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.
Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single…
We introduce a new sampling method for large language models that balances diversity and parallelism.
Adaptive Nucleus Truncation Improves Long-Form Reasoning
Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithm…
Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top- and nucleus sampling, they …
Detection of cell nuclei in microscopic images is a challenging research topic, because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success sho…
Decoding strategies often exclude human-like tokens, creating a detectable gap in generated text.
Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning method…
End-to-end audio recognition system improves accuracy.
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resul…
In this note we show that there are 4-manifolds not containing Gompf nucleus ; in this way we answer Problem 4.98 of Kirby's problem list in the negative.
With ongoing developments and innovations in single-cell RNA sequencing methods, advancements in sequencing performance could empower significant discoveries as well as new emerging possibilities to address biological and medical investigations. In the study, we will be using the dataset collected by the authors of Sys…
This work improved clustering methods by analyzing various datasets and dendrograms.
Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to separate each nucleus. In this paper, we introduce a proposal-free deep …
Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmentation is a very labor-intensive and time-consuming task. Thereby, providing a tool that can facilitate and speed up this procedure is very …
Adaptive contrastive search improves text generation quality and diversity.
The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights. Fuzzy damping coefficients are obtained for each nucleus or center p…
The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent test. The recent discovery of the extremely neutron-rich nuclei around Ca [Tarasov, 2018] and the experimental determination of masse…
Study limits of circadian synchronization under different light signals.
NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.
Top-H decoding improves text generation by balancing creativity and coherence.
Mogami introduced in 1995 a large class of triangulated 3-dimensional pseudomanifolds, henceforth called "Mogami pseudomanifolds". He proved an exponential bound for the size of this class in terms of the number of tetrahedra. The question of whether all 3-balls are Mogami has remained open since, a positive answer wou…
Based on the work of Durhuus-J{ó}nsson and Benedetti-Ziegler, we revisit the question of the number of triangulations of the 3-ball. We introduce a notion of nucleus (a triangulation of the 3-ball without internal nodes, and with each internal face having at most 1 external edge). We show that every triangulation can b…
A new sampler for FLMs improves token-level decoding controls.
AI systems need reliable testing to ensure safety and trustworthiness.
New method boosts performance of diffusion models on discrete data like natural language.
Adds a precortical module to CNNs for improved robustness to light variations.
Bayesian method learns causal orderings from heterogeneous data.
In the area of traditional physics the atomic nucleus belongs to the most complex systems. It involves essentially all elements that characterize complexity including the most distinctive one whose essence is a permanent coexistence of coherent patterns and of randomness. From a more interdisciplinary perspective, thes…
Understanding the three-dimensional (3D) structure of the genome is essential for elucidating vital biological processes and their links to human disease. To determine how the genome folds within the nucleus, chromosome conformation capture methods such as HiC have recently been employed. However, computational methods…
Proposes a Varying-Coefficient MoE model for analyzing dynamic data.
Deep learning classifies autism vs controls with high accuracy using large fMRI dataset.
Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with sets of observations, where each set is labeled with class information, and the prediction of a class label is performed a…
Innocent musing on geodesics on the surface of helical pasta shapes leads to a single continuous 4-parameter family of surfaces invariant under at least a 1-parameter symmetry group and which contains as various limits spheres, tori, helical tubes, and cylinders, all useful for illustrating various aspects of geometry …
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
New model uncovers non-Euclidean neural representations.
The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough binding energy to prevent protons from escaping the nucleus. Predicting properties of unstable nuclear states in the vast territory of proton em…
The paper constructs non-smoothable actions on spin 4-manifolds.
The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses beyond the regions where experimenta…
The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take …
Improves TTS accuracy by correcting context-dependent units.
Enhanced Sampling Scheme improves masked generative modeling.
PRS improves rejection sampling by learning better proposals.
This paper reviews various sampling methods from statistics and machine learning.
RISA improves VFL by using imputed samples with low uncertainty.
Improved privacy-preserving methods for estimating multiple samples from distributions.
Paper introduces a new sampling method combining Consistency Models with importance sampling.
Neural network accuracy improves with denser training samples.