This is a experimental project. Feel free to send feedback!

Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

Systematic Spurion Matching between Low Energy EFT and Chiral Lagrangian

The hadronic chiral Lagrangian can be matched from the low energy effective field theory (LEFT) operators at the quark level. Traditionally, as the mass dimension of the LEFT operators increases, more...

Useful Fields:

The article presents a novel systematic procedure for matching low energy effective field theory and chiral Lagrangian without using external sources, which could lead to significant improvements in theoretical calculations and modeling in particle physics. The methodological rigor and reformulation of established theories are noteworthy, indicating a solid contribution to the field, although its applicability may be limited to specific contexts in hadronic physics.

Evolution of SMBHs in light of PTA measurements: implications for growth by mergers and accretion

We study the growth of supermassive black holes accounting for both accretion and mergers. The former is informed by observations of the quasar luminosity function (QLF) and the latter by the gravitat...

Useful Fields:

This study presents a novel approach to understanding supermassive black hole (SMBH) growth by integrating observational data from both quasars and gravitational waves. The methodology is robust, linking two significant phenomena (quasar activity and gravitational wave background) and providing new insights into their interrelation. The implications for the Eddington ratio and black hole abundance also open pathways for future investigations into the evolution of SMBHs and their contributions to cosmic structure formation.

Phase-space Generalized Brillouin Zone for spatially inhomogeneous non-Hermitian systems

The generalized Brillouin zone (GBZ) has been highly successful in characterizing the topology and band structure of non-Hermitian systems. However, its applicability has been challenged in spatially ...

Useful Fields:

The article presents a novel phase-space generalized Brillouin zone formalism tailored for spatially inhomogeneous non-Hermitian systems, addressing a significant gap in current theoretical frameworks. Its methodological rigor and innovative approach to modeling non-local effects through a bifurcation phenomenon add substantial depth to the field. The potential applications in photonic crystals and metamaterials underscore its practical relevance, making it highly impactful for future research.

Self-consistent solution to the semiclassical Einstein equations of a star

We present the interior solution for a static, spherically symmetric perfect fluid star backreacted by QFT in four dimensions invoking no arbitrary parameters. It corresponds to a constant energy dens...

Useful Fields:

This article presents a novel and complete approach to the semiclassical Einstein equations, which could significantly alter our understanding of astrophysical objects under quantum field theory. The absence of arbitrary parameters and the exploration of non-singular ultra-compact solutions add substantial depth and rigor, making it a groundbreaking study in theoretical physics.

GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation

We present GeoManip, a framework to enable generalist robots to leverage essential conditions derived from object and part relationships, as geometric constraints, for robot manipulation. For example,...

Useful Fields:

GeoManip presents a novel framework that integrates geometric constraints with symbolic language processing in robotic manipulation. The training-free approach, emphasis on generalizability across diverse tasks, and focus on human-robot interaction significantly enhance its relevance. The combination of geometric understanding and action execution without extensive training offers a promising direction for future research in robotics, particularly concerning more adaptable and intelligent robotic systems.

SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods focus on train...

Useful Fields:

The work presents a significant advancement in expressive human pose and shape estimation (EHPS) by addressing both data scaling and model scaling, which are critical challenges in the field. The combination of a systematic investigation across 40 datasets and optimizations in training strategies provides novel insights that can elevate existing methodologies. The use of vision transformers and minimalist architectures demonstrates methodological rigor and the potential for widespread applicability. The robust benchmark results across multiple datasets further validate the claims, indicating strong applicability in real-world scenarios. Overall, the impact of this research can influence future direction in EHPS, particularly in enhancing model adaptability and transferability in diverse environments.

VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs). We d...

Useful Fields:

The article demonstrates significant novelty by exploring an under-researched area of learning from unlabeled video data rather than text-based inputs. The methodological rigor is evident through the comprehensive evaluation of the VideoWorld model across diverse tasks, showcasing its ability to understand and apply complex knowledge in practical scenarios. Furthermore, the open-source nature encourages accessibility and further research, potentially stimulating advancements in related fields.

MUSE observations of V1425 Aql reveal an arc-shaped nova shell

Nova shells are the remnants of a nova eruption in a cataclysmic variable system. By studying their geometry we can better understand the physical mechanisms that shape them during the nova eruption. ...

Useful Fields:

The article presents novel observational data regarding the geometry of nova shells, which is significant for current astrophysical understanding of nova eruptions. The detailed analysis using MUSE data adds methodological rigor, while the findings challenge existing theories and suggest avenues for further research into asymmetric ejecta. This combination of novelty, applicability, and the potential to guide future studies warrants a high relevance score.

Graphs with given automorphism group and large clique number

Barbieri recently showed that the finite graphs realising any given finite automorphism group have unbounded genus, answering a question of Cornwell et al. In this note we give a short proof of a stro...

Useful Fields:

The article presents a significant advancement in understanding the relationship between automorphism groups and graph properties, specifically clique numbers. The novelty of providing a short proof for the unbounded clique number enhances methodological rigor. This result could inspire further research into graph theory and its applications in combinatorial structures. Overall, it is a strong contribution but may primarily appeal to a specialized audience.

Two halves don't make a whole: instability and idleness emerging from the co-evolution of the production and innovation processes

We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explor...

Useful Fields:

The article presents a novel agent-based fund-flow model that explores inefficiencies in production and innovation processes, highlighting the importance of worker idleness and its relationship with creativity. This disaggregated approach provides deep insights into how production and innovation can co-evolve, which is a relatively underexplored area in current literature. The methodological rigor, alongside practical implications, makes the findings valuable for both academics and practitioners. However, while the topic is significant, the generalizability of the model's results may vary across different industries, slightly limiting its potential impact.

Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning

Cryptocurrency is a digital currency that uses blockchain technology with secure encryption. Due to the decentralization of these currencies, traditional monetary systems and the capital market of eac...

Useful Fields:

The article presents a novel application of sentiment analysis specifically focused on cryptocurrency discussions in the Iranian context, which is relatively underexplored compared to English research. The use of diverse natural language processing techniques and classical machine learning algorithms enhances the methodological rigor. The findings provide relevant insights for economic managers in navigating public sentiment around cryptocurrencies, thus adding to its real-world applicability.

Steering Large Language Models with Feature Guided Activation Additions

Effective and reliable control over large language model (LLM) behavior is a significant challenge. While activation steering methods, which add steering vectors to a model's hidden states, are a ...

Useful Fields:

The article presents a novel method (FGAA) for steering large language models, which addresses the key challenges of precision and interpretability. Its rigorous evaluations against existing methods demonstrate clear advancements in model control. The integration of established techniques suggests substantial methodological rigor and potential for real-world applications, making it highly relevant for the advancement of LLM research.

Classifying the groups of order $p q$ in Lean

This note discusses our formalisation in Lean of the classification of the groups of order $p q$ for (not necessarily distinct) prime numbers $p$ and $q$ , together with various...

Useful Fields:

The article presents a formalized classification of groups of order pq, a fundamental topic in group theory. The use of Lean, a proof assistant, enhances the methodological rigor and precision of the results, allowing for potential applications in both theoretical research and educational settings. This work is significant in providing a framework for verifying group properties computationally, which is a relatively novel approach in the field.

Multi-Head Self-Attending Neural Tucker Factorization

Quality-of-service (QoS) data exhibit dynamic temporal patterns that are crucial for accurately predicting missing values. These patterns arise from the evolving interactions between users and service...

Useful Fields:

This article presents a novel approach for tensor factorization that effectively captures the temporal dynamics of QoS data, addressing a critical challenge in the field. Its methodological rigor and demonstrated empirical superiority over existing models indicate significant advancements in predictive performance. However, the practical applicability of the method in broader contexts remains to be evaluated, which slightly lowers the score.

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong

One of the most widely used methods to evaluate LLMs are Multiple Choice Question (MCQ) tests. MCQ benchmarks enable the testing of LLM knowledge on almost any topic at scale as the results can be pro...

Useful Fields:

The article addresses a crucial aspect of evaluating Large Language Models (LLMs), specifically their confidence levels influenced by reasoning strategies. This research reveals important insights into LLM behavior, potentially informing future methodological approaches in AI evaluation and enhancing model reliability. The study's robust empirical evaluation across several models enhances its credibility and applicability in real-world contexts.

Monitoring lead-acid battery function using operando neutron radiography

Investigating batteries while they operate allows researchers to track the inner electrochemical processes involved in working conditions. This study describes the first neutron radiography investigat...

Useful Fields:

The study presents a novel application of operando neutron radiography to monitor lead-acid batteries, which is a significant advancement in the field of electrochemical energy storage. The methodological innovation, combined with a solid experimental design and simulation approach, suggests that the findings could inspire further research in battery technology and diagnostics. Moreover, the study addresses a critical gap in understanding battery processes under operational conditions, enhancing both fundamental and applied research in this area.

The Structure of Scenarios

Scenarios elicit possibilities that may be ignored otherwise, as well as causal relations between them. Even when too little information is available to assess reliable probabilities, the structure of...

Useful Fields:

This article presents a novel methodological approach using hypergraph analysis to evaluate scenario structures, which could significantly enhance the analytical capabilities in fields that rely on scenario planning and decision-making. The discussion on causal relationships and the handling of uncertainty adds substantial value, indicating a robust methodological rigor and potential for broad applicability across various disciplines.

Search for $K_{S,L}$ oscillations and invisible decays into the dark sector at NA64

The decays $K_{S,L}$ have never been experimentally tested. In the Standard Model their branching ratios for the decay into two neutrinos are predicted to be extremely small, $Br(K_{S,L} \...

Useful Fields:

This article addresses an unexplored area of particle physics by proposing experimental tests of $K_{S,L}$ decays, which could reveal new physics beyond the Standard Model (SM). The approach of examining various extensions of the SM and their implications for these decays showcases both novelty and potential for significant impact on future theoretical and experimental research. The rigorous analysis of sensitivities at NA64 further strengthens the importance of the work.

Spectral Bounds of the Generating Graph of $\mathbb{Z}_n.$

Let $G$ be a group. A group is said to be $k$ -generated if it can be generated by its $k$ elements. A generating set of $G$ is called a minimal generating set if no pro...

Useful Fields:

The paper presents a nuanced exploration of the generating graph of a commonly studied group, $ ext{Z}_n$, which contributes to both group theory and spectral graph theory. Its novelty lies in the combination of algebraic properties with graph theoretic perspectives, providing insight into minimal generating sets and spectral properties that could inspire further investigations in related areas. However, the application may be limited primarily to theoretical explorations without substantial practical implications.