arXiv subjects

Yang Tian

Publications and source records attributed to Yang Tian.

87 records · Page 5Linked to original sources

arXiv2022

A unified theory of information transfer and causal relation

Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applications in both science and engineering fields, critical mysteries about its foundations remain unsolved. Fundamental yet difficult questions concern how information transfer and causal relation originate, what they depend on, how they differ from each other, and if they are created by a unified and general quantity. These questions essentially determine the validity of causal relation measurement via information transfer. Here we pursue to lay a complete theoretical basis of information transfer and causal relation. Beyond the well-known relations between these concepts that conditionally hold, we demonstrate that information transfer and causal relation universally originate from specific information synergy and redundancy phenomena characterized by high-order mutual information. More importantly, our theory analytically explains the mechanisms for information transfer and causal relation to originate, vanish, and differ from each other. Moreover, our theory naturally defines the effect sizes of information transfer and causal relation based on high-dimensional coupling events. These results may provide a unified view of information, synergy, and causal relation to bridge Pearl's causal inference theory in computer science and information transfer analysis in physics.

cond-mat.stat-mech↗

arXiv2022

Self-organized critical dynamics of RNA virus evolution

RNA virus (e.g., SARS-CoV-2) evolves in a complex manner. Studying RNA virus evolution is vital for understanding molecular evolution and medicine development. Scientists lack, however, general frameworks to characterize the dynamics of RNA virus evolution directly from empirical data and identify potential physical laws. To fill this gap, we present a theory to characterize the RNA virus evolution as a physical system with absorbing states and avalanche behaviors. This approach maps accessible biological data (e.g., phylogenetic tree and infection) to a general stochastic process of RNA virus infection and evolution, enabling researchers to verify potential self-organized criticality underlying RNA virus evolution. We apply our framework to SARS-CoV-2, the virus accounting for the global epidemic of COVID-19. We find that SARS-CoV-2 exhibits scale-invariant avalanches as mean-field theory predictions. The observed scaling relation, universal collapse, and slowly decaying auto-correlation suggest a self-organized critical dynamics of SARS-CoV-2 evolution. Interestingly, the lineages that emerge from critical evolution processes coincidentally match with threatening lineages of SARS-CoV-2 (e.g., the Delta virus). We anticipate our approach to be a general formalism to portray RNA virus evolution and help identify potential virus lineages to be concerned.

q-bio.PE↗

arXiv2022

Information Evolution in Complex Networks

Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but the noise accounts for the intensification. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks.

physics.bio-ph↗

arXiv2021

On-ground calibrations of the GRID-02 gamma-ray detector

The Gamma-Ray Integrated Detectors (GRID) are a space project to monitor the transient gamma-ray sky in the multi-messenger astronomy era using multiple detectors on-board CubeSats. The second GRID detector, GRID-02, was launched in 2020. The performance of the detector, including the energy response, effective area, angular response, and temperature-bias dependence, is calibrated in the laboratory and presented here. These measurements are compared with particle tracing simulations and validate the Geant4 model that will be used for generating detector responses.

astro-ph.IM↗

arXiv2021

Thermodynamics of Encoding and Encoders

Non-isolated systems have diverse coupling relations with the external environment. These relations generate complex thermodynamics and information transmission between the system and its environment. The framework depicted in the current research attempts to glance at the critical role of the internal orders inside the non-isolated system in shaping the information thermodynamics coupling. We characterize the coupling as a generalized encoding process, where the system acts as an information thermodynamics encoder to encode the external information based on thermodynamics. We formalize the encoding process in the context of the nonequilibrium second law of thermodynamics, revealing an intrinsic difference in information thermodynamics characteristics between information thermodynamics encoders with and without internal correlations. During the information encoding process of an external source $\mathsf{Y}$, specific sub-systems in an encoder $\mathsf{X}$ with internal correlations can exceed the information thermodynamics bound on $\left(\mathsf{X},\mathsf{Y}\right)$ and encode more information than system $\mathsf{X}$ works as a whole. We computationally verify this theoretical finding in an Ising model with a random external field and a neural data set of the human brain during visual perception and recognition. Our analysis demonstrates that the stronger internal correlation inside these systems implies a higher possibility for specific sub-systems to encode more information than the global one. These findings may suggest a new perspective in studying information thermodynamics in diverse physical and biological systems.

cond-mat.stat-mech↗

arXiv2021

Fourier-domain transfer entropy spectrum

We propose the Fourier-domain transfer entropy spectrum, a novel generalization of transfer entropy, as a model-free metric of causality. For arbitrary systems, this approach systematically quantifies the causality among their different system components rather than merely analyze systems as entireties. The generated spectrum offers a rich-information representation of time-varying latent causal relations, efficiently dealing with non-stationary processes and high-dimensional conditions. We demonstrate its validity in the aspects of parameter dependence, statistic significance test, and sensibility. An open-source multi-platform implementation of this metric is developed and computationally applied on neuroscience data sets and diffusively coupled logistic oscillators.

physics.data-an↗

arXiv2021

GRB 210121A: A Typical Fireball Burst Detected by Two Small Missions

The Chinese CubeSat Mission, Gamma Ray Integrated Detectors (GRID), recently detected its first gamma-ray burst, GRB 210121A, which was jointly observed by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM). This burst is confirmed by several other missions, including \fermi and \textit{Insight}-HXMT. We combined multi-mission observational data and performed a comprehensive analysis of the burst's temporal and spectral properties. Our results show that the burst is relatively special in its high peak energy, thermal-like low energy indices, and large fluence. By putting it to the $E_{\rm p}$-$E_{\rmγ, iso}$ relation diagram with assumed distance, we found this burst can be constrained at the redshift range of [0.3,3.0]. The thermal spectral component is also confirmed by the direct fit of the physical models to the observed spectra. Interestingly, the physical photosphere model also constrained a redshift of $z\sim$ 0.3 for this burst, which help us to identify a host galaxy candidate at such a distance within the location error box. Assuming the host galaxy is real, we found the burst can be best explained by the photosphere emission of a typical fireball with an initial radius of $r_0\sim$ 3.2 $\times 10^7$ cm.

astro-ph.HE↗

arXiv2021

Compact CubeSat Gamma-Ray Detector for GRID Mission

Gamma-Ray Integrated Detectors (GRID) mission is a student project designed to use multiple gamma-ray detectors carried by nanosatellites (CubeSats), forming a full-time all-sky gamma-ray detection network that monitors the transient gamma-ray sky in the multi-messenger astronomy era. A compact CubeSat gamma-ray detector, including its hardware and firmware, was designed and implemented for the mission. The detector employs four Gd2Al2Ga3O12 : Ce (GAGG:Ce) scintillators coupled with four silicon photomultiplier (SiPM) arrays to achieve a high gamma-ray detection efficiency between 10 keV and 2 MeV with low power and small dimensions. The first detector designed by the undergraduate student team onboard a commercial CubeSat was launched into a Sun-synchronous orbit on October 29, 2018. The detector was in a normal observation state and accumulated data for approximately one month after on-orbit functional and performance tests, which were conducted in 2019.

astro-ph.IM↗

arXiv2020

A General Verification for Functional Completeness by Abstract Operators

An operator set is functionally incomplete if it can not represent the full set $\lbrace \neg,\vee,\wedge,\rightarrow,\leftrightarrow\rbrace$. The verification for the functional incompleteness highly relies on constructive proofs. The judgement with a large untested operator set can be inefficient. Given with a mass of potential operators proposed in various logic systems, a general verification method for their functional completeness is demanded. This paper offers an universal verification for the functional completeness. Firstly, we propose two abstract operators $\widehat{R}$ and $\breve{R}$, both of which have no fixed form and are only defined by several weak constraints. Specially, $\widehat{R}_{\geq}$ and $\breve{R}_{\geq}$ are the abstract operators defined with the total order relation $\geq$. Then, we prove that any operator set $\mathfrak{R}$ is functionally complete if and only if it can represent the composite operator $\widehat{R}_{\geq}\circ\breve{R}_{\geq}$ or $\breve{R}_{\geq}\circ\widehat{R}_{\geq}$. Otherwise $\mathfrak{R}$ is determined to be functionally incomplete. This theory can be generally applied to any untested operator set to determine whether it is functionally complete.

math.LO↗

arXiv2020

Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences

Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Spiking neural networks (SNNs) represent a family of event-driven models with spatiotemporal dynamics for neuromorphic computing, which are widely benchmarked on neuromorphic data. Interestingly, researchers in the machine learning community can argue that recurrent (artificial) neural networks (RNNs) also have the capability to extract spatiotemporal features although they are not event-driven. Thus, the question of "what will happen if we benchmark these two kinds of models together on neuromorphic data" comes out but remains unclear. In this work, we make a systematic study to compare SNNs and RNNs on neuromorphic data, taking the vision datasets as a case study. First, we identify the similarities and differences between SNNs and RNNs (including the vanilla RNNs and LSTM) from the modeling and learning perspectives. To improve comparability and fairness, we unify the supervised learning algorithm based on backpropagation through time (BPTT), the loss function exploiting the outputs at all timesteps, the network structure with stacked fully-connected or convolutional layers, and the hyper-parameters during training. Especially, given the mainstream loss function used in RNNs, we modify it inspired by the rate coding scheme to approach that of SNNs. Furthermore, we tune the temporal resolution of datasets to test model robustness and generalization. At last, a series of contrast experiments are conducted on two types of neuromorphic datasets: DVS-converted (N-MNIST) and DVS-captured (DVS Gesture).

cs.CV↗

arXiv2019

Performance study of a directional fast neutron detection system based on TPC and plastic scintillation detectors

A neutron time projection chamber can locate the approximate direction of a neutron hot spot with high efficiency and a 4π field of view. The angular resolution can be significantly improved by adding several plastic scintillation detectors and using coincidence events. The specific performances of such a coincidence imaging system are studied based on theoretical calculations and experimental results. The calculated value of the angular resolution is approximately 2° for the current system, which agrees well with the experimental results and sets an upper limit for the angular resolution of traditional back projection based online reconstruction methods. Although the statistical iterative method can breakthrough this limit and further improve the angular resolution, the time consumption is usually a problem. The coincidence imaging system can be further optimized for future applications based on the theoretical model.

physics.ins-det↗

arXiv2018

Directional fast neutron detection using a time projection chamber and plastic scintillation detectors

A new method for directional fast neutron detection is proposed based on a neutron time projection chamber (TPC) and position-sensitive plastic scintillation detectors. The detection system can efficiently locate the approximate location of a hot spot with 4π field-of-view using only the neutron TPC. Then, the system generates a high-resolution image of the hot spot using selected coincidence events in the TPC and the scintillation detectors. A prototype was built and tested using a Cf-252 source. An efficiency of 7.1x10-3 was achieved for fast searching. The angular resolution was 7.8° (full width at half maximum, FWHM) for high-resolution imaging using the simple back projection method.

physics.ins-det↗

arXiv2018

Development of a 3-D Position Sensitive Neutron Detector Based on Organic Scintillators with Double Side SiPM Readout

A 3-D position sensitive neutron detector is being developed based on a plastic scintillator array. A double side SiPM readout is used to determine the depth of interaction (DOI) in each scintillator unit. In the preliminary test, the DOI in a 254 x 6 x 6 mm3 SP101 plastic scintillator is measured at different positions using a collimated Co-60 source. The SiPM (KETEK PM6660) signals are recorded by a 2.5 GS/s digital oscilloscope. The DOI results are calculated using both the amplitude and the temporal information. Position resolutions (FWHM) of 2.5 cm and 6.6 cm are realized, respectively. A detector based on a 2-D array is capable of recording the 3-D position information of the incident neutron. The 3-D detector is to be used together with a neutron time projection chamber as a directional fast neutron detector. According to the simulation results, the angular resolution (8 degree FWHM) is much better than that of a typical neutron scatter camera.

physics.ins-det↗

arXiv2015

Mechanisms for convection triggering by cold pools

Cold pools are fundamental ingredients of deep convection. They contribute to organizing the sub-cloud layer and are considered key elements in triggering convective cells. It was long known that this could happen mechanically, through lifting by the cold pools' fronts. More recently, it has been suggested that convection could also be triggered thermodynamically, by accumulation of moisture around the edges of cold pools. A method based on Lagrangian tracking is here proposed to disentangle the signatures of both forcings and quantify their importance in a given environment. Results from a simulation of radiative-convective equilibrium over the ocean show that parcels reach their level of free convection through a combination of both forcings, each being dominant at different stages of the ascent. Mechanical forcing is an important player in lifting parcels from the surface, whereas thermodynamic forcing reduces the inhibition encountered by parcels before they reach their level of free convection.

physics.ao-ph↗

arXiv2013

High efficiency event-counting thermal neutron imaging using Gd doped micro channel plate

An event-counting thermal neutron imaging detector based on 3 mol % natGd2O3 doped micro channel plate (MCP) has been developed and tested. Thermal neutron imaging experiment was carried out with a low flux neutron beam. Detection efficiency of 33 % was achieved with only one doped MCP. The spatial resolution of 72 μm RMS is currently limited by the readout anode. A detector with larger area and improved readout method is now being developed.

physics.ins-det↗