Miracle-dz/UniFAR-FTUs huggingface dataset:
Transition behaviour of weak turbulent fountains

Numerical simulations of fully turbulent weak fountain flow are used to provide direct evidence for the scaling behaviour of fountain flow over the Froude number range Fr = 0.1–2.1 and Reynolds number range Re = 20–3494. For very weak flow at Fr < 0.4, the flow mean penetration height, Zm, scales with Zm/R0 = A1Fr2/3 + A2Fr2/3 where R0 is the source radius. A1 and A2 are constants which quantify the separate effects of the radial acceleration of fountain fluid from the source (A1) and the backpressure from the surrounding intrusion, if present, on the upflow (A2). The evidence presented in this work suggests that the mechanisms for the two parts in the scaling of Zm scale with Fr2/3. The intrusion behaviour varies with the Reynolds number (Re) but there is no Re affect on the fountain penetration height. For Re < 250 the radial intrusion flow is subcritical and has different behaviour. For Fr between 0.4 and 2.1 the effect of source momentum flux increases and the flow structure changes to one where there is a coherent upflow and a cap region where the flow stagnates and then reverses. The two regions have separate scaling behaviour such that the overall height, through this transition range of Froude numbers, can be described by Zm/R=C1Fr2/3 + C2Fr2, where C1 and C2 are constants. Over this transition range the effect of source velocity profile is more significant than the Reynolds number effects and the effect of inlet turbulence is minor.
Knowing what Social Enterprises know

Social Enterprises (SEs) are normally micro and small businesses that trade to tackle social problems, and to improve communities, people’s life chances, and the environment. Thus, their importance to society and economies is increasing. However, there is still a need for more understanding of how these organisations operate, perform, innovate and scale-up. Obtaining this understanding is the main driver of this paper, which explores the SE activities to manage their knowledge. Interviews from 21 owners and senior members of SEs in UK confirmed that SEs possessed valuable tacit and explicit knowledge about their organisation, practices, experiences and work with communities and costumers. This knowledge is managed informally and, when acquired, is not always converted into usable knowledge, applied to create value, and protected from inappropriate or illegal use. Thus, SEs need to know what they know and manage effectively that knowledge. This can help them to acquire, convert, apply and protect all their knowledge that would result in added value to their organisation and stakeholders, legitimised their practice, adjusted and defined their operational and strategic direction, and informed the measurement of their social impact. This paper contributes to SE and Knowledge Management researchers, SE practitioners and organisations supporting SEs.
Learning Consumer Tastes Through Dynamic Assortments Canan

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
On the design of convolutional neural networks for automatic detection of Alzheimer's disease

Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural networks (CNNs) trained to detect AD using structural brain MRI scans. Specifically, we provide evidence that (1) instance normalization outperforms batch normalization, (2) early spatial downsampling negatively affects performance, (3) widening the model brings consistent gains while increasing the depth does not, and (4) incorporating age information yields moderate improvement. Together, these insights yield an increment of approximately 14% in test accuracy over existing models when distinguishing between patients with AD, mild cognitive impairment, and controls in the ADNI dataset. Similar performance is achieved on an independent dataset.
BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA

The BERT language model (LM) (Devlin et al., 2019) is surprisingly good at answering cloze-style questions about relational facts. Petroni et al. (2019) take this as evidence that BERT memorizes factual knowledge during pre-training. We take issue with this interpretation and argue that the performance of BERT is partly due to reasoning about (the surface form of) entity names, e.g., guessing that a person with an Italian-sounding name speaks Italian. More specifically, we show that BERT's precision drops dramatically when we filter certain easy-to-guess facts. As a remedy, we propose E-BERT, an extension of BERT that replaces entity mentions with symbolic entity embeddings. E-BERT outperforms both BERT and ERNIE (Zhang et al., 2019) on hard-to-guess queries. We take this as evidence that E-BERT is richer in factual knowledge, and we show two ways of ensembling BERT and E-BERT.
CamemBERT: a Tasty French Language Model

Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models –in all languages except English– very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks.
How to avoid mass transfer limitations in ozonation kinetics of phenylphenol isomers

Ozonation is a heterogeneous process of chemical absorption often controlled by a gas-liquid mass transfer rate. This paper presents the results of kinetics in a reaction between phenylphenol isomers and ozone. The degradation of phenylphenol isomers during ozonation proceeds quite fast. In order to avoid the influence of mass transfer limitation the kinetics experiments were conducted in a homogenous liquid-liquid system. The second-order rate constants were determined using classical and competition methods, which are especially recommended for fast reactions. The determined rate constants at pH 2 using the two different methods are almost the same. The increase of pH causes an increase of rate constants for the reaction of phenylphenol isomers with ozone.
Stronger Convergence Results for Deep Residual Networks: Network Width Scales Linearly with Training Data Size

Deep neural networks are highly expressive machine learning models with the ability to interpolate arbitrary datasets. Deep nets are typically optimized via first-order methods, and the optimization process crucially depends on the characteristics of the network as well as the dataset. This work sheds light on the relation between the network size and the properties of the dataset with an emphasis on deep residual networks (ResNets). Our contribution is that if the network Jacobian is full rank, gradient descent for the quadratic loss and smooth activation converges to the global minima even if the network width $m$ of the ResNet scales linearly with the sample size $n$ and logarithmically with the network depth $H$. Consequently, our work is able to provide a theoretical guarantee for the convergence of deep neural networks in the $m=\varOmega (n,\log H)$ regime.
Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.
Convergence to minima for the continuous version of Backtracking Gradient Descent

The main result of this paper is: {\bf Theorem. } Let $f:\mathbb{R}^k\rightarrow \mathbb{R}$ be a $C^{1}$ function, so that $\nabla f$ is locally Lipschitz continuous. Assume moreover that $f$ is $C^2$ near its generalised saddle points. Fix real numbers $\delta _0>0$ and $0<\alpha <1$. Then there is a smooth function $h:\mathbb{R}^k\rightarrow (0,\delta _0]$ so that the map $H:\mathbb{R}^k\rightarrow \mathbb{R}^k$ defined by $H(x)=x-h(x)\nabla f(x)$ has the following property: (i) For all $x\in \mathbb{R}^k$, we have $f(H(x)))-f(x)\leq -\alpha h(x)||\nabla f(x)||^2$. (ii) For every $x_0\in \mathbb{R}^k$, the sequence $x_{n+1}=H(x_n)$ either satisfies $\lim _{n\rightarrow\infty}||x_{n+1}-x_n||=0$ or $ \lim _{n\rightarrow\infty}||x_n||=\infty$. Each cluster point of $\{x_n\}$ is a critical point of $f$. If moreover $f$ has at most countably many critical points, then $\{x_n\}$ either converges to a critical point of $f$ or $\lim _{n\rightarrow\infty}||x_n||=\infty$. (iii) There is a set $\mathcal{E}_1\subset \mathbb{R}^k$ of Lebesgue measure $0$ so that for all $x_0\in \mathbb{R}^k\backslash \mathcal{E}_1$, the sequence $x_{n+1}=H(x_n)$, {\bf if converges}, cannot converge to a {\bf generalised} saddle point. (iv) There is a set $\mathcal{E}_2\subset \mathbb{R}^k$ of Lebesgue measure $0$ so that for all $x_0\in \mathbb{R}^k\backslash \mathcal{E}_2$, any cluster point of the sequence $x_{n+1}=H(x_n)$ is not a saddle point, and more generally cannot be an isolated generalised saddle point. Some other results are proven.
Generative Autoregressive Networks for 3D Dancing Move Synthesis From Music

This letter proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 1,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music.
On the design of multi-stage tournaments when some results are carried over.

The paper discusses the strategy-proofness of sports tournaments with multiple group stages. This design divides the competitors into round-robin groups in the preliminary and main rounds such that the results of matches played in the preliminary round against teams in the same main round group are carried over. It is shown that these tournaments, widely used in handball and other sports, are incentive incompatible because tanking can change the set of qualified teams. Historical examples are presented where a team was ex ante disinterested in winning by a high margin. We propose two incentive compatible mechanisms and compare them to the original format via simulations. Carrying over half of all points scored in the preliminary round turns out to be a promising policy.
On Unitary t-Designs from Relaxed Seeds

The capacity to randomly pick a unitary across the whole unitary group is a powerful tool across physics and quantum information. A unitary t-design is designed to tackle this challenge in an efficient way, yet constructions to date rely on heavy constraints. In particular, they are composed of ensembles of unitaries which, for technical reasons, must contain inverses and whose entries are algebraic. In this work, we reduce the requirements for generating an ε-approximate unitary t-design. To do so, we first construct a specific n-qubit random quantum circuit composed of a sequence of randomly chosen 2-qubit gates, chosen from a set of unitaries which is approximately universal on U(4), yet need not contain unitaries and their inverses nor are in general composed of unitaries whose entries are algebraic; dubbed relaxed seed. We then show that this relaxed seed, when used as a basis for our construction, gives rise to an ε-approximate unitary t-design efficiently, where the depth of our random circuit scales as poly(n,t,log(1/ε)), thereby overcoming the two requirements which limited previous constructions. We suspect the result found here is not optimal and can be improved; particularly because the number of gates in the relaxed seeds introduced here grows with n and t. We conjecture that constant sized seeds such as those which are usually present in the literature are sufficient.
Informal Institutions in Azerbaijan: Exploring the Intricacies of Tapsh

Abstract Research on informal aspects of the post-communist economy and political institutions has developed rapidly since the collapse of the Soviet Union. While there is no lack in research on informal practices in Russia and other Eastern European countries, comprehensive empirical investigations of informality in peripheral regions of the former Soviet Union—such as the Caucasus and Central Asia—are still rare. This article aims to fill this gap by providing an evidence-based empirical account of informal practices in post-Soviet Azerbaijan. Drawing its empirical data from a two-decade-long ethnographic participant observation carried out in various locales of Azerbaijan’s capital, Baku, this study offers nuanced insights into the hitherto unexplored informal practice of tapsh.
EarthquakeGen: Earthquake Simulation Using Generative Adversarial Networks

Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale data set. In recent years, automatic detection methods based on machine learning have been developed to improve accuracy and efficiency. However, the accuracy of those methods relies on a sufficient amount of high-quality training data, which itself can be expensive to obtain due to the requirement of domain knowledge and subject matter expertise. This paper is to resolve this dilemma by answering two questions: (1) provided with a limited number of reliable labels, can we use them to generate more synthetic labels; (2) Can we use those synthetic labels to improve the detectability? Among all the existing generative models, the generative adversarial network (GAN) shows its supreme capability in generating high-quality synthetic samples in multiple domains. We designed our model based on GAN. In particular, we studied several different network structures. By comparing the generated results, our GAN-based generative model yields the highest quality. We further combine the dataset with synthetic samples generated by our generative model and show that the detectability of our earthquake classification model is significantly improved than the one trained without augmenting the training set.
Additive Manufacturing Enabled by Electrospinning for Tougher Bio-Inspired Materials

Nature has taught us fascinating strategies to design materials such that they exhibit superior and novel properties. Shells of mantis club have protein fibres arranged in a 3D helicoidal architecture that give them remarkable strength and toughness, enabling them to absorb high-impact energy. This complex architecture is now possible to replicate with the recent advances in additive manufacturing. In this paper, we used melt electrospinning to fabricate 3D polycaprolactone (PCL) fibrous design to mimic the natural helicoidal structures found in the shells of the mantis shrimp’s dactyl club. To improve the tensile deformation behavior of the structures, the surface of each layer of the samples were treated with carboxyl and amino groups. The toughness of the surface-treated helicoidal sample was found to be two times higher than the surface-treated unidirectional sample and five times higher than the helicoidal sample without surface treatment. Free amino groups (NH2) were introduced on the surface of the fibres and membrane via surface treatment to increase the interaction and adhesion among the different layers of membranes. We believe that this represents a preliminary feasibility in our attempt to mimic the 3D helicoidal architectures at small scales, and we still have room to improve further using even smaller fibre sizes of the modeled architectures. These lightweight synthetic analogue materials enabled by electrospinning as an additive manufacturing methodology would potentially display superior structural properties and functionalities such as high strength and extreme toughness.
Improving Machine Reading Comprehension via Adversarial Training

Adversarial training (AT) as a regularization method has proved its effectiveness in various tasks, such as image classification and text classification. Though there are successful applications of AT in many tasks of natural language processing (NLP), the mechanism behind it is still unclear. In this paper, we aim to apply AT on machine reading comprehension (MRC) and study its effects from multiple perspectives. We experiment with three different kinds of RC tasks: span-based RC, span-based RC with unanswerable questions and multi-choice RC. The experimental results show that the proposed method can improve the performance significantly and universally on SQuAD1.1, SQuAD2.0 and RACE. With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial. We also find that AT helps little in defending against artificial adversarial examples, but AT helps the model to learn better on examples that contain more low-frequency words.
An Empirical Study of the Relation Between Network Architecture and Complexity

In this preregistration submission, we propose an empirical study of how networks handle changes in complexity of the data. We investigate the effect of network capacity on generalization performance in the face of increasing data complexity. For this, we measure the generalization error for an image classification task where the number of classes steadily increases. We compare a number of modern architectures at different scales in this setting. The methodology, setup, and hypotheses described in this proposal were evaluated by peer review before experiments were conducted.
Tensor Regression Using Low-rank and Sparse Tucker Decompositions

This paper studies a tensor-structured linear regression model with a scalar response variable and tensor-structured predictors, such that the regression parameters form a tensor of order $d$ (i.e., a $d$-fold multiway array) in $\mathbb{R}^{n_1 \times n_2 \times \cdots \times n_d}$. It focuses on the task of estimating the regression tensor from $m$ realizations of the response variable and the predictors where $m\ll n = \prod \nolimits_{i} n_i$. Despite the seeming ill-posedness of this problem, it can still be solved if the parameter tensor belongs to the space of sparse, low Tucker-rank tensors. Accordingly, the estimation procedure is posed as a non-convex optimization program over the space of sparse, low Tucker-rank tensors, and a tensor variant of projected gradient descent is proposed to solve the resulting non-convex problem. In addition, mathematical guarantees are provided that establish the proposed method linearly converges to an appropriate solution under a certain set of conditions. Further, an upper bound on sample complexity of tensor parameter estimation for the model under consideration is characterized for the special case when the individual (scalar) predictors independently draw values from a sub-Gaussian distribution. The sample complexity bound is shown to have a polylogarithmic dependence on $\bar{n} = \max \big\{n_i: i\in \{1,2,\ldots,d \} \big\}$ and, orderwise, it matches the bound one can obtain from a heuristic parameter counting argument. Finally, numerical experiments demonstrate the efficacy of the proposed tensor model and estimation method on a synthetic dataset and a collection of neuroimaging datasets pertaining to attention deficit hyperactivity disorder. Specifically, the proposed method exhibits better sample complexities on both synthetic and real datasets, demonstrating the usefulness of the model and the method in settings where $n \gg m$.
Zero-shot Entity Linking with Dense Entity Retrieval

We consider the zero-shot entity-linking challenge where each entity is defined by a short textual description, and the model must read these descriptions together with the mention context to make the final linking decisions. In this setting, retrieving entity candidates can be particularly challenging, since many of the common linking cues such as entity alias tables and link popularity are not available. In this paper, we introduce a simple and effective two-stage approach for zero-shot linking, based on fine-tuned BERT architectures. In the first stage, we do retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then examined more carefully with a cross-encoder, that concatenates the mention and entity text. Our approach achieves a nearly 6 point absolute gain on a recently introduced zero-shot entity linking benchmark, driven largely by improvements over previous IR-based candidate retrieval. We also show that it performs well in the non-zero-shot setting, obtaining the state-of-the-art result on TACKBP-2010. The code and pre-trained models are available at https://github.com/facebookresearch/BLINK.