Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Review on Summarizing Financial News Using Deep Learning

Investors make investment decisions depending on several factors such as fundamental analysis, technical analysis, and quantitative analysis. Another factor on which investors can make investment decisions is through sentiment analysis of news headlines, the sole purpose of this study. Natural Language Processing techniques are typically used to deal with such a large amount of data and get valuable information out of it. NLP algorithms convert raw text into numerical representations that machines can easily understand and interpret. This conversion can be done using various embedding techniques. In this research, embedding techniques used are BoW, TF-IDF, Word2Vec, BERT, GloVe, and FastText, and then fed to deep learning models such as RNN and LSTM. This work aims to evaluate these model's performance to choose the robust model in identifying the significant factors influencing the prediction. During this research, it was expected that Deep Leaming would be applied to get the desired results or achieve better accuracy than the state-of-the-art. The models are compared to check their outputs to know which one has performed better.
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation

With the popularity of 3D sensors in self-driving and other robotics applications, extensive research has focused on designing novel neural network architectures for accurate 3D point cloud completion. However, unlike point cloud classification and reconstruction, the role of adversarial samples in 3D point cloud completion has seldom been explored. In this work, we demonstrate that adversarial samples can benefit neural networks on 3D point cloud completion tasks. We propose a novel approach to craft adversarial samples that improve the performance of models on both clean and adversarial inputs. In contrast to the Projected Gradient Descent (PGD) attack, our method generates adversarial samples that keep the geometric features in clean samples and contain few outliers. In particular, we use minimum absolute curvature directions to constrain the adversarial perturbations for each input point. The gradient components in the minimum absolute curvature directions are taken as adversarial perturbations. In addition, we adopt attack strength accumulation and auxiliary Batch Normalization layers to speed up the training process and alleviate the distribution mismatch between clean and adversarial samples. Experimental results demonstrate that training with the adversarial samples crafted by our method under the geometric-aware constraint effectively enhances the performance of the Point Completion Network (PCN) on the ShapeNet dataset.
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation

Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This is not scalable to large crowds. We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations — Attribution, Contrastive Attribution, and Counterfactual Suggestions — to feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideations. We conducted formative and controlled user studies to understand the usage and usefulness of explanations to improve ideation diversity and quality. Users appreciated that explanation feedback helped focus their efforts and provided directions for improvement. This resulted in explanations improving diversity compared to no feedback or feedback with scores only. Hence, our approach opens opportunities for explainable AI towards scalable and rich feedback for iterative crowd ideation and creativity support tools.
Born-Infeld problem with general nonlinearity

In this paper, using variational methods, we look for non-trivial solutions for the following problem $$\begin{cases}-\text{div}\left(a(|\nabla u|^2)\nabla u\right)=g(u), & \text{ in }\mathbb{R}^N, N \ge 3, \\ u(x) \to 0, & \text{ as } |x| \to +\infty\end{cases}$$ under general assumptions on the continuous nonlinearity $g$. We assume only growth conditions of $g$ at $0$, however no growth conditions at infinity are imposed. If $a(s) = (1−s)^{−1/2}$ , we obtain the well-known Born-Infeld operator, but we are able to study also a general class of a such that $a(s) \to +\infty$ as $s \to 1^{-}$. We find a radial solution to the problem with finite energy.
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment

In this paper we revisit the 2014 NeurIPS experiment that examined inconsistency in conference peer review. We determine that 50\% of the variation in reviewer quality scores was subjective in origin. Further, with seven years passing since the experiment we find that for \emph{accepted} papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count. We trace the fate of rejected papers, recovering where these papers were eventually published. For these papers we find a correlation between quality scores and impact. We conclude that the reviewing process for the 2014 conference was good for identifying poor papers, but poor for identifying good papers. We give some suggestions for improving the reviewing process but also warn against removing the subjective element. Finally, we suggest that the real conclusion of the experiment is that the community should place less onus on the notion of `top-tier conference publications' when assessing the quality of individual researchers. For NeurIPS 2021, the PCs are repeating the experiment, as well as conducting new ones.
Something Old, Something New: Grammar-based CCG Parsing with Transformer Models

This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing approaches. The report also documents a 20-year research program, showing how NLP methods have evolved over this time. The staggering accuracy improvements provided by neural models for CCG parsing can be seen as a reflection of the improvements seen in NLP more generally. The report provides a minimal introduction to CCG and CCG parsing, with many pointers to the relevant literature. It then describes the CCG supertagging problem, and some recent work from Tian et al. (2020) which applies Transformer-based models to supertagging with great effect. I use this existing model to develop a CCG multitagger, which can serve as a front-end to an existing CCG parser. Simply using this new multitagger provides substantial gains in parsing accuracy. I then show how a Transformer-based model from the parsing literature can be combined with the grammar-based CCG parser, setting a new state-of-the-art for the CCGbank parsing task of almost 93% F-score for labelled dependencies, with complete sentence accuracies of over 50%.
Nyquist Sampling and Degrees of Freedom of Electromagnetic Fields

A signal space approach is presented to study the Nyquist sampling, number of degrees of freedom and reconstruction of an electromagnetic field under arbitrary scattering conditions. Conventional signal processing tools, such as the multidimensional sampling theorem and Fourier theory, are used to provide a linear system theoretic interpretation of electromagnetic wave propagation, thereby revealing the spatially bandlimited nature of electromagnetic fields. Their spatial bandwidth is dictated by the selectivity of the underlying scattering that allows establishing the Nyquist spatial sampling with a reduction of the number of fields' samples needed to be processed.
Optical to X-Ray Signatures of Dense Circumstellar Interaction in Core-collapse Supernovae

Progenitors of core-collapse supernovae (SNe) can shed significant mass to circumstellar material (CSM) in the months to years preceding core collapse. The ensuing SN explosion launches ejecta that may subsequently collide with this CSM, producing shocks that can power emissions across the electromagnetic spectrum. In this work we explore the thermal signatures of dense CSM interaction when the CSM density profile is truncated at some outer radius. CSM with optical depth >c/v (where v is the shock velocity) will produce primarily ∼blackbody optical/UV emission, whereas lower optical depth CSM will power bremsstrahlung X-ray emission. Focusing on the latter, we derive light curves and spectra of the resulting X-ray transients that include a detailed treatment of Comptonization. Due to strong photoelectric absorption, the X-ray light curve is dominated by the postinteraction phase that occurs after the shock reaches the CSM truncation radius. We treat this regime here for the first time. Using these results, we present the phase space of optical, UV, and X-ray transients as a function of CSM properties, and discuss detectability prospects. We find that ROSAT would not have been sensitive to CSM X-ray transients but that eROSITA is expected to detect many such events. Future wide-field UV missions such as the Ultraviolet Transient Astronomy Satellite will dramatically enhance sensitivity to large optical depth CSM configurations. Finally, we present a framework within which CSM properties may be directly inferred from observable features of X-ray transients. This can serve as an important tool for studying stellar mass loss using SN X-ray detections.
Towards a computational definition of the tresillo rhythm and its tracing in popular music

This paper discusses the use and popularity of a rhythm, which henceforth is referred to as"Tresillo rhythm". We first define and formalizes the Tresillo rhythm. Given a mathematical representation of the rhythm, it is then traced in the US Billboard Top 20 Charts of the last 20 years. To detect and determine the use of this rhythm in a song, we compute the similarity of a song with this rhythm. The calculated similarity, then indicates how similar the rhythm of a pop song is compared to the prior defined Tresillo rhythm. To assert and cross-validate the computer rhythm similarity, two different formalizations of the Tresillo rhythm have been compiled and several different approaches to calculated rhythm similarities have been tested and compared. This similarity measure is then used to do an empirical study on the usage of the Tresillo rhythm in the US Billboard Top 20 Charts of the past 20 years (1999-2019). Finally, we argue about some of the possible reasons for the observed trend.
On Net Energy Metering X: Optimal Prosumer Decisions, Social Welfare, and Cross-Subsidies

We introduce NEM X, an inclusive retail tariff model that captures features of existing net energy metering (NEM) policies. It is shown that the optimal prosumer decision under NEM X obeys by a two-threshold policy. The threshold policy yields three household consumption modes: (a) the net-consuming mode where the prosumer consumes more than its behind-the-meter distributed energy resource (DER) production when the DER production is below a predetermined lower threshold, (b) the net-producing mode where the prosumer consumes less than its DER production when the DER production is above a predetermined upper threshold, and (c) the net-zero energy mode where the prosumer’s consumption matches its DER generation when its DER production is between the lower and upper thresholds. Both the thresholds are obtained in closed-form and computed apriori. Next, we analyze the regulator’s rate-setting process that determines NEM X parameters such as retail/sell rates, fixed charges, and price differentials in time-of-use tariffs’ on and off-peak periods. A stochastic Ramsey pricing program that maximizes social welfare subject to the revenue break-even constraint for the regulated utility is formulated. Performance of several NEM X policies is evaluated using real and synthetic data to illuminate impacts of NEM policy designs on social welfare, cross-subsidies of prosumers by consumers, and payback time of DER investments that affect long-run DER adoptions.
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement learning (RL) is a promising approach for acquiring short-horizon skills autonomously. However, the focus of RL algorithms has largely been on the success of those individual skills, more so than learning and grounding a large repertoire of skills that can be sequenced to complete extended multi-stage tasks. The latter demands robustness and persistence, as errors in skills can compound over time, and may require the robot to have a number of primitive skills in its repertoire, rather than just one. To this end, we introduce EMBER, a model-based RL method for learning primitive skills that are suitable for completing long-horizon visuomotor tasks. EMBER learns and plans using a learned model, critic, and success classifier, where the success classifier serves both as a reward function for RL and as a grounding mechanism to continuously detect if the robot should retry a skill when unsuccessful or under perturbations. Further, the learned model is task-agnostic and trained using data from all skills, enabling the robot to efficiently learn a number of distinct primitives. These visuomotor primitive skills and their associated pre- and post-conditions can then be directly combined with off-the-shelf symbolic planners to complete long-horizon tasks. On a Franka Emika robot arm, we find that EMBER enables the robot to complete three long-horizon visuomotor tasks at 85% success rate, such as organizing an office desk, a file cabinet, and drawers, which require sequencing up to 12 skills, involve 14 unique learned primitives, and demand generalization to novel objects.
A Correlated Network Scale-Up Model: Finding the Connection Between Subpopulations

Abstract Aggregated Relational Data (ARD), formed from “How many X’s do you know? ” questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive as it does not require a sample from the target population and does not ask respondents to self-reveal their own status. This is helpful for studying hard-to-reach populations like female sex workers who may be hesitant to reveal their status. From December 2008 to February 2009, the Kiev International Institute of Sociology (KIIS) collected ARD from 10,866 respondents to estimate the size of HIV-related groups in Ukraine. To analyze this data, we propose a new ARD model which incorporates respondent and group covariates in a regression framework and includes a bias term that is correlated between groups. We also introduce a new scaling procedure using the correlation structure to further reduce biases. The resulting size estimates of those most-at-risk of HIV infection can improve the HIV response efficiency in Ukraine. Additionally, the proposed model allows us to better understand two network features without the full network data: (a) What characteristics affect who respondents know, and (b) How is knowing someone from one group related to knowing people from other groups. These features can allow researchers to better recruit marginalized individuals into the prevention and treatment programs. Our proposed model and several existing NSUM models are implemented in the networkscaleup R package. Supplementary materials for this article are available online.
Scenario generation for market risk models using generative neural networks

In this research study, we show how existing approaches of using generative adversarial networks (GANs) as economic scenario generators (ESG) can be extended to an entire internal market risk model—with enough risk factors to model the full band-width of investments for an insurance company and for a time horizon of one year, as required in Solvency 2. We demonstrate that the results of a GAN-based internal model are similar to regulatory-approved internal models in Europe. Therefore, GAN-based models can be seen as an alternative data-driven method for market risk modeling.
Sharp global convergence guarantees for iterative nonconvex optimization: A Gaussian process perspective

We consider a general class of regression models with normally distributed covariates, and the associated nonconvex problem of fitting these models from data. We develop a general recipe for analyzing the convergence of iterative algorithms for this task from a random initialization. In particular, provided each iteration can be written as the solution to a convex optimization problem satisfying some natural conditions, we leverage Gaussian comparison theorems to derive a deterministic sequence that provides sharp upper and lower bounds on the error of the algorithm with sample-splitting. Crucially, this deterministic sequence accurately captures both the convergence rate of the algorithm and the eventual error floor in the finite-sample regime, and is distinct from the commonly used"population"sequence that results from taking the infinite-sample limit. We apply our general framework to derive several concrete consequences for parameter estimation in popular statistical models including phase retrieval and mixtures of regressions. Provided the sample size scales near-linearly in the dimension, we show sharp global convergence rates for both higher-order algorithms based on alternating updates and first-order algorithms based on subgradient descent. These corollaries, in turn, yield multiple consequences, including: (a) Proof that higher-order algorithms can converge significantly faster than their first-order counterparts (and sometimes super-linearly), even if the two share the same population update and (b) Intricacies in super-linear convergence behavior for higher-order algorithms, which can be nonstandard (e.g., with exponent 3/2) and sensitive to the noise level in the problem. We complement these results with extensive numerical experiments, which show excellent agreement with our theoretical predictions.
Data-driven controllers and the need for perception systems in underwater manipulation

The underwater environment poses a complex problem for developing autonomous capabilities for Underwater Vehicle Manipulator Systems (UVMSs). The modeling of UVMSs is a complicated and costly process due to the highly nonlinear dynamics and the presence of unknown hydrodynamical effects. This is aggravated in tasks where the manipulation of objects is necessary, as this may not only introduce external disturbances that can lead to a fast degradation of the control system performance, but also requires the coordinating with a vision system for the correct grasping and operation of the object. In this article, we introduce a control strategy for UVMSs working with unknown payloads. The proposed control strategy is based on a data-driven optimal controller. We present a number of experimental results showing the benefits of the proposed strategy. Furthermore, we include a discussion regarding the visual perception requirements for the UVMS in order to achieve full autonomy in underwater manipulation tasks of unknown payloads.
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC

We propose a demonstration-efficient strategy to compress a computationally expensive Model Predictive Controller (MPC) into a more computationally efficient representation based on a deep neural network and Imitation Learning (IL). By generating a Robust Tube variant (RTMPC) of the MPC and leveraging properties from the tube, we introduce a data augmentation method that enables high demonstration-efficiency, capable of compensating the distribution shifts typically encountered in IL. Our approach opens the possibility of zero-shot transfer from a single demonstration collected in a nominal domain, such as a simulation or a robot in a lab/controlled environment, to a domain with bounded model errors/perturbations. Numerical and experimental evaluations performed on a trajectory tracking MPC for a multirotor show that our method outperforms strategies commonly employed in IL, such as DAgger and Domain Randomization, in terms of demonstration-efficiency and robustness to perturbations unseen during training.
Effective field theory of fluctuating wall in open systems: from a kink in Josephson junction to general domain wall

We investigate macroscopic behaviors of fluctuating domain walls in nonequilibrium open systems with the help of the effective field theory based on symmetry. Since the domain wall in open systems breaks the translational symmetry, there appears a gapless excitation identified as the Nambu-Goldstone (NG) mode, which shows the non-propagating diffusive behavior in contrast to those in closed systems. After demonstrating the presence of the diffusive NG mode in the (2+1)-dimensional dissipative Josephson junction, we provide a symmetry-based general analysis for open systems breaking the one-dimensional translational symmetry. A general effective Lagrangian is constructed based on the Schwinger-Keldysh formalism, which supports the presence of the gapless diffusion mode in the fluctuation spectrum in the thin wall regime. Besides, we also identify a term peculiar to the open system, which possibly leads to the instability in the thick-wall regime or the nonlinear Kardar-Parisi-Zhang coupling in the thin-wall regime although it is absent in the Josephson junction.
One Source, Two Targets: Challenges and Rewards of Dual Decoding

Machine translation is generally understood as generating one target text from an input source document. In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other. As we discuss, such a device serves several practical purposes, from multi-target machine translation to the generation of controlled variations of the target text. We present an analysis of possible implementations of dual decoding, and experiment with four applications. Viewing the problem from multiple angles allows us to better highlight the challenges of dual decoding and to also thoroughly analyze the benefits of generating matched, rather than independent, translations.
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations

Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, leading to large amounts of tracked data. However, much of that information is not salient for reflection and self-awareness. To tackle this burden for reflection, we created the SalienTrack framework, which aims to 1) identify salient tracking events, 2) select the salient details of those events, 3) explain why they are informative, and 4) present the details as manually elicited or automatically shown feedback. We implemented SalienTrack in the context of nutrition tracking. To do this, we first conducted a field study to collect photo-based mobile food tracking over 1-5 weeks. We then report how we used this data to train an explainable-AI model of salience. Finally, we created interfaces to present salient information and conducted a formative user study to gain insights about how SalienTrack could be integrated into an interface for reflection. Our key contributions are the SalienTrack framework, a demonstration of its implementation for semi-automated feedback in an important and challenging self-tracking context and a discussion of the broader uses of the framework.