Bursty Nature Research Articles

Strong Balmer break objects at z~7-10 uncovered with JWST

We report the discovery of robust spectroscopically confirmed Balmer break (BB) galaxies and candidates, with secure spectroscopic redshifts $7.1 z 9.6$, from publicly available James Webb Space Telescope (JWST) extra-galactic photometric and spectroscopic surveys. To achieve this, we used dedicated filters probing the BB and inspected the objects with NIRSpec spectroscopy. We have recovered the previously known objects with strong BBs and here reveal 10-11 new objects with clear BBs, thus tripling the number of spectroscopically confirmed galaxies with a BB at $z >7$. Approximately half of them show a pure BB and no signs of recent star formation, whereas the other half show BB and emission lines, most likely indicating galaxies whose star formation ceased earlier and has restarted recently. Overall, we find that $ 10-20$<!PCT!> of all galaxies from our sample show signatures of an evolved stellar population. Furthermore, we find that the strength of the BB does not significantly depend on the rest-UV and rest-optical brightness of these sources. In short, our work confirms that photometry alone has the potential to measure BB strengths and to identify evolved stellar populations at high redshift, and that such objects may be more frequent than previously thought. The presence of galaxies with a range of break strengths and the joint presence of BB and emission lines indicate a bursty nature of the star formation in the early Universe.

Intermittency, bursty turbulence, and ion and electron phase-space holes formation in collisionless current-carrying plasmas

In the previous studies of nonlinear saturation of the Buneman instability caused by high electron drift velocity relative to ions, the phase-space holes and the plateau on the electron velocity distribution function were identified as features of the saturation stage of instability [notably in the paper by Omura et al., J. Geophys. Res. 108, 1197 (2003)]. We have performed a much longer simulation of the Buneman instability and observed a secondary instability. This secondary instability generates fast electron-acoustic waves. By analyzing the phase-space plot of ions and electrons, we show that the fast electron heating and the formation of the plateau of electron velocity distribution function are not due to the quasi-linear diffusion but due to the nonlinear interaction of ion- and electron-acoustic solitary waves (phase-space holes) by exchange of trapped electrons in each wave. We also report the details on the intermittent and bursty nature of turbulence driven by this instability.

Modified Split-Rendering Architecture to enable AI-assisted Application-aware MAC for XR Slice

Supporting Extended Reality (XR) over traditional networks is challenging due to its bursty nature, low latency, and high-reliability requirements. We demonstrate that DiffServ in EPON results in low network utilization, around 10%. Additionally, XR frames are highly correlated due to changes in the application-layer data caused by human activity. By exploiting this correlation, in this letter, we introduce a cross-layer MAC scheduling termed "Application-aware MAC," which provides a manifold increase in network utilization by introducing a small prediction error in the application layer. For enabling this, in this letter, we also suggest a modified split rendering architecture with additional NFVs.

EPOCHS IX. When cosmic dawn breaks: evidence for evolved stellar populations in 7 < z < 12 galaxies from PEARLS GTO and public NIRCam imaging

ABSTRACT The presence of evolved stars in high-redshift galaxies can place valuable indirect constraints on the onset of star formation in the Universe. Thus, we use PEARLS GTO (Prime Extragalactic Areas for Reionization and Lensing Science Guaranteed Time Observations) and public NIRCam (Near Infrared Camera) photometric data to search for Balmer-break candidate galaxies at 7 < z < 12. We find that our Balmer-break candidates at z ∼ 10.5 tend to be older (115 Myr), have lower inferred [O iii] + Hβ equivalent widths (120 Å), have lower specific star formation rates (6 Gyr−1) and redder UV slopes (β = −1.8) than our control sample of galaxies. However, these trends all become less strong at z ∼ 8, where the F444W filter now probes the strong rest-frame optical emission lines, thus providing additional constraints on the current star formation activity of these galaxies. Indeed, the bursty nature of epoch of reionization galaxies can lead to a disconnect between their current spectral energy distribution (SED) profiles and their more extended star formation histories. We discuss how strong emission lines, the cumulative effect of weak emission lines, dusty continua, and active galactic nuclei can all contribute to the photometric excess seen in the rest-frame optical, thus mimicking the signature of a Balmer break. Additional medium-band imaging will thus be essential to more robustly identify Balmer-break galaxies. However, the Balmer break alone cannot serve as a definitive proxy for the stellar age of galaxies, being complexly dependent on the star formation history. Ultimately, deep Near Infrared Spectrograph (NIRSpec) continuum spectroscopy and Mid-Infrared Instrument (MIRI) imaging will provide the strongest indirect constraints on the formation era of the first galaxies in the Universe, thereby revealing when cosmic dawn breaks.

Transcription factor competition facilitates self-sustained oscillations in single gene genetic circuits.

Genetic feedback loops can be used by cells to regulate internal processes or to keep track of time. It is often thought that, for a genetic circuit to display self-sustained oscillations, a degree of cooperativity is needed in the binding and unbinding of actor species. This cooperativity is usually modeled using a Hill function, regardless of the actual promoter architecture. Furthermore, genetic circuits do not operate in isolation and often transcription factors are shared between different promoters. In this work we show how mathematical modelling of genetic feedback loops can be facilitated with a mechanistic fold-change function that takes into account the titration effect caused by competing binding sites for transcription factors. The model shows how the titration effect facilitates self-sustained oscillations in a minimal genetic feedback loop: a gene that produces its own repressor directly without cooperative transcription factor binding. The use of delay-differential equations leads to a stability contour that predicts whether a genetic feedback loop will show self-sustained oscillations, even when taking the bursty nature of transcription into account.

Automatic Modulation Classification in Impulsive Noise: Hyperbolic-Tangent Cyclic Spectrum and Multibranch Attention Shuffle Network

Automatic modulation classification plays an essential role in cognitive communication systems. Traditional automatic modulation classification approaches are primarily developed under Gaussian noise assumptions. Nevertheless, recent empirical studies show that the impulsive noise has emerged in numerous wireless communication systems. The bursty nature of impulsive noise fundamentally challenges the applicability of traditional automatic modulation classification approaches. In order to accurately identify the modulation schemes in impulsive noise environment, in this paper, we propose a novel modulation classification approach through using hyperbolic-tangent cyclic spectrum and multi-branch attention shuffle neural networks. First, based on the designed hyperbolic-tangent autocorrelation function, hyperbolic-tangent cyclic spectrum is proposed to effectively suppress impulsive noise and extract the discriminating features. Then, based on the hyperbolic-tangent cyclic spectrum, a novel deep shuffle neural network is proposed as a classifier to perform the modulation classification through multi-branch attention mechanism to reweight all features. Both numerical and real-data experimental results demonstrate that the proposed algorithm can correctly classify modulation schemes with high accuracy and robustness in impulsive noise.

Burstiness and information spreading in active particle systems.

We construct a temporal network using the two-dimensional Vicsek model. The bursts of the interevent times for a specific pair of particles are investigated numerically. We found that, for different noise strengths, the distribution of the interevent times of a target edge follows a heavy tail, revealing the burstiness of the signals. To further characterize the nature of the burstiness, we calculate the burstiness parameters and the memory coefficients. The results show that, near the phase transition points of the Vicsek model, the burstiness parameters reach the minimum values for each density, indicating a relationship between the phase transition of the Vicsek model and the bursty nature of the signals. Furthermore, we investigate the spreading dynamics on our temporal network using a susceptible-infected model and observe a positive correlation between them.

Keep the Bursts and Ditch the Interleavers

While many communications media, such as wireless and certain classes of wireline channels, typically lead to bursty errors, most decoders are designed assuming memoryless channels. Consequently, communication systems generally rely on interleaving over tens of thousands of bits to match decoder assumptions. Even for short high rate codes, awaiting sufficient data in interleaving and de-interleaving is a significant source of unwanted latency. We construct an extension to the recently proposed Guessing Random Additive Noise Decoding (GRAND) algorithm, which we call GRAND-MO for GRAND Markov Order. By foregoing interleaving and instead making use of the bursty nature of noise, low-latency communication is possible with block error rates outperforming their interleaved counterparts by a substantial margin. We establish that certain well-known binary codes with structured code-word patterns are ill-suited for use in bursty channels, but Random Linear Codes (RLCs) prove robust to correlated noise. We further demonstrate that by operating directly on modulated symbols rather than de-mapped bits, GRAND-MO achieves further performance and complexity gains by exploiting information that is lost in demodulation. As a result, GRAND-MO provides one potential solution for applications that require ultra-reliable low latency communication.

Evidence for trapping-induced nonlinear frequency shifts in Langmuir waves driven via stimulated Raman scattering

Thomson scattering is used to detect the spectra of Langmuir waves driven through the backward stimulated Raman scattering process in a diffraction limited laser focal spot. Measured Langmuir wave spectral frequencies are found to vary in time and have broadened spectral power, consistent with a nonlinear frequency shift of the driven Langmuir wave due to electron-trapping. Broadening of the Langmuir wave spectral power is observed to decrease in time, consistent with measured variations in the frequency shift of the driven Langmuir waves. Furthermore, the observed spectral broadening is consistent with the temporally short (ps), bursty nature of backward stimulated Raman scattered light observed in simulations that cannot be resolved by the Thomson scattering diagnostic. Comparison of the broadened spectrum with time integrated spectra from two-dimensional particle-in-cell simulations shows favorable comparison in the broadened spectral widths, supporting the supposition of electron-trapping induced, nonlinear shifting of daughter Langmuir wave frequencies.

Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes.

Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.

Relevance of the North‐South Electric Field Component in the Propagation of Fast Convective Earthward Flows in the Magnetotail: An Event Study

AbstractFast earthward plasma flows are commonly observed in the magnetotail plasma sheet. These flows are often termed as bursty bulk flows because of their bursty nature, and they are considered to be generated by magnetic reconnection. Close to the neutral sheet (Bx ∼ 0), the fast flows are considered to be associated with an enhanced dawn‐to‐dusk electric field (Ey > 0), which together with the northward magnetic field component (Bz > 0) protrude the plasma earthward via enhanced E × B‐drift. Sometimes, reversals in the dawn‐dusk velocity component perpendicular to the magnetic field (V⊥y) are measured in association with Bx sign changes in the flows. This suggests that the electric field component in the north‐south direction (Ez) can play a role in determining the dawn‐dusk direction of the enhanced drift. We present data measured by the Magnetospheric Multiscale, which demonstrate that Ez can have a dictating role for V⊥y of fast flows. Furthermore, it is shown that the critical contribution of Ez is not limited only to V⊥y, but it can also dominantly determine the enhanced drift of the fast flows in the X direction (V⊥x). The latter can occur also near and at the neutral sheet, which adds an alternative configuration to the conventional picture of Ey and Bz being the main players in driving the earthward fast flows. The domination of Ez in the studied events appears with potential signatures of an influence of a nonzero dawn‐dusk component of the interplanetary magnetic field (IMF By) on the magnetotail.

EEG-based multi-level stress classification with and without smoothing filter

Recently, multi-level stress assessment has become an active research subject. In this context, researchers typically develop models based on machine learning classifiers and features extracted from biosignals like electrocardiogram (ECG) or electroencephalogram (EEG). For that purpose, EEG power spectral density (PSD) is a recurrent feature owing to its high responsiveness and remarkable performance. However, PSD is usually smoothed to cope with its bursty nature, what may cause data leakage and hence call into question classification performance. In this study, our aim was twofold: first, to examine the effect of EEG-PSD smoothing in three-level stress classification, and second, to evaluate the practical viability of a two-level stress detector without smoothing. To this end, we conducted participants through a stress-relax session while recording their EEG. Then, we estimated the EEG-PSD and used the stress reported by the participants as labels for classification. Initially, we developed a three-level stress classifier and examined the effect of smoothing on its performance. We found that classification performance was directly proportional to smoothing intensity (F1-score 0.61–0.94), and also that when smoothing was not applied to features, classification performance was insufficient for practical applicability (AUC < 0.7). We link this behavior to train-test contamination due to smoothing. Subsequently, we attempted two-level stress classification without smoothing. In this case, performance met the criteria for practical applicability (AUC = 0.76). This suggest that performance enhancement in three-level stress classification was caused by data leakage produced by smoothing, and hence, to render realistic stress classifiers each epoch should be processed individually.

A Markov model for IEEE 802.15.4 MAC protocol with energy-efficient GTS utilization under saturated and unsaturated traffic conditions

In the present work, we have proposed an analytical Markov-chain model of the modified slotted Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) IEEE 802.15.4 Medium Access Control (MAC) protocol, which efficiently utilizes the Guaranteed Time Slots (GTSs) under saturated traffic conditions. The five dimensional Markov chain model based analysis is performed for variable Contention Access Period (CAP). Two constraints are introduced i.e. Global Utilization Factor (GUF) and Local Utilization Factor (LUF), to adjust the duration of CAP in each cycle. The work mainly focuses on utilization of GTS under saturated traffic condition for high traffic load applications. To evaluate the performance of our proposed model, we have compared the performance with Misic’s model of standard IEEE 802.15.4 MAC protocol. In addition, the analysis is carried out for practical networks where the data traffic is bursty in nature. The results reflect that our proposed algorithm improves the throughput of the IEEE 802.15.4 MAC protocol in energy-efficient manner at high traffic load applications.

Magnetic reconnection as a mechanism for energy extraction from rotating black holes

Spinning black holes store rotational energy that can be extracted. When a black hole is immersed in an externally supplied magnetic field, reconnection of magnetic field lines within the ergosphere can generate negative energy (relative to infinity) particles that fall into the black hole event horizon while other particles escape stealing energy from the black hole. We show analytically that energy extraction via magnetic reconnection is possible when the black hole spin is high (dimensionless spin $a\sim1$) and the plasma is strongly magnetized (plasma magnetization $\sigma_0>1/3$). The parameter space region where energy extraction is allowed depends on the plasma magnetization and the orientation of the reconnecting magnetic field lines. For $\sigma_0 \gg 1$, the asymptotic negative energy at infinity per enthalpy of the decelerated plasma that is swallowed by a maximally rotating black hole is found to be $\epsilon^\infty_- \simeq - \sqrt{\sigma_0/3}$. The accelerated plasma that escapes to infinity and takes away black hole energy asymptotes the energy at infinity per enthalpy $\epsilon^\infty_+ \simeq \sqrt{3\sigma_0}$. We show that the maximum power extracted from the black hole by the escaping plasma is $P_{\rm extr}^{\rm max} \sim 0.1 M^2\sqrt{\sigma_0}\,w_0$ (here, $M$ is the black hole mass and $w_0$ is the plasma enthalpy density) for the collisionless plasma regime and one order of magnitude lower for the collisional regime. Energy extraction causes a significant spindown of the black hole when $a \sim 1$. The maximum efficiency of the plasma energization process via magnetic reconnection in the ergosphere is found to be $\eta_{\rm max} \simeq 3/2$. Since fast magnetic reconnection in the ergosphere should occur intermittently in the scenario proposed here, the associated emission within a few gravitational radii from the black hole is expected to display a bursty nature.

IntFlow: Integrating Per-Packet and Per-Flowlet Switching Strategy for Load Balancing in Datacenter Networks

Datacenter network load balancing schemes handle network traffic generated by massive different applications. Some packet-based or flowlet-based schemes capture traffic bursts for load balancing. But frequent rerouting within a flow can mix ACKs belonging to different paths in congestion control protocols, which adversely affects flow rate control. Besides, performance optimization effect of flowlet-based schemes may be less noticeable under smoother workloads. And several packet-based mechanisms implemented at end hosts can proactively reroute congested flows based on flow status even under a smooth workload, but fail to improve performance with the bursty nature of traffic. Therefore, existing schemes cannot adapt to different burst levels of dynamic traffic in datacenter networks and still have significant performance flaws in some ways. This paper proposes IntFlow, a novel load balancing scheme that integrates end-host based per-packet monitoring of flow status with flowlet switching in programable switches. IntFlow proactively reroutes flows experiencing network congestion or failures and avoids doing flowlet switching for small flows with high sending rate. IntFlow can provide excellent performance under both high burst and smooth workloads. Finally experimental results show IntFlow achieves up to 32% and 28% better performance than CONGA and Hermes under asymmetries, respectively.

Optimising Self-Similarity Network Traffic for Better Performance

Given the ubiquity of the burstiness present across many networking facilities and services, predicting and managing self-similar traffic has become a key issue owing to new complexities associated with self-similarity which makes difficult the achievement of high network performance and quality of service (QoS). In this study ANN model was used to model and simulate FCE Okene computer network traffic. The ANN is a 2-39-1 Feed Forward Backpropagation network implemented to predict the bursty nature of network traffic. Wireshark tools that measure and capture packets of network traffic was deployed. Moreover, variance-time method is a log-log scale plot, representing variance versus a non-overlapping block of size m aggregate variance level engaged to established conformity of the ANN approach to self-similarity characteristic of the network traffic. The predicted series were then compared with the corresponding real traffic series. Suitable performance measurements used were the Means Square Error (MSE) and the Regression Coefficient. Our results showed that burstiness is present in the network across many time scales. The study also established the characteristic property of a long-range dependence (LRD). The work recommended that network traffic observation should be longer thereby enabling larger volume of traffic to be capture for better accuracy of traffic modelling and prediction.

Time-Dependent Pricing for Bandwidth Slicing Under Information Asymmetry and Price Discrimination

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP’s revenue and reducing the peak-to-average traffic ratio (PATR).

The Critical Role of Collisionless Plasma Energization on the Structure of Relativistic Magnetic Reconnection

Abstract During magnetically dominated relativistic reconnection, inflowing plasma depletes the initial relativistic pressure at the x-line and collisionless plasma heating inside the diffusion region is insufficient to overcome this pressure loss. The resulting significant pressure drop causes a collapse at the x-line, essentially a localization mechanism of the diffusion region necessary for fast reconnection. The extension of this low-pressure region (into the outflow) further explains the bursty nature of antiparallel reconnection because a once opened outflow exhaust can also collapse, which repeatedly triggers secondary tearing islands. However, a stable single x-line reconnection can be achieved when an external guide field exists, since the reconnecting magnetic field component rotates out of the reconnection plane at outflows, providing additional magnetic pressure to keep the exhaust open.

Model of Load Distribution Between Web Proxy Servers Using Network Traffic Analysis

Proxy servers are installed to give information access services to requested client nodes or units individually. As of now, in the present framework intermediaries are confronting the performance bottleneck that is when numerous clients get to the Internet all the while since such servers have a constrained limit. Web servers often experience and face overload situations due to the extremely bursty nature of traffic caused on the Internet. Our concentration in the work is to propose successful load control instruments for web servers. A significant angle in the load control is to limit the work spent by the proxy server to distribute the load and pass on to the main servers. This research work proposes a technique for load balancing on proxy servers by investigating the load on the network between servers and proxy servers. The research investigation is based on calculating the bits per second movements of packet and sockets with respect to the ports such as HTTP/HTTPS and TCP. This proposed method increases the performance by reducing the load on the proxy servers.

Enhancing Cloud-Based IoT/M2M System Scalability by Dynamic Network Slicing

With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research proposes a dynamic SDN-based network slicing mechanism to tackle the scalability problems caused by such heterogeneity and fluctuation of IoT application requirements. The proposed method can automatically create a network slice on-the-fly for each new type of IoT application and adjust the QoS characteristics of the slice dynamically according to the changing requirements of an IoT application. Validated with extensive experiments, the proposed mechanism demonstrates better platform scalability when compared to a static slicing system.