Miracle-dz/UniFAR-FTUs huggingface dataset:
ON WEAKLY NONLINEAR DESCRIPTIONS OF NONLINEAR INTERNAL GRAVITY WAVES IN A ROTATING REFERENCE FRAME

We discuss model equations for the description of horizontally propagating waves in the interior of a density stratified ocean in a rotating reference frame (e.g a rotating planet). Using linear theory we outline the complications the inclusion of rotation introduces. These complications preclude the strict application of equations in the classical Korteweg-de Vries (KdV) hierarchy. In place of the well-studied KdV-type equations, the socalled Ostrovsky equation is often employed to describe waves in the presence of rotation. However, this equation lacks the mathematical structure of the KdV equation, and in particular is not fully integrable. We present numerical integrations, based on spectral methods, of the Ostrovsky equation which show that solitary wave-like solutions decay slowly by radiating energy to a tail of dispersive waves. We discuss the shortcomings of the Ostrovsky equation and propose an alternative model equation that captures the linear, dispersive wave behaviour exactly.
KURDISH STRUGGLE FOR DEMOCRACY AND GENDER EQUALITY IN SYRIA

The struggles of Kurdish women in Rojava Kurdistan (Northern Syria) became known to many during the brutal attacks of ISIS (Islamic State of Iraq and Syria) against the city of Kobane in northern Syria on September 15th, 2014. While Kurdish men and women were trying to defend the city from ISIS militiamen with limited ammunition and inadequate weapons compared to the sophisticated weapons in the hands of ISIS, Kurds worldwide took to the streets to be the voice for Rojava and Kobane. From the battle to defend Kobane onward, Western media and politicians noted the bravery of the Kurdish women that fought ISIS and its brutal treatment—including enslavement—of women. These troops under the command of Kurdish woman commander Rojda Felat defeated ISIS in Raqqa, the capital of the so-called ISIS in 2018. Kurdish women fighters joined all military operations from Kobane to Baghouz, ISIS's last stronghold, to defeat them. In the liberated territories, one of the most striking images has been that of women tearing and burning their black burqas (black attire that covers a woman’s body from head to toe) with joy. But Kurdish women's achievements are not limited to military operations. In this paper I will examine the Kurdish Women’s Movement as an example of women organizing in times of conflict, analyzing their effort to guarantee gender equality post-conflict.
Machine Learning Parallelism Could Be Adaptive, Composable and Automated

In recent years, the pace of innovations in the fields of machine learning (ML) has accelerated, researchers in SysML have created algorithms and systems that parallelizeML training over multiple devices or computational nodes. As ML models become more structurally complex, many systems have struggled to provide allround performance on a variety of models. Particularly, ML scale-up is usually underestimated in terms of the amount of knowledge and time required to map from an appropriate distribution strategy to the model. Applying parallel training systems tocomplex models adds nontrivial development overheads in addition to model prototyping, and often results in lower-than-expected performance. This thesis identifies and addresses research challenges in both usability and performance in parallel ML techniques and system implementations. The first part of this thesis presents a simple design principle, adaptive parallelism, that applies suitable parallelization techniques to model building blocks (e.g. layers) according to their specific ML properties. Following it, we derive a series of optimizations and implementations optimizing different aspects of ML parallelization. We examine them and show that they significantly boost the efficiency or scalability of ML training on clusters 2-10x in their applicable scenarios. Generalizing this methodology, this second part of this thesis formulates the ML parallelization as an end-to-end optimization problem, and seeks to solve it automatically, for two broad paradigms of ML parallelization tasks: single-node dynamicbatching and distributed ML parallelisms. We present principled representations to express the two classes of ML parallelisms, along with composable system architectures,Cavs and AutoDist, respectively. They enable rapid compositions of parallelization strategies for unseen models, improve parallelization performance, and simplify parallel ML programming. On top of them, the third part of this thesis presents an automatic parallelization framework, AutoSync, to automatically optimize synchronization strategies indata-parallel distributed training. AutoSync achieves high performance “out-of-thebox” – it navigates the space spanned by the proposed representation, and automaticallyidentifies synchronization strategies that report 1.2 - 1.6x speedups over existing hand-optimized systems, lowering the technical barrier of distributed ML and helping make it accessible to a larger community of users. Collectively, the set of techniques and systems developed in this thesis lead to the proof of the concept and the prototype implementation of an end-to-end compiler system for large-scale ML training on distributed environments.
Opportunity and challenges of eHealth and mHealth for patients and caregivers

behavioural change. The employment of spreadsheets, Smartphone/ tablet apps, and wireless monitors that log mood, food-intake, sleep, hydration level, and heart rate in the Quantified Self movement underlines the promises of mHealth technologies in communicating personal health knowledge and promoting health behaviours. In addition, a new class of patient-driven health care services is emerging to supplement and extend traditional health care delivery models and empower patient self-care [7]. Patient-driven healthcare can be characterized as having an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. The potential exists to both improve traditional health care systems and expand the concept of health care through new services.
Towards a national digital elevation model

It has become apparent that a national digital elevation model (DEM) of sufficient resolution and accuracy is required for South Africa, as national government departments are not able to meet their reporting mandates without it. The Chief Directorate: National Geo-Spatial Information (CD:NGI) has conducted a user needs survey to determine the exact requirements. A DEM Community of Practice (COP) spearheaded by CD:NGI and the Council for Scientific and Industrial Research (CSIR) has been tasked to analyse these needs, and to create an index of all existing DEMs in the country amongst other things. This paper summarises existing DEMs in South Africa including global, national and small area DEMs, and provides the resolution, accuracy currency and availability of each. The results of the user needs survey are presented. At the time of the survey, SRTM90m was the most popular DEM. The most common applications included 3D visualisation, environmental applications, remote sensing and hydrology. The majority of the respondents noted that they required a DEM with a resolution of 10 m or higher, and an accuracy of 2,5 m or better. This is not practical for a national mapping organisation, and further investigation needs to take place to better understand the user’s requirements.
A Substantial Discount on Ski Passes: A Synthetic Control Analysis

The synthetic control method serves as an appropriate and promising approach to do quantitative comparison research. However, the method is rarely used in the context of tourism. We fill this research gap by applying the method to the case of a Swiss mountain destination. Alpine tourist destinations have suffered from a declining demand in the last decade. Fewer tourist visit ski resorts. Consequently, the tourism industry in these destinations is under pressure to seek for solutions to face this falling demand. Saas-Fee, a Swiss mountain destination particularly hard hit by the decline of visitors has come up with an innovative offer: A substantial price discount on ski passes. In this present study, we aim to estimate the causal effect of the price discount in Saas-Fee. We adopt the synthetic control method to analyze the impact of the destinations’ policy on tourism demand. Further, we perform placebo tests on all units in our donor pool to evaluate the significance our results. Finally, we show how to assess the assumptions of the synthetic control method in the context of tourism.
OP-ICES190177 1..15

Pablo León *, Nina Bednar sek, Pam Walsham, Kathryn Cook, Susan E. Hartman, Deborah Wall-Palmer, Jennifer Hindson, Kevin Mackenzie, Lynda Webster, and Eileen Bresnan Marine Scotland Science, Marine Laboratory, 375 Victoria Road, Aberdeen AB11 9DB, UK Southern California Coastal Water Research Project, 3535 Harbor Blvd # 110, Costa Mesa, CA 92626, USA National Oceanography Centre, European Way, Southampton SO14 3ZH, UK Naturalis Biodiversity Centre, Leiden 2300 RA, The Netherlands Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK *Corresponding author: tel: þ 44 131 24 42844; e-mail: pablo.diaz@gov.scot.
The Comparison of Primary, Secondary and Tertiary Amine Ligands on Palladium (II) Complex Ion on Thermo-Physical, Chemical Reactivity, and Biological Properties: A DFT Study

Palladyum kenetlenme reaksiyonlari icin katalizor olarak dikkate alinir ve sanayide kullanisli bir metaldir. Termo-fiziksel, kimyasal reaktivite ve biyolojik etkilesim, kimya endustrisi, ilac endustrisi ve akademi de en cok dikkate alinan parametrelerdir. Farkli amin ligandlari iceren Pd (II) kompleks iyonu, calismanin teorik kisminda DFT yontemi kullanilarak incelenmistir. Serbest enerji, entropi, dipol moment, baglanma enerjisi, nukleer enerji, elektronik enerji, olusum isisi gibi termo-fiziksel parametreler, HOMO enerjisi, LUMO enerjisi, HOMO-LUMO enerji araligi, iyonlasma enerjisi, elektronegatiflik, sertlik, yumusaklik ve elektron ilgisi gibi reaktivite ozellikleri, yuk yogunlugu, yuzey alani, hacim logP, Polarlanabilirlik, refraktivite, molekuler kutle, PIC50 gibi biyolojik ozellikleri DFT methodu yardimiyla hesaplanmistir. L01, L02, L03 ve L04 icin HOMO-LUMO enerji araligi degerleri 10.78, 0.59, 0.50 ve 10.73; PIC50 degerleri sirasiyla -20.41, -8.46, -1.69 ve 1.83 tur. L02 ile L03 un, L02 ile L04 un kimyasal kararliliklari hemen hemen aynidir. QSAR calismasi molekullerin biyolojik aktiflikleri hakkinda bilgi saglar. Amin ligandlarini iceren dort Pd(II) kompleks iyonu L03 ve L04 icin guclu biyolojik aktiviteye sahiptir.
P o S ( C R F 2 0 1 0 ) 0 1 1 On the transparency of the universe for very high energy photons

The transparency of the universe for very high energy (VHE, E> 100 GeV) photons from sources at cosmological distances is limited due to pair-productio n with low energy photons of the extra galactic background light (EBL) in the optical to infrared b and. Here, we use all available energy spectra from VHE emitting active galactic nuclei to search f or signatures of non-standard physics. A combined statistical study using a new approach shows evid ence for a hardening of the intrinsic power laws at the transition from optical thin to optical thi ck regions. Additionally, we find a moderately significant p-value of the expected absorption e ffect.
Lindblad approximation and spin relaxation in quantum electrodynamics

This article is concerned with the time evolution of spin observables for generalized spin boson models. This applies in particular to a model of nuclear magnetic resonance, namely a 1 2 -spin particle in a constant external magnetic field and in interaction with the quantized electromagnetic field (photons). We derive a Lindblad (or GKLS) type approximation of the spin dynamics initially in a photon vacuum state together with a precise control of the error coming from this approximation. The error term is bounded by g 2 where g is the coupling constant of the spin–photon interaction. The point here is the uniformity in time t > 0 of this error control.
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses. A reinforcement learning based race balance network (RL-RBN) is proposed. We formulate the process of finding the optimal margins for non-Caucasians as a Markov decision process and employ deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function. Guided by the agent, the skewness of feature scatter between races can be reduced. Besides, we provide two ethnicity aware training datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be utilized to study racial bias from both data and algorithm aspects. Extensive experiments on RFW database show that RL-RBN successfully mitigates racial bias and learns more balanced performance for different races.
A Domain Adaptive Density Clustering Algorithm for Data With Varying Density Distribution

As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse cluster loss and cluster fragmentation. To address these problems, we propose a Domain-Adaptive Density Clustering (DADC) algorithm, which consists of three steps: domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble. For data with VDD features, clusters in sparse regions are often neglected by using uniform density peak thresholds, which results in the loss of sparse clusters. We define a domain-adaptive density measurement method based on $K$ K-Nearest Neighbors (KNN) to adaptively detect the density peaks of different density regions. We treat each data point and its KNN neighborhood as a subgroup to better reflect its density distribution in a domain view. In addition, for data with ED or MDDM features, a large number of density peaks with similar values can be identified, which results in cluster fragmentation. We propose a cluster center self-identification and cluster self-ensemble method to automatically extract the initial cluster centers and merge the fragmented clusters. Experimental results demonstrate that compared with other comparative algorithms, the proposed DADC algorithm can obtain more reasonable clustering results on data with VDD, ED and MDDM features. Benefitting from a few parameter requirement and non-iterative nature, DADC achieves low computational complexity and is suitable for large-scale data clustering.
Causality for Machine Learning

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.
Structure-preserving algorithms for multi-dimensional fractional Klein-Gordon-Schrödinger equation

This paper aims to construct structure-preserving numerical schemes for multi-dimensional space fractional Klein-Gordon-Schr\"{o}dinger equation, which are based on the newly developed partitioned averaged vector field methods. First, we derive an equivalent equation, and reformulate the equation as a canonical Hamiltonian system by virtue of the variational derivative of the functional with fractional Laplacian. Then, we develop a semi-discrete conservative scheme via using the Fourier pseudo-spectral method to discrete the equation in space direction. Further applying the partitioned averaged vector field methods on the temporal direction gives a class of fully-discrete schemes that can preserve the mass and energy exactly. Numerical examples are provided to confirm our theoretical analysis results at last.
SAL: Sign Agnostic Learning of Shapes From Raw Data

Recently, neural networks have been used as implicit representations for surface reconstruction, modelling, learning, and generation. So far, training neural networks to be implicit representations of surfaces required training data sampled from a ground-truth signed implicit functions such as signed distance or occupancy functions, which are notoriously hard to compute. In this paper we introduce Sign Agnostic Learning (SAL), a deep learning approach for learning implicit shape representations directly from raw, unsigned geometric data, such as point clouds and triangle soups. We have tested SAL on the challenging problem of surface reconstruction from an un-oriented point cloud, as well as end-to-end human shape space learning directly from raw scans dataset, and achieved state of the art reconstructions compared to current approaches. We believe SAL opens the door to many geometric deep learning applications with real-world data, alleviating the usual painstaking, often manual pre-process.
A Simple yet Effective Way for Improving the Performance of GANs

In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features. To alleviate this problem, this brief presents a simple but effective way that improves the performance of generative adversarial network (GAN) without imposing the training overhead or modifying the network architectures of existing methods. The proposed method employs a novel cascading rejection (CR) module for discriminator, which extracts multiple non-overlapped features in an iterative manner using the vector rejection operation. Since the extracted diverse features prevent the discriminator from concentrating on non-meaningful features, the discriminator can guide the generator effectively to produce the images that are more similar to the real images. In addition, since the proposed CR module requires only a few simple vector operations, it can be readily applied to existing frameworks with marginal training overheads. Quantitative evaluations on various datasets including CIFAR-10, CelebA, CelebA-HQ, LSUN, and tiny-ImageNet confirm that the proposed method significantly improves the performance of GAN and conditional GAN in terms of Frechet inception distance (FID) indicating the diversity and visual appearance of the generated images.
hauWE: Hausa Words Embedding for Natural Language Processing

Words embedding (distributed word vector representations) have become an essential component of many natural language processing (NLP) tasks such as machine translation, sentiment analysis, word analogy, named entity recognition and word similarity. Despite this, the only work that provides word vectors for Hausa language is that of [1] trained using fastText, consisting of only a few words vectors. This work presents words embedding models using Word2Vec’s Continuous Bag of Words (CBoW) and Skip Gram (SG) models. The models, hauWE (Hausa Words Embedding), are bigger and better than the only previous model, making them more useful in NLP tasks. To compare the models, they were used to predict the 10 most similar words to 30 randomly selected Hausa words. hauWE CBoW’s 88.7% and hauWE SG’s 79.3% prediction accuracy greatly outperformed [1]’s 22.3%.
CoverNet: Multimodal Behavior Prediction Using Trajectory Sets

We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse set of trajectories. The size of this set remains manageable due to the limited number of distinct actions that can be taken over a reasonable prediction horizon. We structure the trajectory set to a) ensure a desired level of coverage of the state space, and b) eliminate physically impossible trajectories. By dynamically generating trajectory sets based on the agent's current state, we can further improve our method's efficiency. We demonstrate our approach on public, real world self-driving datasets, and show that it outperforms state-of-the-art methods.
The Convex Information Bottleneck Lagrangian

The information bottleneck (IB) problem tackles the issue of obtaining relevant compressed representations T of some random variable X for the task of predicting Y. It is defined as a constrained optimization problem that maximizes the information the representation has about the task, I(T;Y), while ensuring that a certain level of compression r is achieved (i.e., I(X;T)≤r). For practical reasons, the problem is usually solved by maximizing the IB Lagrangian (i.e., LIB(T;β)=I(T;Y)−βI(X;T)) for many values of β∈[0,1]. Then, the curve of maximal I(T;Y) for a given I(X;T) is drawn and a representation with the desired predictability and compression is selected. It is known when Y is a deterministic function of X, the IB curve cannot be explored and another Lagrangian has been proposed to tackle this problem: the squared IB Lagrangian: Lsq−IB(T;βsq)=I(T;Y)−βsqI(X;T)2. In this paper, we (i) present a general family of Lagrangians which allow for the exploration of the IB curve in all scenarios; (ii) provide the exact one-to-one mapping between the Lagrange multiplier and the desired compression rate r for known IB curve shapes; and (iii) show we can approximately obtain a specific compression level with the convex IB Lagrangian for both known and unknown IB curve shapes. This eliminates the burden of solving the optimization problem for many values of the Lagrange multiplier. That is, we prove that we can solve the original constrained problem with a single optimization.
Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue

Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary. Sino-Korean words, which can be converted into corresponding Chinese characters, account for more than fifty of the entire Korean vocabulary. Motivated by this, we propose a simple linguistically motivated solution to improve the performance of the Korean-to-Chinese neural machine translation model by using their common vocabulary. We adopt Chinese characters as a translation pivot by converting Sino-Korean words in Korean sentences to Chinese characters and then train the machine translation model with the converted Korean sentences as source sentences. The experimental results on Korean-to-Chinese translation demonstrate that the models with the proposed method improve translation quality up to 1.5 BLEU points in comparison to the baseline models.