Miracle-dz/UniFAR-FTUs huggingface dataset:
Imbalance aware lithography hotspot detection: a deep learning approach

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.
Discriminative 3D Shape Modeling for Few-Shot Instance Segmentation

In this paper, we present a simple and efficient scheme for segmenting approximately convex 3D object instances in depth images in a few-shot setting via discriminatively modeling the 3D shape of the object using a neural network. Our key idea is to select pairs of 3D points on the depth image between which we compute surface geodesics. As the number of such geodesics is quadratic in the number of image pixels, we can create a large training set of geodesics using only very limited ground truth instance annotations. These annotations are used to create a binary label for each geodesic, which indicates whether or not that geodesic belongs entirely to one instance segment. A neural network is then trained to classify the geodesics using these labels. During inference, we create geodesics from selected seed points in the test depth image, then produce a convex hull of the points that are classified by the neural network as belonging to the same instance, thereby achieving instance segmentation. We present experiments applying our method to segmenting instances of food items in real-world depth images. Our results demonstrate promising performances compared to prior methods in accuracy and computational efficiency.
Discovering Infinite Number of Independent Balanced Forms of Some Typical Chemical Equations

This paper reports on a novel idea falling within the purview of chemical science and engineering. It deals with a novel procedure for the algebraic method of balancing chemical reaction equations. The basic principle and relevant procedure of the novel scheme are offered along with illustrative examples. The fact that the balanced form of a chemical equation is not always unique is illustrated by the novel procedure offered in the paper. It has been found from the present study that, for each of the chemical equations (not involving ions) with {(m + n) - s} > 1, and for each of those involving ions with {(m + n) - s} > 2, where m = total number of reactants, n = total number of products, and s = total number of different kinds of elements on each side of the chemical equation, there exists an infinite number of independent balanced forms for each of those typical chemical equations.
Learning about Women’s Competence: the Dynamic Response of Political Parties to Gender Quotas in South Korea∗

Although gender quotas in politics are one of the most common affirmative action policies worldwide, the merits of these policies remain an object of debate. Opponents of quotas are concerned with an equity-efficiency tradeoff – quotas lower the average quality of politicians. This paper explores this tradeoff by studying the effect of gender quotas on parties’ selection of candidates in South Korean municipal council elections. South Korea provides us with a rare opportunity to observe how highly male-dominated political parties react to gender quotas: (i) we can exploit the discontinuity in the intensity of the quota’s effect at given cut-offs of council size; (ii) gender quotas were implemented in only one of two independent election arms, leaving space for adjustment in the other arm. We find that political parties initially counteract the quota by putting forth fewer female candidates in the unaffected arm. However, this pattern gradually reverses over time. The evidence is consistent with efficiency gains from the quota. Parties initially selected a suboptimally low number of women due to biased beliefs regarding their ability, but they slowly revise their beliefs after exposure. ∗This research is supported by the STICERD Research Grant and a grant of the French National Research Agency (ANR), “Investissements d’Avenir” (LabEx Ecodec/ANR-11-LABX-0047). †Department of Economics, Stockholm University, and CEP (LSE); email: Jay-Euijung.Lee@su.se. ‡Department of Economics, Trinity College Dublin, TRiSS, and TIME; email: zanellam@tcd.ie.
Searches for dark matter self-annihilation signals from dwarf spheroidal galaxies and the Fornax galaxy cluster with imaging air Cherenkov telescopes

Many astronomical observations indicate that dark matter pervades the universe and dominates the formation and dynamics of cosmic structures. Weakly interacting massive particles (WIMPs) with masses in the GeV to TeV range form a popular class of dark matter candidates. WIMP self-annihilation may lead to the production of γ -rays in the very high energy regime above 100 GeV, which is observable with imaging air Cherenkov telescopes (IACTs). For this thesis, observations of dwarf spheroidal galaxies (dSph) and the Fornax galaxy cluster with the Cherenkov telescope systems H.E. S.S. , MAGIC and VERITAS were used to search for γ -ray signals of dark matter annihilations. The work consists of two parts: First, a likelihood-based statistical technique was introduced to combine published results of dSph observations with the different IACTs. The technique also accounts for uncertainties on the “J factors”, which quantify the dark matter content of the dwarf galaxies. Secondly, H.E. S.S. observations of the Fornax cluster were analyzed. In this case, a collection of dark matter halo models was used for the J factor computation. In addition, possible signal enhancements from halo substructures were considered. None of the searches yielded a significant γ -ray signal. Therefore, the results were used to place upper limits on the thermally averaged dark matter self-annihilation cross-section 〈σv〉. Different models for the final state of the annihilation process were considered. The cross-section limits range from 〈σv〉UL ∼ 10−19 cm3s−1 to 〈σv〉UL ∼ 10−25 cm3s−1 for dark matter particles masses between 100 GeV and 100 TeV. Some of the diverse model uncertainties causing this wide range of 〈σv〉UL values were analyzed.
Merariq Tradition of Sasak Tribe in Social Construction and National Law

_______________________ Abstract ____________________________________________________________ Generally, Sasak society still maintains a culture of eloping or kidnapping girls to be wives. The aims of this study were to analyze the existence of merariq marriage tradition as a social construction in Sasak Tribe and how merariq marriage tradition in Sasak tribal is reviewed on the implementation of Law No. 1 of 1974. To achieve the goal, this research uses qualitative methods with descriptive. To collect data, this tehniques are observation, document studies and interviews objectively describing the phenomenon. The 20 informants in this study are; eloping actors, indigenous leaders and environmental chiefs who are divided into 10 informants each in 2 locations. The findings showed that one of the normative teachings contained in the practice of merariq is a sense of pride in the identity of the individual and the identity of the female families is characterized by the desire that the girl married by a man which used the it escape process. The pride has a social contribution to the existence of the family in the middle of the local community. On the other hand,
Enlightened Dictators? Good Governance In Autocratic International Organizations

A large and growing number of international organizations (IOs) are made up and governed by illiberal or outright authoritarian regimes. Many of these authoritarian IOs (AIOs) formally adopt good governance mandates, linking goals like democracy promotion, anti-corruption policies and human rights to their broader mission. Why do AIOs adopt good governance mandates that appear to conflict with the norms and standards these regimes apply at home? We argue that AIOs adopt these standards when they face substantial pressure from inside or outside the IO to adopt them. Central to our argument is that not all aspects of good governance are inherently or equally threatening to autocratic regimes. They pursue strategies that minimize the threat by externalizing policy outside the membership and strategically defining the goals to avoid or enact. This allows autocratic governments to uptake good governance talk but lessen any deep commitment to the norms and sometimes even to use them strategically to project their own power outside of the organization. Using data on 48 regional IOs with primarily autocratic membership between 1945 and 2015, we demonstrate that AIOs facing pressure from external good governance promoters will adopt good governance mandates but strategically shape those mandates in their favor if they can form bargaining coalitions with like-minded governments. The findings have sobering implications for the future of good governance promotion through IOs.
Expectation-Maximization via Pretext-Invariant Representations

Contrastive learning methods have been widely adopted in numerous unsupervised and self-supervised visual representation learning methods. Such algorithms aim to maximize the cosine similarity between two positive samples while minimizing that of the negative samples. Recently, Grill et al. propose an algorithm, BYOL, to utilize only positive samples, completely giving up on negative ones, by introducing a Siamese-like asymmetric architecture. Although many recent state-of-the-art (SOTA) methods adopt the architecture, most of them simply introduce the additional neural network, the predictor, without much exploration of the asymmetrical architecture. In contrast, He et al. propose SimSiam, a simple Siamese architecture relying on the stop-gradient operation instead of the momentum encoder and describe the framework from the perspective of Expectation-Maximization. We argue that BYOL-like algorithms attain suboptimal performance due to representation inconsistency during training. In this work, we explain and propose a novel self-supervised objective, Expectation-Maximization via Pretext-Invariant Representations (EMPIR), which enhances Expectation-Maximization-based optimization in BYOL-like algorithms by enforcing augmentation invariance within a local region of k nearest neighbors, resulting in consistent representation learning. In other words, we propose Expectation-Maximization as a core task of asymmetric architectures. We show that it consistently outperforms other (SOTA) algorithms by a decent margin. We also demonstrate its transfer learning capabilities on downstream image recognition tasks.
User Grouping and Scheduling in OFDMA-NOMA for Industrial Communication: A Low Latency Throughput-aware Approach

Future wireless networks demand very high-speed and reliable connectivity to realize smart industrial and manufacturing systems. Furthermore, there has been a tremendous growth in the number of connected sensors and devices, requiring efficient utilization of radio resources. To achieve this, we propose a user grouping and scheduling framework for hybrid multiple access scheme that combines non-orthogonal multiple access (NOMA) with orthogonal frequency division multiple access (OFDMA). First, we analyse the impact of channel gain difference among a group of power domain NOMA users on the bit error rate (BER) performance and achieved throughput. Based on the analysis, we develop a throughput-aware user grouping scheme for multiple ultra reliable low latency communication (URLLC) users, each with its own demanded throughput, taking the channel gain differences into consideration. For low latency operation, we propose a scheduling method that aims to minimize the number of utilized time slots, for a given bandwidth. The proposed scheme is evaluated in terms of BER, achieved throughput, and fairness of the grouped users to indicate that it fulfils the demanded throughput of each user, while providing lower latency compared to OMA only scenario. It is shown that OFDMA-NOMA with the proposed scheme outperforms a previously proposed user pairing scheme for uplink NOMA.
Concentration of Measure Phenomenon and its Implications for Sample-based Planning Algorithms in Very-High Dimensional Configuration Spaces

In very high-dimensional $(\gg 10)$ spaces, a collection of points generated uniformly at random will concentrate very tightly about its expected value - defying intuition developed in low-dimensional spaces. This paper explores the implications of this for two major classes of sample-based robot motion planning algorithms: Rapidly Exploring Random Trees (RRTs) and Probabilistic Road Maps (PRMs). First we show that the graph vertices concentrate in a thin-shelled hyper-sphere, with almost none near the origin nor at the edges of the workspace. Next we examine how varying one of the algorithms' parameters - the maximum edge length- can dramatically alter the algorithms' complexity and the connectivity of the resulting graph. Finally, we explore how the position of the initial node, often placed arbitrarily, can impact the shape of the graph. While the contributions of this paper are largely theoretical, many robotic applications of practical interest have extremely high-dimensional configuration spaces including humanoids, swarms and soft (a.k.a. continuum) robotics.
Exploring Multimodal Gait Rehabilitation and Assistance through an Adaptable Robotic Platform

Lower-limb exoskeletons and smart walkers are robotic devices to assist patients in regaining their autonomy after a stroke. The integration of these devices enables gait rehabilitation and functional compensation, promoting natural over-ground walking. This article presents the Adaptable Robotic Platform for Gait Rehabilitation and Assistance (AGoRA V2 platform), which integrates the new AGoRA V2 Smart Walker and the AGoRA V2 unilateral lower-limb exoskeleton. It was evaluated with 14 healthy subjects using physiological and kinematic variables and a perception assessment. The study entailed four conditions: Without exoskeleton (WOE), With Exoskeleton (WE&T), With Walker (WW), and With Platform (WP). Results indicate a reduction in the muscle activity of the Rectus Femoris (18%) and Vastus Lateralis (15%), comparing WE&T and WP, as well as walking without any device (WOE) and using any robotic device (WE&T, WW, WP). Results suggest the importance of combining the exoskeleton with the robotic walker and the assistance of each device independently. Moreover, using the complete platform induces slower gait patterns than the walker, as the mean impulse force and linear velocity decrease by 42% and 44%, respectively. These results demonstrate that the platform contributes to safety, and improvements in gait parameters and muscular activity, indicating the system's potential to act as a modular device according to users' conditions and therapeutic goals.
Distributed Robust Control Framework for Adaptive Cruise Control Systems

A distributed robust adaptive control framework is proposed for an adaptive cruise control system. The proposed approach is designed based on the model reference adaptive control approach. A robust control term is employed to make the system robust to any bounded disturbances, and a concurrent learning framework is leveraged to ensure the convergence of estimated parameters. The main feature of the developed robust adaptive cruise controller is that it does not require the speed of the lead vehicle. It also considers uncertainties in both position and speed in the double integrator model. The string stability notion of the proposed approach is also investigated, and the performance of the control framework is evaluated in simulations in the presence of parametric uncertainties, disturbances, and noise.
Relations structure-fonction du premier transfert de brin chez le vih-1

Les premier et second transferts de brin sont deux etapes essentielles de la transcription inverse du genome du virus de l’immunodeficience humaine de type 1 (VIH-1). De nombreuses etudes in vitro suggerent que les transferts necessitent l’action de la proteine de nucleocapside du VIH-1 (NC). Le premier transfert, se produisant de l’extremite 5’ vers l’extremite 3’ de l’ARN genomique du VIH-1, repose en grande partie sur un appariement ADN-ARN impliquant la region r de l’ADN strong-stop (ADNss) et la region R situee a l’extremite 3’ de l’ARN viral (ARN 3’UTR). Les structures, interactions et dynamiques qui gouvernent cet appariement sont mal connues. Jusqu’a notre etude, la formation de l’hybride ADN-ARN n’avait ete etudiee in vitro qu’avec des courts acides nucleiques qui ne refletent que partiellement les structures formees par l’ADNss et l’ARN 3’UTR dans le virus en cours de replication. L’objectif principal de ma these a ete de caracteriser in vitro les mecanismes moleculaires qui gouvernent l’hybridation de l’ADNss avec l’ARN 3’UTR. Pour atteindre cet objectif, j’ai utilise des methodes de la biologie moleculaire et j’ai analyse la structure secondaire de l’ADNss entier avec trois sondes de structure (DNase I, mung bean nuclease et permanganate de potassium) : i) en l’absence de NC ; ii) en presence de NC ; iii) en presence de l’ARN 3’UTR.Les resultats obtenus nous ont permis d’etre les premiers a determiner in vitro la structure secondaire de l’ADNss entier du VIH-1 et a identifier dans celui-ci quatre sites sur lesquels se fixe preferentiellement la NC. A notre connaissance, les structures secondaires des ADNss d’autres retrovirus n’ont pas ete determinees. Nos donnees structurales sont en faveur d’une structure secondaire de l’ADNss du VIH-1 constituee de trois tiges-boucles (u5, cpoly(A) et cTAR) et trois regions simple-brin en l’absence ou presence de NC. Notre analyse phylogenetique suggere que la structure secondaire de l’ADNss et les sites forts NC sont conserves parmi les differents groupes du VIH-1. Nos resultats suggerent aussi qu’une partie de la region u5 de l’ADNss etablit des interactions tres faibles et probablement transitoires avec une partie de la region U3 de l’ARN 3’UTR en l’absence de NC. En realisant des cinetiques d’hybridation et en utilisant deux ADNss mutes, nous avons montre que l’hybridation ADNss-ARN 3’UTR necessite l’activite de la NC et que ce processus ne repose pas sur une seule voie d’initiation. Nos resultats supportent un modele dans lequel la premiere etape est la fixation de la NC au niveau des quatre sites, ce qui va declencher l’ouverture de la structure tridimensionnelle de l’ADNss et favoriser ainsi l’accessibilite de la region r ; la seconde etape etant la destabilisation par la NC des structures secondaires ; la troisieme etape etant l’appariement par la NC des regions complementaires r et R. L’ensemble des resultats obtenus permet a mon equipe d’accueil d’initier de nouvelles etudes in vitro et ex vivo.
Solar PV hosting capacity: grid-based vs. market-based scenarios

Assessing the capability of a distribution grid to accommodate new solar PV installations, namely its hosting capacity (HC), has been a prevalent research topic. Although providing a technical limit to how much additional solar PV can be integrated into a distribution grid without trespassing operational limits, commonly used HC analysis (HCA) does not consider consumer preferences or the economic feasibility of installations. Using a market-based optimal power flow (MBOPF) and HCA, we compare the economic and technical limits of solar PV capacity integration in low voltage distribution systems (LVDS). Findings illustrate that (1) the PV HC computed using grid limits only does not give a complete picture of solar PV capacity integration potential, (2) linear, deterministic power flow is not a foolproof method for assessing the network-secure amount of PV, and (3) the number of technically feasible installation sites supersedes the economically feasible ones.
Effective Deep Learning Approach to Denoise Optical Coherence Tomography Images Using BM3D-Based Preprocessing of the Training Data Including Both Healthy and Pathological Cases

Retinal diseases are significant cause of visual impairment globally. In the worst case they may lead to severe vision loss or blindness. Accurate diagnosis is a key factor in the right treatment planning that can stop or slow the disease. The examination that can aid in the right diagnosis is Optical Coherence Tomography (OCT). OCT scans are susceptible to various noise effects which deteriorate their quality and as a result may impede the analysis of their content. In this paper, we propose a novel and effective method for OCT image denoising using a deep learning model trained on pairs of noisy and clean scans obtained by BM3D filtering. A comprehensive dataset of 21926 OCT scans, collected from 869 patients (1639 eyes), covering both healthy and pathological cases, was used for training and testing of the proposed scheme. The method was validated taking into account quantitative metrics concerning image quality. In addition, the proposed denoising scheme was evaluated by analyzing the impact of applying it in the eye disease classification based on Convolutional Neural Networks (CNNs) where we obtained the improvement of around 1–3 pp (percentage point). A separate dataset of 25697 scans collected from 1910 patients (2953 eyes) was used for this purpose. The conducted experiments have proved that the method can be applied as a preprocessing step in order to provide better disease classification results and can be useful in other OCT image analysis tasks. The proposed solution is much faster and perform better than the classical BM3D filter (over ninetyfold speed-up) and other related methods, especially when a big set of images needs to be processed at once. Furthermore, the use of the diverse dataset show the benefit over methods which are based on using only healthy scans for the training of the neural network.