called attributes) of the attacks. However, the validities of decompositions are not established by attack trees themselves, and fallacious decisions about security may be made when the attack trees are inaccurate. This paper enriches attack trees with effects of attacks, with a formal system focusing on refinement scenarios. Relationships among effects indicate relationships among attacks and it allows for a systematic evaluation of attack decompositions. To describe effects this paper applies Barwise-Seligman's channel theory. Infomorphisms, in particular, play a significant role to connect effects with distinct granularities. As a result, the consistency of a decomposition is formally defined and a condition for it is stated. This framework is applied to a case study of a vehicular network system. As an application of the idea of consistency, possible degrees of mitigation for attacks in attack trees are discussed.
Multi-Way Noiseless Signal Amplification in a Symmetrical Cascaded Four-Wave Mixing Process

According to the fundamental laws of quantum optics, vacuum noise is inevitably added to the signal when one tries to amplify a signal. However, it has been recently shown that noiseless signal amplification can be realized when a phase-sensitive process is involved. Two phase-sensitive schemes, a correlation injection scheme and a two-beam phase-sensitive amplifier scheme, are both proposed to realize multi-way noiseless signal amplification in a symmetrical cascaded four-wave mixing process. We theoretically study the possibility of the realization of four-way noiseless signal amplification by using these two schemes. The results show that the correlation injection scheme can only realize one-way noiseless signal amplification, but that the two-beam phase-sensitive amplifier scheme can lead to four-way noise figure values below 1. Our results here may find potential applications in quantum information processing, e.g., the realization of quantum information tap and quantum non-demolition measurement, etc.
Swarm optimization based texture classification in extreme scale variations

Texture analysis has remained a remarkably trendsetting and productive field of research in the last two decades. There has been much progress, but the impact of illumination changes on automated texture classification and segmentation has gained very less focus. Research work carried out in the field of texture identification frequently focuses on the identification of textures with intraclass changes including illumination, rotation, viewpoint and small scale variations. Consequently, variations in texture owing to modifications in scale constitute among the ones that are difficult to manage. In this research work, as the first step, the classification of textures due to vast changes in scale is studied. In order to deal with this problem, first the solution is introduced and then the scale changes are reduced based on the predominant patterns in the texture. Inspired by the challenges imposed by this issue, a novel swarm intelligence approach known as Ant Colony Optimization (ACO) algorithm is introduced for modifying the components in the hidden layers used during the network training, for the extraction of more useful semantic texture patterns.
CORRELATION BETWEEN PARENT’S EDUCATIONAL BACKGROUND WITH STUDENT ENGLISH ACHIEVEMENT

Correlation between parent’s educational background and student English achievement at the first-year student of SMA Muhammadiyah Sawangan. Department of English Education, The Faculty of Tarbiyah and Teachers Training, Syarif Hidayatullah State Islamic University Jakarta. The object of this study is to describe any correlation between parent education background and student English achievement. In conducting this research, the researchers gave a test and questionnaires, and the data were reached through the Quantitative method. The quantitative method was obtained by analyzing the test and questionnaires result. The finding of the research show that is no correlation between parent’s educational background towards student’s English achievement