arXiv subjects

Jin Sun

Publications and source records attributed to Jin Sun.

At least 73 records · Page 4Linked to original sources

arXiv2023

SAM for Poultry Science

In recent years, the agricultural industry has witnessed significant advancements in artificial intelligence (AI), particularly with the development of large-scale foundational models. Among these foundation models, the Segment Anything Model (SAM), introduced by Meta AI Research, stands out as a groundbreaking solution for object segmentation tasks. While SAM has shown success in various agricultural applications, its potential in the poultry industry, specifically in the context of cage-free hens, remains relatively unexplored. This study aims to assess the zero-shot segmentation performance of SAM on representative chicken segmentation tasks, including part-based segmentation and the use of infrared thermal images, and to explore chicken-tracking tasks by using SAM as a segmentation tool. The results demonstrate SAM's superior performance compared to SegFormer and SETR in both whole and part-based chicken segmentation. SAM-based object tracking also provides valuable data on the behavior and movement patterns of broiler birds. The findings of this study contribute to a better understanding of SAM's potential in poultry science and lay the foundation for future advancements in chicken segmentation and tracking.

cs.CV↗

arXiv2023

On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.

cs.AI↗

arXiv2023

AGI for Agriculture

Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry.

cs.AI↗

arXiv2023

Electroweak precision tests for triplet scalars

Electroweak precision observables are fundamentally important for testing the standard model (SM) or its extensions. The influences to observables from new physics within the electroweak sector can be expressed in terms of oblique parameters S, T, U. The recently reported W mass excess anomaly by CDF modifies these parameters in a significant way. By performing the global fit with the CDF new W mass measurement data, we obtain $S=0.03 \pm 0.03$, $T=0.06 \pm 0.02$ and $U=0.16 \pm 0.03$ (or $S=0.14 \pm 0.03$, $T=0.24 \pm 0.02$ with $U=0$) which is significantly away from zero as SM would predict. The CDF excess strongly indicates the need of new physics beyond SM. We carry out a global fit to study the influence of two different cases of simple extensions by a hyper-charge $Y=1$ triplet scalar $Δ$ (corresponding to the type-II seesaw model) and a $Y=0$ real triplet scalar $Σ$ on electroweak precision tests to determine parameter space in these models to solve the W mass anomaly and discuss the implications. We find that these triplets can affect the oblique parameters significantly at the tree and loop levels.

hep-ph↗

arXiv2023

What's in a Decade? Transforming Faces Through Time

How can one visually characterize people in a decade? In this work, we assemble the Faces Through Time dataset, which contains over a thousand portrait images from each decade, spanning the 1880s to the present day. Using our new dataset, we present a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like, had it been taken in other decades. Our framework optimizes a family of per-decade generators that reveal subtle changes that differentiate decade--such as different hairstyles or makeup--while maintaining the identity of the input portrait. Experiments show that our method is more effective in resynthesizing portraits across time compared to state-of-the-art image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at https://facesthroughtime.github.io

cs.CV↗

arXiv2022

Dark photon kinetic mixing effects for the CDF W-mass measurement

A new $U(1)_X$ gauge boson $X$ primarily interacting with a dark sector can have renormalizable kinetic mixing with the standard model (SM) $U(1)_Y$ gauge boson $Y$. This mixing besides introduces interactions of dark photon and dark sector with SM particles, it also modifies interactions among SM particles. The modified interactions can be casted into the oblique $S$, $T$ and $U$ parameters. We find that with the dark photon mass larger than the $Z$ boson mass, the kinetic mixing effects can reduce the tension of the W mass excess problem reported recently by CDF from $7σ$ deviation to within $3 σ$ compared with theory prediction. If there is non-abelian kinetic mixing between $U(1)_X$ and $SU(2)_L$ gauge bosons, in simple renormalizable models of this type a triplet Higgs is required to generate the mixing. We find that this triplet with a vacuum expectation value of order 5 GeV can naturally explain the W mass excess.

hep-ph↗

arXiv2022

Impact of granular inclusions on the phase behavior of colloidal gels

Colloidal gels formed from small attractive particles are commonly used in formulations to keep larger components in suspension. However, despite extensive work characterizing unfilled gels, little is known about how larger inclusions alter the phase behavior and microstructure of the colloidal system. Here we use numerical simulations to examine how larger `granular' particles can alter the gel transition phase boundaries. We find two distinct regimes depending on both the filler size and native gel structure: a `passive' regime where the filler fits into already-present voids, giving little change in the transition, and an `active' regime where the filler no longer fits in these voids and instead perturbs the native structure. In this second regime the phase boundary is controlled by an effective colloidal volume fraction given by the available free volume.

cond-mat.soft↗

arXiv2022

CP violating dark photon kinetic mixing and type-III seesaw model

The hypothetical dark photon portal connecting the visible and dark sectors of the Universe has received considerable attention in recent years, with a focus on CP-conserving kinetic mixing between the Standard Model (SM) hypercharge gauge boson and a new U(1)$_X$ gauge boson. In the effective field theory context, one may write down non-renormalizable CP-violating kinetic mixing interactions involving the $X$ and SU(2)$_L$ gauge bosons. We construct for the first time a renormalizable model for CP-violating kinetic mixing that induces CP-violating non-Abelian kinetic mixing at mass dimension five. The model grows out of the type-III seesaw model, with the lepton triplets containing right-handed neutrinos playing a crucial role in making the model renormalizable and providing a bridge to the origin of neutrino mass. This scenario also accommodates electron electric dipole moments (EDM) as large as current experimental bound, making future EDM searches an important probe of this scenario.

hep-ph↗

arXiv2022

Flavor Specific $U(1)_{B_q-L_μ}$ Gauge Model for Muon $g-2$ and $b \to s \bar μμ$ Anomalies

The muon $(g-2)_μ$ and $b\to s \bar μμ$ induced $B$ anomalies as hints of new physics beyond the standard model (SM) have attracted much attention. These two anomalies indicate that there may exist new interaction specifically related to muon. A lot of theoretical ideas have been proposed to explain these anomalies. Gauged flavor specific $U(1)_{B_q-L_μ}$ is among the promising ones. The new gauge boson $Z'$ from $U(1)_{B_q-L_μ}$ interacts with muon and provides necessary ingredient to solve the $(g-2)_μ$ anomaly. The $Z'$-quark coupling can generate flavor changing interactions after diagonalization of quark mass matrix between weak eigen-state and mass eigen-state basis. We revisit challenges for such models attempting to explain the $(g-2)_μ$ and $B$ anomalies separately or simultaneously. We find although for $U(1)_{B_q-L_μ}$ models there is still parameter space to provide solutions for separately explaining the $(g-2)_μ$ and $B$ anomalies, there exists no parameter space for such models to solve both the anomalies simultaneously, after taking into account existing constraints from $τ\to μγ$, $τ\to 3 μ$, neutrino trident and $B_s - \bar B_s$ data. Among them leptonic processes restrict $Z^\prime$ mass to be less than a few hundred MeV if required to solve the $(g-2)_μ$ anomaly, which causes conflict between data from $B_s - \bar B_s$, $D^0 - \bar D^0$ mixing and also hadron decays with $Z^\prime$ in the final states. The effects of $U(1)_Y$ and $U(1)_{B_q-L_μ}$ kinetic mixing on these anomalies are also studied. We find that neither can these effects do much to bring the two anomalies together to be solved simultaneously.

hep-ph↗

arXiv2022

Widening the $U(1)_{L_μ-L_τ}$ $Z^\prime$ mass range for resolving the muon $g-2$ anomaly

Exchanging a $Z^\prime$ gauge boson is a favored mechanism to solve the muon $(g-2)_μ$ anomaly. Among such models the $Z^\prime$ from $U(1)_{L_μ- L_τ}$ gauge group has been extensively studied. In this model the same interaction addressing $(g-2)_μ$, leads to an enhanced muon neutrino trident (MNT) process $ν_μN \to ν_μμ\bar μN$ constraining the $Z^\prime$ mass to be less than a few hundred MeV. Many other $Z^\prime$ models face the same problem. It has long been realized that the coupling of $Z^\prime$ in the model can admit $(\bar μγ^μτ+ \bar ν_μγ^μL ν_τ)Z^\prime_μ$ interaction which does not contribute to the MNT process. It can solve $(g-2)_μ$ anomaly for a much wider $Z^\prime$ mass range. However this new interaction induces $τ\to μ\barν_μν_τ$ which rules out it as a solution to $(g-2)_μ$ anomaly. Here we propose a mechanism by introducing type-II seesaw $SU(2)_L$ triplet scalars to evade constraints from all known data to allow a wide $Z^\prime$ mass range to solve the $(g-2)_μ$ anomaly. This mechanism opens a new window for $Z^\prime$ physics.

hep-ph↗

arXiv2021

Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision

The abundance and richness of Internet photos of landmarks and cities has led to significant progress in 3D vision over the past two decades, including automated 3D reconstructions of the world's landmarks from tourist photos. However, a major source of information available for these 3D-augmented collections---namely language, e.g., from image captions---has been virtually untapped. In this work, we present WikiScenes, a new, large-scale dataset of landmark photo collections that contains descriptive text in the form of captions and hierarchical category names. WikiScenes forms a new testbed for multimodal reasoning involving images, text, and 3D geometry. We demonstrate the utility of WikiScenes for learning semantic concepts over images and 3D models. Our weakly-supervised framework connects images, 3D structure, and semantics---utilizing the strong constraints provided by 3D geometry---to associate semantic concepts to image pixels and 3D points.

cs.CV↗

arXiv2021

Flavor-changing Majoron interactions with leptons

When the Standard Model Higgs sector is extended with a complex singlet that breaks global lepton number symmetry spontaneously, a massless Goldstone boson called the Majoron J arises. In addition to increasing Higgs invisible decay through mixing, the Majoron can generally have flavor-changing interactions with fermions. We find that type-III seesaw model poses such interesting properties with both charged leptons and neutrinos. This opens up new channels to search for the Majoron. We use the experimental data such as muonium-anti-muonium oscillation and flavor-changing neutrino and charged lepton decays to put constraints on the couplings. As a novel way to reveal the chiral properties of these interactions, we propose an experimentally measurable polarization asymmetry of flavor-changing l to l' J decays.

hep-ph↗

arXiv2021

Structure Of Flavor Changing Goldstone Boson Interactions

General flavor changing interactions for a Goldstone boson (GB) to fermions due to a spontaneous global $U(1)_G$ symmetry breaking are discussed. This GB may be the Axion, solving the strong QCD CP problem, if there is a QCD anomaly for the $U(1)_G$ charge assignments for quarks. Or it may be the Majoron in models from lepton number violation in producing seesaw Majorana neutrino masses if the symmetry breaking scale is much higher than the electroweak scale. It may also, in principle, play the roles of Axion and Majoron simultaneously as far as providing solution for the strong CP problem and generating a small Majorana neutrino masses are concerned. Great attentions have been focused on flavor conserving GB interactions. Recently flavor changing Axion and Majoron models have been studied in the hope to find new physics from rare decays in the intensity frontier. In this work, we will provide a systematic model building aspect study for a GB having flavor changing neutral current (FCNC) interactions in both the quark and lepton sectors, or separately in the quark, charged lepton and neutrino sectors with sources of FCNC interactions identified in detail. We provide a general proof of the equivalence of using physical GB components and GB broken generators for calculating GB couplings to two gluons and two photons, and some issues related to models having spontaneous CP violation are discussed. We will also provide some details for obtaining FCNC GB interactions in several popular models, such as the Type-I, -II, -III seesaw and Left-Right symmetric models, and point out some special features in these models.

hep-ph↗

arXiv2020

DFSZ Axion Couplings Revisited

Among many possibilities, solar axion has been proposed to explain the electronic recoil events excess observed by Xenon1T collaboration, although it has tension with astrophysical observations. The axion couplings, to photon $g_{aγ}$ and to electron $g_{ae}$ play important roles. These couplings are related to the Peccei-Quinn (PQ) charges $X_f$ for fermions. In most of the calculations, $g_{aγ}$ is obtained by normalizing to the ratio of electromagnetic anomaly factor $E = TrX_f Q^2_f N_c$ ($N_c$ is 3 and 1 for quarks and charged leptons respectively) and QCD anomaly factor $N = TrX_q T(q)$ ($T(q)$ is quarks' $SU(3)_c$ index). The broken PQ symmetry generator is used in the calculation which does not seem to extract out the components of broken generator in the axion which are "eaten" by the $Z$ boson. However, using the physical components of axion or the ratio of anomaly factors should obtain the same results in the DFSZ for $g_{aγ}$. When going beyond the standard DFSZ models, such as variant DFSZ models, where more Higgs doublets and fermions have different PQ charges, one may wonder if the results are different. We show that the two methods obtain the same results as expected, but the axion couplings to quarks and leptons $g_{af}$ (here f indicates one of the fermions in the SM) are more conveniently calculated in the physical axion basis. The result depends on the values of the vacuum expectation values leading to a wider parameter space for $g_{af}$ in beyond the standard DFSZ axion. We also show explicitly how flavor conserving $g_{af}$ couplings can be maintained when there are more than one Higgs doublets couple to the up and down fermion sectors in variant DFSZ models at tree level, and how flavor violating couplings can arise.

hep-ph↗

arXiv2020

$SU(3)$ Flavor Symmetry for Weak Hadronic Decays of ${\bf B}_{bc}$ Baryons

Baryons with a heavy c-quark or a heavy b-quark and also two c-quarks have been discovered. These states are expected in QCD and therefore provide a test for the theory. There should be double beauty baryons, and also an intriguing possibility that baryons ${\bf B}_{bc}$ with a c-quark, a b-quark. These states are yet to be discovered. The main decay modes of ${\bf B}_{bc}$ are expected to be weak processes from theoretical understanding of their mass spectrum. These decay modes can provide crucial information about these heavy baryons ${\bf B}_{bc}$. We analyze two body hadronic weak decays for ${\bf B}_{bc}$ using $SU(3)$ flavor symmetry. Any one of the $c$ and $b$ decays will induce ${\bf B}_{bc}$ to decay. We find that the Cabibbo allowed decays ${\bf B}_{bc} \to {\bf B}_b + M$ due to $c \to s u \bar d$ can be crucial for exploration. The LHC may have the sensitivity to discover such decays. Other ${\bf B}_{bc}$ decays due to $b \to c q' \bar q$ are sub-leading. Several relations among branching ratios are obtained which can be used to test $SU(3)$ flavor symmetry.

hep-ph↗

arXiv2020

Hidden Footprints: Learning Contextual Walkability from 3D Human Trails

Predicting where people can walk in a scene is important for many tasks, including autonomous driving systems and human behavior analysis. Yet learning a computational model for this purpose is challenging due to semantic ambiguity and a lack of labeled data: current datasets only tell you where people are, not where they could be. We tackle this problem by leveraging information from existing datasets, without additional labeling. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. However, this augmented data is still sparse. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss. Using this strategy, we demonstrate a model that learns to predict a walkability map from a single image. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance compared to baselines and state-of-the-art models.

cs.CV↗

arXiv2020

CP violating phase sum rule $δ^q_{\rm KM}+δ^l_{\rm KM}=0$ for CKM and PMNS matrices

The non-zero Dirac phases $δ^q$ and $δ^l$ in the CKM and PMNS mixing matrices signify CP violation. In general they are independent. Experimental data including recent T2K results show, however, that in the original KM parameterization for the mixing matrix, the sum $δ^q_{\rm KM} + δ^l_{\rm KM}$ is close to zero with $δ^q_{\rm KM}$ to be approximately $π/2$. The KM parameterization may have provided some hints that these phases are actually related and CP is maximally violated. We show that this sum rule can be accommodated in models with spontaneous CP violation where both phases originate from a non-trivial common spontaneous CP violating maximized phase in the Higgs potential. We find some interesting phenomenological consequences for flavor changing neutral current and CP violation for such a model. In particular, data from $B_s - \bar B_s$ mixing provide very strong constraints on the mass scale for the new neutral scalars in the model, yet the model still allows the electric dipole moments of electron and neutron to reach to their current upper bounds. The model can be tested by near future experiments.

hep-ph↗

arXiv2020

Visual Chirality

How can we tell whether an image has been mirrored? While we understand the geometry of mirror reflections very well, less has been said about how it affects distributions of imagery at scale, despite widespread use for data augmentation in computer vision. In this paper, we investigate how the statistics of visual data are changed by reflection. We refer to these changes as "visual chirality", after the concept of geometric chirality - the notion of objects that are distinct from their mirror image. Our analysis of visual chirality reveals surprising results, including low-level chiral signals pervading imagery stemming from image processing in cameras, to the ability to discover visual chirality in images of people and faces. Our work has implications for data augmentation, self-supervised learning, and image forensics.

cs.CV↗