A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
EdgeNet improves Arabic numeral classification accuracy to 99.59%.
Paper tackles Arabic question similarity, outperforming state-of-the-art.
Paper evaluates Arabic question similarity, 9 teams participated.
ArSentD-LEV dataset improves sentiment analysis in Levantine Arabic tweets.
Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded …
A hybrid K-NN and SVM technique improves classification accuracy.
Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest prediction, we focus on filtering the vast volume of tweets to identify tweets relevant to unrest, which can be provided to downstream users for …
Improved neural model predicts gender from tweets.
The international community was caught by surprise on 5 June 2017 when Saudi Arabia, the United Arab Emirates (UAE), Bahrain and Egypt severed diplomatic ties with Qatar, accusing it of destabilizing the region. More than one year after this diplomatic rift, several questions remain unaddressed. This study focuses on t…
Unified BERT model improves NER across multiple languages.
Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs individual and independent pairwise alignment of feature trajectories. The modified technique, termed feature trajectory dynamic time warping (FTDTW), is a…
We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language mo…
Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the compl…
I present a unified discussion of several recently published results concerning the escalation, timing and severity of violent events in human conflicts and global terrorism, and set them in the wider context of real-world and cyber-based collective violence and illicit activity. I point out how the borders distinguish…
In this paper, we propose and develop the novel idea of treating musical sheets as literary documents in the traditional text analytics parlance, to fully benefit from the vast amount of research already existing in statistical text mining and topic modelling. We specifically introduce the idea of representing any give…
Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several researc…
This paper explores historical and philosophical aspects of angles and solid angles, inspired by Euler's work.
Macroeconomic theories of growth and wealth distribution have an outsized influence on national and international social and economic policies. Yet, due to a relative lack of reliable, system wide data, many such theories remain, at best, unvalidated and, at worst, misleading. In this paper, we introduce a novel econom…
The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic. We constructed an example of structured data, motivated by problems from image-to-text conversion (OCR), which req…
By drawing on ideas from optimisation theory, artificial neural networks (ANN), graph embeddings and sparse representations, I develop a novel technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at addressing the feature extraction problem. The proposed method uses (preferably deep) ANNs for projecting i…
High and volatile global food prices have led to food riots and played a critical role in triggering the Arab Spring revolutions in recent years. The severe drought in the US in the summer of 2012 led to a new increase in food prices. Through the fall, they remained at a threshold above which the riots and revolutions …
Probabilistic numerics expands numerical tasks with black box methods.
Study disproves a generalized numerical criterion for certain pairs.
New method combines ODE filters and numerical quadrature to propagate model uncertainty.
Efficient numerical method for time-fractional Black-Scholes model.
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation …
The paper solves complex swing option pricing equations with numerical methods.
Study identifies numerical signs of blow-up in hydrodynamic equations.
We develop the theory of smooth principal bundles for a smooth group , using the framework of diffeological spaces. After giving new examples showing why arbitrary principal bundles cannot be classified, we define -numerable bundles, the smooth analogs of numerable bundles from topology, and prove that pulling ba…
Introduces numerical Gaussian process Kalman filtering for infinite-dimensional systems.
FiNCAT tool automatically identifies financial numerals in documents.
Proves representability of complex semigroup systems.
Language models can predict numeric values as strings.
The paper analyzes numerical instability in variational flows and proposes a diagnostic method.
Study shows how numerical discretization affects reconstructions and parameter distributions in nano metrology.
Paper introduces NumLLM for better financial text understanding with numeric variables.
We give (1) an upper bound on the denominators of numerical boundary slopes and (2) an upper bound on the differences between two numerical boundary slopes, for Montesinos knot exteriors.
Improves E2E ASR performance on numeric sequences with additional training data and denormalization.
Parallel-in-time solver reduces ODE simulation time from linear to logarithmic.
We provide notions of numerical effectiveness and numerical flatness for Higgs vector bundles on compact Kähler manifolds in terms of fibre metrics. We prove several properties of bundles satisfying such conditions and in particular we show that numerically flat Higgs bundles have vanishing Chern classes, and that they…
A new framework improves tensor completion accuracy by considering numerical priors.
End-to-end solution for recognizing handwritten numerals, avoiding traditional preprocessing steps.
Paper approximates fractional harmonic maps with numerical methods.
LLMs struggle with arithmetic tasks unless they use high numerical precision.
This paper tackles Bayesian system identification with probabilistic numerical methods.
Develops a numerical method for LRM strategies in BNS models with infinite active jumps.
Deep learning solves high-dimensional PDEs efficiently.
In this Article, a fast numerical numerical algorithm for pricing discrete double barrier option is presented. According to Black-Scholes model, the price of option in each monitoring date can be evaluated by a recursive formula upon the heat equation solution. These recursive solutions are approximated by using Legend…