Miracle-dz/UniFAR-FTUs huggingface dataset:
Patient Capital Outperformance :

Among high Active Share portfolios – whose holdings differ substantially from their benchmark – only those with patient investment strategies (with holding durations of over two years) on average outperform, over 2% per year. Funds trading frequently generally underperform, including those with high Active Share. Among patient funds, separating closet index from high Active Share funds matters, as low Active Share funds on average underperform even with patient strategies. Our results suggest that U.S. equity markets provide opportunities for longer‐term active managers, perhaps because of the limited arbitrage capital devoted to patient and active investment strategies.
Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating

POI recommendation is a key task in tourism information systems. However, in contrast to conventional point of interest (POI) recommender systems, the available data is extremely sparse; most tourist visit a few sightseeing spots once and most of these spots have no check-in data from new tourists. Most conventional systems rank sightseeing spots based on their popularity, reputations, and category-based similarities with users' preferences. They do not clarify what users can experience in these spots, which makes it difficult to meet diverse tourism needs. To this end, in this work, we propose a mechanism to recommend POIs to tourists. Our mechanism include two components: one is a probabilistic model that reveals the user behaviors in tourism; the other is a pseudo rating mechanism to handle the cold-start issue in POIs recommendations. We carried out extensive experiments with two datasets collected from Flickr. The experimental results demonstrate that our methods are superior to the state-of-the-art methods in both the recommendation performances (precision, recall and F-measure) and fairness. The experimental results also validate the robustness of the proposed methods, i.e., our methods can handle well the issue of data sparsity.
Scalable Pareto Front Approximation for Deep Multi-Objective Learning

Multi-objective optimization is important for various Deep Learning applications, however, no prior multi-objective method suits very deep networks. Existing approaches either require training a new network for every solution on the Pareto front or add a considerable overhead to the number of parameters by introducing hyper-networks conditioned on modifiable preferences. In this paper, we present a novel method that contextualizes the network directly on the preferences by adding them to the input space. In addition, we ensure a well-spread Pareto front by forcing the solutions to preserve a small angle to the preference vector. Through extensive experiments, we demonstrate that our Pareto fronts achieve state-of-the-art quality despite being computed significantly faster. Furthermore, we demonstrate the scalability as our method approximates the full Pareto front on the CelebA dataset with an EfficientNet network at a marginal training time overhead of 7% compared to a single-objective optimization. We make the code publicly available at https://github.com/ruchtem/cosmos.
SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs

Single-image generative adversarial networks learn from the internal distribution of a single training example to generate variations of it, removing the need of a large dataset. In this paper we introduce SpecSinGAN, an unconditional generative architecture that takes a single one-shot sound effect (e.g., a footstep; a character jump) and produces novel variations of it, as if they were different takes from the same recording session. We explore the use of multi-channel spectrograms to train the model on the various layers that comprise a single sound effect. A listening study comparing our model to real recordings and to digital signal processing procedural audio models in terms of sound plausibility and variation revealed that SpecSinGAN is more plausible and varied than the procedural audio models considered, when using multi-channel spectrograms. Sound examples can be found at the project website: https://www.adrianbarahonarios. com/specsingan/
Brittle interpretations: The Vulnerability of TCAV and Other Concept-based Explainability Tools to Adversarial Attack

Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning. A number of explainability techniques have been developed which use a set of examples to represent a human-interpretable concept in a model’s activations. In this work we show that these explainability methods can suffer the same vulnerability to adversarial attacks as the models they are meant to analyze. We demonstrate this phenomenon on two well-known concept-based approaches to the explainability of deep learning models: TCAV and faceted feature visualization. We show that by carefully perturbing the examples of the concept that is being investigated, we can radically change the output of the interpretability method, e.g. showing that stripes are not an important factor in identifying images of a zebra. Our work highlights the fact that in safety-critical applications, there is need for security around not only the machine learning pipeline but also the model interpretation process.
An Empirical Investigation of Multi-bridge Multilingual NMT models

In this paper, we present an extensive investigation of multi-bridge, many-to-many multilingual NMT models (MB-M2M) ie. , models trained on non-English language pairs in addition to English-centric language pairs. In addition to validating previous work which shows that MB-M2M models can overcome zeroshot translation problems, our analysis reveals the following results about multibridge models: (1) it is possible to extract a reasonable amount of parallel corpora between non-English languages for low-resource languages (2) with limited non-English centric data, MB-M2M models are competitive with or outperform pivot models, (3) MB-M2M models can outperform English-Any models and perform at par with Any-English models, so a single multilingual NMT system can serve all translation directions.
Noether charges: the link between empirical significance of symmetries and non-separability

A fundamental tenet of gauge theory is that physical quantities should be gaugeinvariant. This prompts the question: can gauge symmetries have physical significance? On one hand, the Noether theorems relate conserved charges to symmetries, endowing the latter with physical significance, though this significance is sometimes taken as indirect. But for theories in spatially finite and bounded regions, the standard Noether charges are not gauge-invariant. I here argue that gauge-variance of charges is tied to the nature of the non-locality within gauge theories. I will flesh out these links by providing a chain of (local) implications: ‘local conservation laws ’⇒ ‘conserved regional charges ’ ⇔ ‘non-separability ’ ⇔ ‘direct empirical significance of symmetries ’.
Fast Approximate Inference for Spatial Extreme Value Models

The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process on the GEV parameters. Inference for such hierarchical GEV models is typically carried out using Markov chain Monte Carlo (MCMC) methods. However, MCMC can be prohibitively slow and computationally intensive when the number of latent variables is moderate to large. In this paper, we develop a fast Bayesian inference method for spatial GEV models based on the Laplace approximation. Through simulation studies, we compare the speed and accuracy of our method to both MCMC and a more sophisticated but less ﬂexible Bayesian approximation. A case study in forecasting extreme wind speeds is presented.
Deprojection of external barred galaxies from photometry

The observations of external galaxies are projected to the 2D sky plane. Reconstructing the 3D intrinsic density distribution of a galaxy from the 2D image is challenging, especially for barred galaxies, but is a critical step for constructing galactic dynamical models. Here we present a method for deprojecting barred galaxies and we validate the method by testing against mock images created from an N-body simulation with a peanut-shaped bar. We decompose a galaxy image into a bulge (including a bar) and a disk. By subtracting the disk from the original image a barred bulge remains. We perform multi-Gaussian expansion (MGE) fit to each component, then we deproject them separately by considering the barred bulge is triaxial while the disk is axisymmetric. We restrict the barred bulge to be aligned in the disk plane and has a similar thickness to the disk in the outer regions. The 3D density distribution is thus constructed by combining the barred bulge and the disk. Our model can generally recover the 3D density distribution of disk and inner barred bulge regions, although not a perfect match to the peanut-shaped structure. By using the same initial conditions, we integrate the orbits in our model-inferred potential and the true potential by freezing the N-body simulation. We find that 85% of all these orbits have similar morphologies in these two potentials, and our model supports the orbits that generate a boxy/peanut-shaped structure and an elongated bar similar to these in the true potential.
MIMICause : Defining, identifying and predicting types of causal relationships between biomedical concepts from clinical notes

Understanding of causal narratives communicated in clinical notes can help make strides towards personalized healthcare. In this work, MIMICause, we propose annotation guidelines, develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts in clinical notes; communicated implicitly or explicitly, identiﬁed either in a single sentence or across multiple sentences. We annotate a total of 2714 de-identiﬁed examples sampled from the 2018 n2c2 shared task dataset and train four different language model based architectures. Annotation based on our guidelines achieved a high inter-annotator agreement i.e. Fleiss’ kappa ( κ ) score of 0.72 and our model for identiﬁcation of causal relation achieved a macro F1 score of 0.56 on test data. The high inter-annotator agreement for clinical text shows the quality of our annotation guidelines while the provided baseline F1 score sets the direction for future research towards understanding narratives in clinical texts.
Ethics lines and Machine learning: a design and simulation of an Association Rules Algorithm for exploiting the data

Data mining techniques offer great opportunities for developing ethics lines, tools for communication, participation and innovation whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate. Finally, I offer a simulated application of the Apriori algorithm, supplying it with synthetic data to find out its potential, strengths and limitations.
FlexiTerm: A more efficient implementation of flexible multi-word term recognition

Terms are linguistic signifiers of domain-specific concepts. Automated recognition of multi-word terms (MWT) in free text is a sequence labelling problem, which is commonly addressed using supervised machine learning methods. Their need for manual annotation of training data makes it difficult to port such methods across domains. FlexiTerm, on the other hand, is a fully unsupervised method for MWT recognition from domain-specific corpora. Originally implemented in Java as a proof of concept, it did not scale well, thus offering little practical value in the context of big data. In this paper, we describe its re-implementation in Python and compare the performance of these two implementations. The results demonstrated major improvements in terms of efficiency, which allow FlexiTerm to transition from the proof of concept to the production-grade application.
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence

This paper proposes a transformer over transformer framework, called Transformer$^2$, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learned from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformer$^2$ can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformer$^2$ manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.
LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing

Semantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in novel settings. In this work, we show that better systematic generalization can be achieved by producing the meaning representation (MR) directly as a graph and not as a sequence. To this end we propose LAGr, the L abeling A ligned Gr aphs algorithm that produces semantic parses by predicting node and edge labels for a complete multi-layer input-aligned graph. The strongly-supervised LAGr algorithm requires aligned graphs as inputs, whereas weakly-supervised LAGr infers alignments for originally unaligned target graphs using an approximate MAP inference procedure. On the COGS and CFQ compositional generalization benchmarks the strongly-and weakly-supervised LAGr algorithms achieve signiﬁcant improvements upon the baseline seq2seq parsers.
Control-oriented Modeling of Bend Propagation in an Octopus Arm

Bend propagation in an octopus arm refers to a stereotypical maneuver whereby an octopus pushes a bend (localized region of large curvature) from the base to the tip of the arm. Bend propagation arises from the complex interplay between mechanics of the flexible arm, forces generated by internal muscles, and environmental effects (buoyancy and drag) from the surrounding fluid. In part due to this complexity, much of prior modeling and analysis work has relied on the use of high dimensional computational models. The contribution of this paper is to present a control-oriented reduced order model based upon a novel parametrization of the curvature of the octopus arm. The parametrization is motivated by the experimental results. The reduced order model is related to and derived from a computational model which is also presented. The results from the two sets of models are compared using numerical simulations which is shown to lead to useful qualitative insights into bend propagation. A comparison between the reduced order model and experimental data is also reported.
Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-fidelity Feedback

In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle. In real-life, the feedbacks of such oracles are often noisy and available after some unknown delay that may depend on the computation time of the oracle. Additionally, if the exact evaluations are expensive but coarse approximations are available at a lower cost, the feedbacks can have multi-fidelity. In order to address this problem, we propose a generic extension of hierarchical optimistic tree search (HOO), called ProCrastinated Tree Search (PCTS), that flexibly accommodates a delay and noise-tolerant bandit algorithm. We provide a generic proof technique to quantify regret of PCTS under delayed, noisy, and multi-fidelity feedbacks. Specifically, we derive regret bounds of PCTS enabled with delayed-UCB1 (DUCB1) and delayed-UCB-V (DUCBV) algorithms. Given a horizon T, PCTS retains the regret bound of non-delayed HOO for expected delay of O(log T), and worsens by T^((1-α)/(d+2)) for expected delays of O(T^(1-α)) for α ∈ (0,1]. We experimentally validate on multiple synthetic functions and hyperparameter tuning problems that PCTS outperforms the state-of-the-art black-box optimization methods for feedbacks with different noise levels, delays, and fidelity.
The Neglected Sibling: Isotropic Gaussian Posterior for VAE

Deep generative models have been widely used in several areas of NLP, and various techniques have been proposed to augment them or address their training challenges. In this paper, we propose a simple modification to Variational Autoencoders (VAEs) by using an Isotropic Gaussian Posterior (IGP) that allows for better utilisation of their latent representation space. This model avoids the sub-optimal behavior of VAEs related to inactive dimensions in the representation space. We provide both theoretical analysis, and empirical evidence on various datasets and tasks that show IGP leads to consistent improvement on several quantitative and qualitative grounds, from downstream task performance and sample efficiency to robustness. Additionally, we give insights about the representational properties encouraged by IGP and also show that its gain generalises to image domain as well.
Nonzero Skyrmion Hall Effect in Topologically Trivial Structures

It is widely believed that the skyrmion Hall effect, often disruptive for device applications, vanishes for overall topologically trivial structures such as (synthetic) antiferromagnetic skyrmions and skyrmioniums due to a compensation of Magnus forces. In this manuscript, however, we report that in contrast to the case of spin-transfer torque driven skyrmion motion, this notion is generally false for spin-orbit torque driven objects. We show that the skyrmion Hall angle is directly related to their helicity and imposes an unexpected roadblock for developing faster and lower input racetrack memories based on spin-orbit torques.
A modified kinetic theory for frictional-collisional bedload transport valid from dense to dilute regime

Modelling sediment transport is still a challenging problem and is of major importance for the study of particulate geophysical flows. In this work, the modelling of sediment transport in the collisional regime is investigated with a focus on the continuum modelling of the granular flow. For this purpose, a frictional-collisional approach, combining a Coulomb model with the kinetic theory of granular gases, is developed. The methodology is based on a comparison with coupled fluid-discrete simulations, that classical kinetic theory model fails to reproduce. In order to improve the continuum model, the fluctuating energy balance is computed in the discrete simulations and systematically compared with the kinetic theory closure laws. Inter-particle friction is shown to affect the radial distribution function and to increase the energy dissipation, in agreement with previous observations in the literature. Due to saltating particles, whose motion can not be captured by the kinetic theory, departure from the viscosity and diffusivity laws are observed in the dilute part of the granular flow. Finally, the quadratic nature of the drag force is shown to increase the granular fluctuating energy dissipation. Based on these observations, modifications of the kinetic theory closure laws are proposed. The modified model reproduces perfectly the discrete simulations in the entire depth structure of the granular flow. These modifications are shown to impact the rheological properties of the flow and they make it possible to recover the μ(I) rheology in the dense regime.
Improved Lower Bounds for Strongly Separable Matrices and Related Combinatorial Structures

In nonadaptive group testing, the main research objective is to design an efficient algorithm to identify a set of up to <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> positive elements among <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> samples with as few tests as possible. Disjunct matrices and separable matrices are two classical combinatorial structures while one provides a more efficient decoding algorithm and the other needs fewer tests, i.e., larger rate. Recently, a notion of strongly separable matrix has been introduced, which has the same identifying ability as a disjunct matrix, but has larger rate. In this paper, we use a modified probabilistic method to improve the lower bounds for the rate of strongly separable matrices. Using this method, we also improve the lower bounds for some well-known combinatorial structures, including locally thin set families and cancellative set families.
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?

Equivariance has emerged as a desirable property of representations of objects subject to identity-preserving transformations that constitute a group, such as translations and rotations. However, the expressivity of a representation constrained by group equivariance is still not fully understood. We address this gap by providing a generalization of Cover's Function Counting Theorem that quantifies the number of linearly separable and group-invariant binary dichotomies that can be assigned to equivariant representations of objects. We find that the fraction of separable dichotomies is determined by the dimension of the space that is fixed by the group action. We show how this relation extends to operations such as convolutions, element-wise nonlinearities, and global and local pooling. While other operations do not change the fraction of separable dichotomies, local pooling decreases the fraction, despite being a highly nonlinear operation. Finally, we test our theory on intermediate representations of randomly initialized and fully trained convolutional neural networks and find perfect agreement.