Custom NLP system extracts clinical data for breast cancer analysis.
problem Manual extraction of information from text-based medical records is tedious and requires specialized knowledge.
method Combines standard text mining techniques with advanced synonym detection for global analysis.
result Achieved good extraction accuracy for various concepts of interest without requiring existing corpora or ontologies.
SAFER method certifies robustness to word substitutions without model structure.
problem Certified robustness against synonymous word substitutions in NLP models.
method Randomized smoothing with stochastic ensemble of randomized inputs.
result Significantly outperforms state-of-the-art methods for certified robustness.
Isothermic parameterizations} are synonyms of isothermal curvature line parameterizations, for surfaces immersed in Euclidean spaces. We provide a method of constructing isothermic coordinate charts on surfaces which admit them, starting from an arbitrary chart. One of the primary applications of this work consists of …
Improves text clustering by incorporating sequential features and word embeddings.
problem Lack of sequential information and synonym handling in current text clustering methods.
method SiDPMM model that models documents as joint of bags of words, sequential features, and word embeddings.
result Significant improvement in performance and accurate inference of cluster numbers.
This work improves neural network robustness to symbol substitutions using formal verification.
problem Neural networks' vulnerability to adversarial attacks, especially under discrete text perturbations.
method Formal verification using Interval Bound Propagation on a simplex model of input perturbations.
result Models show improved verified accuracy under perturbations with formal guarantees.
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction. In this work, we continue…
Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation technique for statistical machine translation to make it more adjustable and robust. We intend to adapt it to resemble human evaluation more. We perform experiments to evaluate the performance of our technique against the primary existing evalua…
As we show using the notion of equilibrium in the theory of infinite sequential games, bubbles and escalations are rational for economic and environmental agents, who believe in an infinite world. This goes against a vision of a self regulating, wise and pacific economy in equilibrium. In other words, in this context, …
We lay down an elementary yet fundamental lemma concerning a finite algebraicness property of a smooth map from an Azumaya/matrix manifold with a fundamental module to a smooth manifold. This gives us a starting point to build a synthetic (synonymously, C∞-algebraic) symplectic geometry and calibrated geometr…
Proposes BU-SPO method to improve text classification robustness.
problem Vulnerability of deep models in text classification.
method Bigram and unigram based adaptive Semantic Preservation Optimization (BU-SPO) method.
result Achieves highest attack success rates and semantic similarity by changing the smallest number of words.
We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nua…
The paper analyzes word embeddings and their failure to distinguish polarized terms.
problem Word embeddings fail to correctly distinguish terms with opposite polarities.
method Mathematical analysis of word2vec model, synthetic corpus generation, empirical assessment.
result Word embeddings treat antonyms as frequentist synonyms, leading to mixed polarity terms.
Project classifies Hinglish social content on platforms like Twitter, Reddit.
problem Classifying abusive and hate-inducing content in Hinglish on social media.
method Used deep learning with bi-directional sequence models and text augmentation techniques.
result Produced a state-of-the-art classifier that outperforms previous work.
Develops techniques to align word embeddings from different sources.
problem Aligning word embeddings from diverse datasets or methods.
method Simple closed-form techniques for optimal rotation, translation, and scaling.
result Maximizes cosine similarity and minimizes root mean squared errors.
This paper introduces the distinction between aleatoric and epistemic uncertainty in machine learning.
problem The need to distinguish between types of uncertainty in machine learning.
method Introduction and overview of existing methods.
result The distinction between aleatoric and epistemic uncertainty.
Paper improves relation extraction in clinical texts with limited data.
problem Relation extraction in narrow knowledge domains with scarce annotated data.
method Introduces a bag-of-concepts (BoC) model and compares it with window-bounded co-occurrence (WBC).
result BoC model outperforms baseline and other complex methods on small dataset.
Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic f…
Sparse PCA selects variables with FDR control for improved performance.
problem Sparse PCA selects irrelevant variables when maximizing explained variance.
method Proposes FDR-controlled selection using T-Rex selector.
result Significant performance improvement over traditional sparse PCA.
New method improves consistency in preference learning for neural networks.
problem Inconsistent surrogate losses in preference learning for neural networks.
method Formulated a margin-shifted ranking framework and introduced Structure-Aware H-consistency. result Proved superior consistency guarantees for capacity-bounded models using heavy-tailed surrogates.
New analysis shows low volatility can be unstable in financial markets.
problem Understanding the relationship between volatility and market stability.
method Using mean first hitting time as a stability indicator and comparing to standard volatility measures.
result Low volatility can be associated with higher instability in financial markets.
This paper tackles rare word problem in low-resource language pairs using NMT.
problem Rare word problem in neural machine translation, especially for low-resource languages.
method Three solutions: enhanced source context, morphology learning, and wordnet synonyms.
result Significant improvements in BLEU scores (+1.0 points) on English-Vietnamese and Japanese-Vietnamese.
The study predicts clinical significance of BRCA1 and BRCA2 nsSNPs using neural networks.
problem Predicting clinical significance of BRCA1 and BRCA2 nsSNPs with unknown clinical significance.
method Hydrophobicity/hydrophilicity scores, Probabilistic Neural Network (PNN), and Deep Neural Network-Stacked AutoEncoder (DNN).
result The methods achieve high prediction accuracy (87.97% for BRCA1, 82.17% for BRCA2 using PNN, and 95.41% for BRCA1, 92.80% for BRCA2 using DNN).
Hyperbolic embeddings reduce dimensions for hierarchical data with high precision.
problem Embedding hierarchical data structures like synonym or type hierarchies efficiently.
method Combinatorial construction and hyperbolic multidimensional scaling (h-MDS) for metric spaces.
result Hyperbolic embeddings achieve high precision with few dimensions, e.g., 0.989 MAP with only 2 dimensions on WordNet.
Proposes a method for imputing missing data and estimating class uncertainties.
problem Missing data and uncertainties in target class assignments.
method Generative imputation and stochastic prediction using generator, discriminator, and predictor networks.
result Effectiveness in generating imputations and estimating class uncertainties.
New study shows tradeoffs between compression quality, distortion, and perception.
problem Optimizing compression for low distortion often sacrifices perceptual quality.
method Adopted Blau & Michaeli's perceptual quality definition and studied the rate-distortion-perception tradeoff.
result Restricting perceptual quality to high generally requires a trade-off between rate and distortion.
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publica…
This paper explores how LLMs can improve pipeline-based conversational agents.
problem Limitations of pipeline-based conversational agents in human-like conversations.
method Investigated LLMs' capabilities in two phases: design and development, and operations.
result LLMs can enhance pipeline-based agents in various tasks like data generation, intent classification, and auto-correction.
In this Part II of D(11), we introduce new objects: super-Ck-schemes and Azumaya super-Ck-manifolds with a fundamental module (or, synonymously, matrix super-Ck-manifolds with a fundamental module), and extend the study in D(11.1) ([L-Y3], arXiv:1406.0929 [math.DG]) to define the notion of `differentiable maps…
The paper assesses text classification robustness through maximal safe radius computation.
problem Vulnerability of neural network models to small input modifications.
method Maximal safe radius computation, Monte Carlo Tree Search, syntactic filtering, linear bounding techniques.
result Approximation methods for computing upper and lower bounds of maximal safe radius.
Study classifies Persian speech acts for better understanding of text intent.
problem Understanding the intended function of Persian texts.
method Dictionary-based statistical technique using WordNet for SA recognition.
result Proposed method achieved state-of-the-art accuracy of 0.95 for Persian SA classification.
ExCIR provides efficient, consistent, and scalable explainability for complex models.
problem Complex models lack transparency and require efficient, stable, and scalable explainability methods.
method ExCIR uses correlation-aware feature attribution with robust centering and groupwise aggregation.
result ExCIR delivers trustworthy agreement with global baselines and full model rankings, reduces runtime, and scales to large datasets.
Tests neural networks for compositional generalization in language.
problem Understanding how neural networks generalize in a compositional manner.
method Developed five tests bridging linguistic and philosophical compositionality with neural models.
result Revealed strengths and weaknesses of different neural architectures.
Proposes a method to derive knowledge graphs from EHR data.
problem Challenges in deriving generalizable knowledge from EHR data.
method Infer conditional dependency structure via a latent graphical block model (LGBM).
result Perfect recovery of block structure demonstrated.
New link detection results using closures of 3-braids.
problem Link detection using homology theories.
method Closure operations on 3-braids and homology theories.
result Detection of specific links using link Floer homology, Khovanov homology, and annular Khovanov homology.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
Flexible pipeline for 3D vehicle detection from 2D images.
problem Current methods lack 3D perception of vehicles and other objects.
method Adopt any 2D detection network, fuse with 3D point cloud, develop model fitting algorithm, refine with CNN.
result 3D detection results rank second among algorithms, demonstrating competencies.
Deep learning improves combustor anomaly detection in gas turbines.
problem Improving anomaly detection performance in gas turbine combustors.
method Hierarchically learned features from exhaust gas temperature sensor measurements using deep learning.
result Deep learning-based anomaly detection significantly improved combustor anomaly detection performance.
Paper proposes RL for real-time smart grid cyber attack detection.
problem Real-time detection of cyber-attacks in smart grids.
method Formulated as POMDP, uses model-free reinforcement learning.
result Effective in timely and accurate detection of cyber-attacks.
Graph energy helps detect communities in networks better than traditional methods.
problem Detecting communities in sparse networks where traditional methods fail.
method Using graph energy based on the full spectrum of adjacency matrices.
result The difference in graph energy between a planted partition model and an Erdős--Rényi network has a distinct transition at the detectability threshold.
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
problem Detecting PMH signals without prior CSI is challenging and computationally expensive.
method Proposes HEM-ML and HEM-KD schemes using EM and KD-tree for efficient detection.
result Achieves comparable detection results to optimal ML detector with reduced complexity.
Develops slope detection for 3-manifolds with torus boundaries.
problem Determining slopes on the boundary of 3-manifolds with torus boundaries.
method Introduces order-detection and representation-detection of slopes, proving their equivalence.
result Shows how slopes' behavior changes with cabling, improving previous results.
2DSig-Detect detects adversarial perturbations in images.
problem Adversarial attacks degrade image model performance.
method 2D-signature embedded semi-supervised framework using rough path theory.
result 2DSig-Detect outperforms other methods in detecting adversarial perturbations.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
FADO detects adversarial scenarios without stochastic methods.
problem Adversarial scenarios where stochastic methods are not applicable.
method Machine learning and online learning theory-based deterministic detection.
result FADO provides theoretical guarantees for detection performance.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and PC detection.
Real-time fuel leakage detection framework MOCPD improves accuracy.
problem Early detection of fuel leakage to prevent hazards and losses.
method Memory-based Online Change Point Detection (MOCPD) framework.
result MOCPD outperforms baseline methods in detection accuracy.
Study on detecting hierarchical community structures in networks.
problem Detecting hierarchical community structures in networks.
method Analysis of planted hierarchies of partitions in networks, identifying additional detectability phases.
result There are additional phases in which the presence of multiple consistent partitions can either help or hinder detection of hierarchical structures.
QuickStop detects misinformation quickly using Markov models.
problem Real-time detection of misinformation.
method Markov model for probabilistic information spreading; optimal stopping algorithm.
result QuickStop outperforms existing algorithms in accuracy and detection time.