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Programmers spend a significant amount of time reading code during the software development process. This trend is amplified by the emergence of large language models (LLMs) that automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from practitioners' perspectives in this new era. In this paper, we conduct a survey to explore the practitioners' perspectives on code readability in the age of LLMs and investigate the readability of our LLM-based software development agents framework, HULA, by comparing its generated code with human-written code in real-world scenarios. Overall, the findings underscore that (1) readability remains a critical aspect of software development; (2) the readability of our LLM-generated code is comparable to human-written code, fostering the establishment of appropriate trust and driving the broad adoption of our LLM-powered software development platform., Comment: 6 pages, 2 figures, 5 tables, under review

JWST has enabled the detection of the UV continuum of galaxies at z>10, evidencing a population of extremely blue, potentially dust-free galaxies. Interpreting the UV spectra of galaxies as they redden is complicated by the well-known degeneracy between stellar ages, dust, and nebular continuum. The main goal of this paper is to develop a theoretical model for the relationship between galaxy UV slopes, bursty star formation histories, dust evolution, and the contribution from nebular regions. We accomplish this via cosmological zoom-in simulations, and in specific, build a layered model where we simulate the UV slopes of galaxies with increasingly complex physics. Our main results follow. (i) Unattenuated stellar populations with no nebular emission exhibit a diverse range of intrinsic UV slopes, with values ranging from beta ~ -3 --> -2.2 due to long delays between bursts. This is manifested by an inverse correlation between the intrinsic UV slope and sSFR for early galaxies such that higher sSFR corresponds to bluer UV slopes. (ii) When including dust, our model galaxies demonstrate a rapid rise in dust obscuration between z ~ 8-10. This increase in dust mass is due to high grain-grain shattering rates, and enhanced growth per unit dust mass in very small grains, resulting in UV-detected galaxies at z ~ 12 descending into ALMA-detectable galaxies by z ~ 6. The rapid rise in dust content at z ~ 8-10 leads to a systematic reddening of the UV slopes during this redshift range. (iii) The inclusion of nebular continuum reddens the UV slope by a median factor Delta beta ~ 0.2-0.4. However, when including nebular continuum, our highest redshift galaxies (z~12) are insufficiently blue compared to observations; this may imply an evolving escape fraction from HII regions with redshift., Comment: Submitted to ApJ; comments welcome

Large-scale magnetic fields are observed off the midplanes of disk galaxies, indicating that they harbour magnetised halos. These halos are crucial to studies of galaxy evolution, galactic-scale outflows, and feedback from star formation activity. Identifying the magnetised halo of the Milky Way is challenging because of the potential contamination from foreground emission arising in local spiral arms. Additionally, it is unclear how our magnetic halo is influenced by recently revealed large-scale structures such as the X-ray emitting eROSITA Bubbles, which, according to previous simulations, might be transient structures powered by the Galactic Center or the Galaxy's star-forming ring. Here we report the identification of several kpc-scale magnetised structures based on their polarized radio emission and their gamma-ray counterparts, which can be interpreted as the radiation of relativistic electrons. These non-thermal structures extend far above and below the Galactic plane and are spatially coincident with the thermal X-ray emission from the eROSITA Bubbles. The morphological consistency of these structures suggests a common origin, which can be sustained by Galactic outflows driven by the active star-forming regions located at 3-5 kpc from the Galactic Centre. These results reveal how X-ray-emitting and magnetised halos of spiral galaxies can be related to intense star formation activities and suggest that the X-shaped coherent magnetic structures observed in their halos can stem from galaxy outflows., Comment: Initially submitted on March 2nd, 2024

A muon collider represents a promising candidate for the next generation of particle physics experiments after the expected end of LHC operations in the early 2040s. Rare or hard-to-detect processes at the LHC, such as the production of multiple gauge bosons, become accessible at a TeV muon collider. We present here the prospects of detecting quantum entanglement and the violation of Bell inequalities in H to ZZ to 4l events at a potential future muon collider. We show that the spin density matrix of the Z boson pairs can be reconstructed using the kinematics of the charged leptons from the Z boson decays. Once the density matrix is determined, it is straightforward to obtain the expectation values of various Bell operators and test the quantum entanglement between the Z boson pair. Through a detailed study based on Monte-Carlo simulation, we show that the generalized CGLMP inequality can be maximally violated, and testing Bell inequalities could be established with high significance., Comment: 11 pages, 3 figures, updated version

In this study, we assess the effectiveness and robustness of the recently proposed $T'$-expansion scheme for expanding the equation of state of strongly interacting matter to finite density, by comparing its performance relative to the conventional Taylor expansion method in various effective QCD models. We use baryon number density and its susceptibilities to calculate the expansion coefficients in the $T'$-expansion scheme with and without the Stefan-Boltzmann limit correction. Our methodology involves comparing truncation orders to exact solutions to assess the scheme's accuracy. We utilize Ideal, Excluded Volume, and van der Waals formulations of the Hadron Resonance Gas (HRG) model at low temperatures, and the Cluster Expansion Model at higher temperatures. Our findings indicate that the $T'$-expansion scheme offers superior convergence properties near and above the chiral crossover temperature, where the chiral-criticality-inspired scaling $(\partial/ \partial T)_{\mu_B} \sim (\partial^2/\partial \mu_B^2)_T$ holds. However, it shows limited improvement in the HRG models, indicating that it may not be the most suitable choice for describing the hadronic phase., Comment: 11 pages, 4 figures

Attosecond science offers unprecedented precision in probing the initial moments of chemical reactions, revealing the dynamics of molecular electrons that shape reaction pathways. A fundamental question emerges: what role, if any, do quantum coherences between molecular electron states play in photochemical reactions? Answering this question necessitates quantum tomography: the determination of the electronic density matrix from experimental data, where the off-diagonal elements represent these coherences. The Maximal Entropy (MaxEnt) based Quantum State Tomography (QST) approach offers unique advantages in studying molecular dynamics, particularly with partial tomographic data. Here, we explore the application of MaxEnt-based QST on photoexcited ammonia, necessitating the operator form of observables specific to the performed measurements. We present two methodologies for constructing these operators: one leveraging Molecular Angular Distribution Moments (MADMs) which accurately capture the orientation-dependent vibronic dynamics of molecules; and another utilizing Angular Momentum Coherence Operators to construct measurement operators for the full rovibronic density matrix in the symmetric top basis. A key revelation of our study is the direct link between Lagrange multipliers in the MaxEnt formalism and the unique set of MADMs. Furthermore, we achieve a groundbreaking milestone by constructing, for the first time, the entanglement entropy of the electronic subsystem: a metric that was previously inaccessible. The entropy vividly reveals and quantifies the effects of coupling between the excited electron and nuclear degrees of freedom. Consequently, our findings open new avenues for research in ultrafast molecular spectroscopy within the broader domain of quantum information science., Comment: 18 pages, 4 figures

The X$_2$MH$_6$ family, consisting of an electropositive cation X and a main group metal M octahedrally coordinated by hydrogen, has been predicted to hold promise for high-temperature conventional superconductivity. Herein, we analyze the electronic structure of two members of this family, Mg$_2$IrH$_6$ and Ca$_2$IrH$_6$, showing why the former may possess superconducting properties rivaling those of the cuprates, whereas the latter does not. Within Mg$_2$IrH$_6$ the vibrations of the IrH$_6^{4-}$ anions are key for the superconducting mechanism, and they induce coupling in the $e_g^*$ set, which are antibonding between the H 1$s$ and the Ir $d_{x^2-y^2}$ or $d_{z^2}$ orbitals. Because calcium possesses low-lying d-orbitals, $e_g^* \rightarrow$ Ca $d$ back-donation is preferred, quenching the superconductivity. Our analysis explains why high critical temperatures were only predicted for second or third row X metal atoms, and may hold implications for superconductivity in other systems where the antibonding anionic states are filled.

For fast-tracking climate change response, green hydrogen is key for achieving greenhouse gas neutral energy systems. Especially Sub-Saharan Africa can benefit from it enabling an increased access to clean energy through utilizing its beneficial conditions for renewable energies. However, developing green hydrogen strategies for Sub-Saharan Africa requires highly detailed and consistent information ranging from technical, environmental, economic, and social dimensions, which is currently lacking in literature. Therefore, this paper provides a comprehensive novel approach embedding the required range of disciplines to analyze green hydrogen cost-potentials in Sub-Saharan Africa. This approach stretches from a dedicated land eligibility based on local preferences, a location specific renewable energy simulation, locally derived sustainable groundwater limitations under climate change, an optimization of local hydrogen energy systems, and a socio-economic indicator-based impact analysis. The capability of the approach is shown for case study regions in Sub-Saharan Africa highlighting the need for a unified, interdisciplinary approach.

We study the effect of geometric frustration on dilational mechanical metamaterial membranes. While shape frustrated elastic plates can only accommodate non-zero Gaussian curvature up to size scales that ultimately vanish with their elastic thickness, we show that frustrated {\it metamembranes} accumulate hyperbolic curvatures up to mesoscopic length scales that are ultimately independent of the size of their microscopic constituents. A continuum elastic theory and discrete numerical model describe the size-dependent shape and internal stresses of axisymmetric, trumpet-like frustrated metamembranes, revealing a non-trivial crossover to a much weaker power-law growth in elastic strain energy with size than in frustrated elastic membranes. We study a consequence of this for the self-limiting assembly thermodynamics of frustrated trumpets, showing a several-fold increase the size range of self-limitation of metamembranes relative to elastic membranes., Comment: 6 pages, 3 figures, supplementary text appendices

The design and development of power electronics converters pose a multitude of challenges. The evaluation of power electronics converters, particularly when operating at high power levels, presents a significant task, offering designers a deeper understanding of the functionality. Several methodologies have been devised to conduct hardware-in-the-loop (HIL) tests, which are classified into two main categories: controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) tests. This paper explores the advantages and drawbacks of these two approaches and introduces a straightforward and cost-effective CHIL method for initial proof of concept and risk-free controller training. This method is based on the interaction between MATLAB/Simulink and Python. To assess the operation of the proposed system, a modeled battery pack (96S1P) is charged using a modeled battery charger converter, employing a non-linear PID controller. In this scenario, the controller benefits from the CC-CV technique for charging the battery pack. The results are presented in the final section., Comment: 2024 IEEE Transportation Electrification Conference & Expo (ITEC 2024) (5 pages, 11 figures)

With the advent of smart grid (SG) systems, electricity networks have been able to ensure greater efficiency and utility by interconnecting their grids through cloud-based technology. As SGs become increasingly complex, a wide range of security challenges arise, threatening the grid's reliability, safety, efficiency, and stability. The security challenges include the potential exposure of personal data due to hackers intercepting the communications between the SG infrastructure and the smart meters. Security awareness plays a vital role in addressing some of these challenges. However, the traditional training programs are no longer efficient for instilling information security culture in organisations or from an individual user perspective. Gamification is a new concept in the field of information security awareness training (SAT) campaigns that can be introduced to fill in this gap by providing employees with a means of practising and learning about many security flaws and risks that exist within the organisation. Thus, this paper examines the effectiveness of gamification in promoting security awareness among smart meter components for smart grid users/operators. A gaming application is developed as part of the study with the aim of training and evaluating the results through three difficulty levels of questionnaires. Furthermore, the results are evaluated for the three difficulty levels as well as the overall flag captured. It can be demonstrated that the scores of participants in the three levels have improved by 40%, 35% and 29%, respectively. This reflects the awareness of learning within our system., Comment: 18 pages, 15 figures and 2 tables

Adapting language models (LMs) to novel domains is often achieved through fine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning introduces new knowledge into an LM, enabling it to comprehend and efficiently perform a target domain task. Fine-tuning can however be inadvertently insensitive if it ignores the wide array of disparities (e.g in word meaning) between source and target domains. For instance, words such as chronic and pressure may be treated lightly in social conversations, however, clinically, these words are usually an expression of concern. To address insensitive fine-tuning, we propose Mask Specific Language Modeling (MSLM), an approach that efficiently acquires target domain knowledge by appropriately weighting the importance of domain-specific terms (DS-terms) during fine-tuning. MSLM jointly masks DS-terms and generic words, then learns mask-specific losses by ensuring LMs incur larger penalties for inaccurately predicting DS-terms compared to generic words. Results of our analysis show that MSLM improves LMs sensitivity and detection of DS-terms. We empirically show that an optimal masking rate not only depends on the LM, but also on the dataset and the length of sequences. Our proposed masking strategy outperforms advanced masking strategies such as span- and PMI-based masking., Comment: Paper alrerady accepted for publishing by the NAACL 2024 conference (main conference paper)

We present a novel construction of the QCD equation of state (EoS) at finite baryon density. Our work combines a recently proposed resummation scheme for lattice QCD results with the universal critical behavior at the QCD critical point. This allows us to obtain a family of equations of state in the range $0 \leq \mu_B \leq 700$ MeV and 25 MeV $\leq T \leq 800$ MeV, which match lattice QCD results near $\mu_B=0$ while featuring a critical point in the 3D Ising model universality class. The position of the critical point can be chosen within the range accessible to beam-energy scan heavy-ion collision experiments. The strength of the singularity and the shape of the critical region are parameterized using a standard parameter set. We impose stability and causality constraints and discuss the available ranges of critical point parameter choices, finding that they extend beyond earlier parametric QCD EoS proposals. We present thermodynamic observables, including baryon density, pressure, entropy density, energy density, baryon susceptibility and speed of sound, that cover a wide range in the QCD phase diagram relevant for experimental exploration., Comment: 16 pages, 18 figures

The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on the performance of computer vision in vehicle automation, for many public datasets, this metric is unknown. Additionally, wide field-of-view (FOV) cameras have become increasingly popular, particularly for low-speed vehicle automation applications. To investigate image quality in datasets, this paper proposes an adaptation of the Natural Scenes Spatial Frequency Response (NS-SFR) algorithm to suit cameras with a wide field-of-view., Comment: Accepted for publication in the Electronic Imagine Autonomous Vehicles and Machines (EI-AVM) Conference 2024

The Internet of Things (IoT) technology uses small and cost-effective sensors for various applications, such as Industrial IoT. However, these sensor nodes are powered by fixed-size batteries, which creates a trade-off between network performance and long-term sustainability. Moreover, some applications require the network to provide a certain level of service, such as a lower delay for critical data, while ensuring the operational reliability of sensor nodes. To address this energy challenge, external energy harvesting sources, such as solar and wind, offer promising and eco-friendly solutions. However, the available energy from a single energy source is insufficient to meet these requirements. This drives the utilization of a hybrid energy harvesting approach, such as the integration of solar and wind energy harvesters, to increase the amount of harvested energy. Nevertheless, to fully utilize the available energy, which is dynamic in nature, the sensor node must adapt its operation to ensure sustainable operation and enhanced network performance. Therefore, this paper proposes a hybrid energy harvesting-based energy neutral operation (ENO) medium access control (MAC) protocol, called HENO-MAC, that allows the receiver node to harvest energy from the solar-wind harvesters and adapt its duty cycle accordingly. The performance of the proposed HENO-MAC was evaluated using the latest realistic solar and wind data for two consecutive days in GreenCastalia. The simulation results demonstrate that the duty cycle mechanism of HENO-MAC effectively utilizes the harvested energy to achieve ENO and uses the available energy resources efficiently to reduce the packet delay for all packets and the highest priority packet by up to 28.5% and 27.3%, respectively, when compared with other existing MAC protocols., Comment: This paper has been accepted for presentation at the IEEE Wireless Communications and Networking Conference (WCNC) 2024

The BEST Collaboration equation of state combining lattice data with the 3D Ising critical point encounters limitations due to the truncated Taylor expansion up to $\frac{\mu_B}{T} \sim 2.5$. This truncation consequently restricts its applicability at high densities. Through a resummation scheme, the lattice results have been extended to $\frac{\mu_B}{T} = 3.5$. In this article, we amalgamate these ideas with the 3D-Ising model, yielding a family of equations of state valid up to $\mu_B=700 \text{MeV}$ with the correct critical behavior. Our equations of state feature tunable parameters, providing a stable and causal framework-a crucial tool for hydrodynamics simulations., Comment: 4 pages, 2 figures, Contribution to Quark Matter2023 (Houston, TX, 3-9 Sep. 2023)

The complexity and scale of IT systems are increasing dramatically, posing many challenges to real-world anomaly detection. Deep learning anomaly detection has emerged, aiming at feature learning and anomaly scoring, which has gained tremendous success. However, little work has been done on the thresholding problem despite it being a critical factor for the effectiveness of anomaly detection. In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network. The proposed method can be integrated into many systems that require dynamic thresholding. An auto-encoder is utilized in this study to obtain feature representations and produce anomaly scores for complex input data. ADT can adjust thresholds adaptively by utilizing the anomaly scores from the auto-encoder and significantly improve anomaly detection performance. The properties of ADT are studied through experiments on three real-world datasets and compared with benchmarks, hence demonstrating its thresholding capability, data-efficient learning, stability, and robustness. Our study validates the effectiveness of reinforcement learning in optimal thresholding control in anomaly detection.

Tiger conservation necessitates the strategic deployment of multifaceted initiatives encompassing the preservation of ecological habitats, anti-poaching measures, and community involvement for sustainable growth in the tiger population. With the advent of artificial intelligence, tiger surveillance can be automated using object detection. In this paper, an accurate illumination invariant framework is proposed based on EnlightenGAN and YOLOv8 for tiger detection. The fine-tuned YOLOv8 model achieves a mAP score of 61% without illumination enhancement. The illumination enhancement improves the mAP by 0.7%. The approaches elevate the state-of-the-art performance on the ATRW dataset by approximately 6% to 7%., Comment: accepted at ICCIS 2023

The $\phi$-divergence-based moment method was recently introduced Abdelmalik et al. (2023) for the discretization of the radiative transfer equation. At the continuous level, this method is very close to the entropy-based MN methods and possesses its main properties, i.e. entropy dissipation, rotational invariance and energy conservation. However, the $\phi$-divergence based moment systems are easier to resolve numerically due to the improved conditioning of the discrete equations. Moreover, exact quadrature rules can be used to compute moments of the distribution function, which enables the preservation of energy conservation, entropy dissipation and rotational invariants, discretely. In this paper we consider different variants of the $\phi$-divergence closures that are based on different approximations of the exponential function and the Planck function. We compare the approximation properties of the proposed closures in the numerical benchmarks.

State-of-the-art deep CNN face matchers are typically created using extensive training sets of color face images. Our study reveals that such matchers attain virtually identical accuracy when trained on either grayscale or color versions of the training set, even when the evaluation is done using color test images. Furthermore, we demonstrate that shallower models, lacking the capacity to model complex representations, rely more heavily on low-level features such as those associated with color. As a result, they display diminished accuracy when trained with grayscale images. We then consider possible causes for deeper CNN face matchers "not seeing color". Popular web-scraped face datasets actually have 30 to 60% of their identities with one or more grayscale images. We analyze whether this grayscale element in the training set impacts the accuracy achieved, and conclude that it does not. We demonstrate that using only grayscale images for both training and testing achieves accuracy comparable to that achieved using only color images for deeper models. This holds true for both real and synthetic training datasets. HSV color space, which separates chroma and luma information, does not improve the network's learning about color any more than in the RGB color space. We then show that the skin region of an individual's images in a web-scraped training set exhibits significant variation in their mapping to color space. This suggests that color carries limited identity-specific information. We also show that when the first convolution layer is restricted to a single filter, models learn a grayscale conversion filter and pass a grayscale version of the input color image to the next layer. Finally, we demonstrate that leveraging the lower per-image storage for grayscale to increase the number of images in the training set can improve accuracy of the face recognition model., Comment: This is replacement version of the previous arxiv submission: 2309.05180 (Our Deep CNN Face Matchers Have Developed Achromatopsia). The past version is published in CVPRW and available in IEEE proceedings. This submitted version is an extension of the conference paper

Matrix/array analysis of networks can provide significant insight into their behavior and aid in their operation and protection. Prior work has demonstrated the analytic, performance, and compression capabilities of GraphBLAS (graphblas.org) hypersparse matrices and D4M (d4m.mit.edu) associative arrays (a mathematical superset of matrices). Obtaining the benefits of these capabilities requires integrating them into operational systems, which comes with its own unique challenges. This paper describes two examples of real-time operational implementations. First, is an operational GraphBLAS implementation that constructs anonymized hypersparse matrices on a high-bandwidth network tap. Second, is an operational D4M implementation that analyzes daily cloud gateway logs. The architectures of these implementations are presented. Detailed measurements of the resources and the performance are collected and analyzed. The implementations are capable of meeting their operational requirements using modest computational resources (a couple of processing cores). GraphBLAS is well-suited for low-level analysis of high-bandwidth connections with relatively structured network data. D4M is well-suited for higher-level analysis of more unstructured data. This work demonstrates that these technologies can be implemented in operational settings., Comment: Accepted to IEEE HPEC, 8 pages, 8 figures, 1 table, 69 references. arXiv admin note: text overlap with arXiv:2203.13934. text overlap with arXiv:2309.01806

Defending community-owned cyber space requires community-based efforts. Large-scale network observations that uphold the highest regard for privacy are key to protecting our shared cyberspace. Deployment of the necessary network sensors requires careful sensor placement, focusing, and calibration with significant volumes of network observations. This paper demonstrates novel focusing and calibration procedures on a multi-billion packet dataset using high-performance GraphBLAS anonymized hypersparse matrices. The run-time performance on a real-world data set confirms previously observed real-time processing rates for high-bandwidth links while achieving significant data compression. The output of the analysis demonstrates the effectiveness of these procedures at focusing the traffic matrix and revealing the underlying stable heavy-tail statistical distributions that are necessary for anomaly detection. A simple model of the corresponding probability of detection ($p_{\rm d}$) and probability of false alarm ($p_{\rm fa}$) for these distributions highlights the criticality of network sensor focusing and calibration. Once a sensor is properly focused and calibrated it is then in a position to carry out two of the central tenets of good cybersecurity: (1) continuous observation of the network and (2) minimizing unbrokered network connections., Comment: Accepted to IEEE HPEC, 9 pages, 12 figures, 1 table, 63 references, 2 appendices

Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the interruption of the supply chain in 2022 led to a severe contrast media shortage in the US. Both factors justify the necessity of contrast media reduction in relevant clinical applications. In this study, we propose a diffusion model-based deep learning framework to address this challenge. First, we simulate different levels of low contrast dosage DECT scans from the standard normal contrast dosage DECT scans using material decomposition. Conditional denoising diffusion probabilistic models are then trained to enhance the contrast media and create contrast-enhanced images. Our results demonstrate that the proposed methods can generate high-quality contrast-enhanced results even for images obtained with as low as 12.5% of the normal contrast dosage. Furthermore, our method outperforms selected competing methods in a human reader study.

Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NLP community. External knowledge often adopted to enhance KG embeddings ranges from semantically rich lexical dependency parsed features to a set of relevant key words to entire text descriptions supplied from an external corpus such as wikipedia and many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made, the proposal in this work suggests that it can be improved even further. Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions. Different from prior work that plugs formal entity descriptions declared in knowledge bases, this framework leverages a retriever model to selectively identify richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the framework treats the number of descriptions to use in augmentation process as a parameter, which allows the flexibility of enumerating across several numbers before identifying an appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively, in comparison to text-enhanced knowledge graph augmentation methods using traditional CNNs., Comment: Article has already been puclished to Journal of Artificial Intelligence Research (JAIR)

Nationally approved emission factors of mass versus particle size for particulate emissions from UK MSW incinerators when converted to particle number versus size, indicate that nearly all (>90\%) of the emitted particles are ultra fine particles (ufps) < .1 micron in size. A similar result is true also of US MSW incinerators emissions. This would imply that the bag/fiber filters used for the removal of particles produced in the incineration process have a very low efficiency for the removal of ufps. This result is at variance with recent assertions that bag filters have a high removal efficiency for ufps. An analysis of fiber filter retention based on the fundamental mechanisms for the deposition of small particles to single filter fibers and their dependence on particle size, shows that whilst the removal efficiency is 100 % for particles << . 1 micron, there is a minimum of the filter retention efficiency in the region 0.05 to 0.5 microns where the concentration of the ufps is most likely to be greatest. In some cases depending on the flow and particle size, the filter efficiency is as low as 5% compared to almost 100% retention efficiency for particles > 1 micron (within the inertial impaction range of particles). It is believed this explains the very high release rates \sim10^{14} particles/s from these incinerators., Comment: 15 pages, 8 figures, 2 tables

This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics results, as well as observations from neutron stars and their mergers. The validity of different constraints, concerning specific conditions and ranges of applicability, is also provided.

Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel. However, current methods do not scale to large batch sizes -- a frequent desideratum in practice (e.g. drug discovery or simulation-based inference). We present a novel algorithm, SOBER, which permits scalable and diversified batch global optimisation and quadrature with arbitrary acquisition functions and kernels over discrete and mixed spaces. The key to our approach is to reformulate batch selection for global optimisation as a quadrature problem, which relaxes acquisition function maximisation (non-convex) to kernel recombination (convex). Bridging global optimisation and quadrature can efficiently solve both tasks by balancing the merits of exploitative Bayesian optimisation and explorative Bayesian quadrature. We show that SOBER outperforms 11 competitive baselines on 12 synthetic and diverse real-world tasks., Comment: 34 pages, 12 figures

Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging. It is non-trivial to extend an algorithm developed for flat images to the spherical ones. In this work, we focus on the challenging task of spherical image inpainting with deep learning-based regularizer. Instead of a naive application of existing models for planar images, we employ a fast directional spherical Haar framelet transform and develop a novel optimization framework based on a sparsity assumption of the framelet transform. Furthermore, by employing progressive encoder-decoder architecture, a new and better-performed deep CNN denoiser is carefully designed and works as an implicit regularizer. Finally, we use a plug-and-play method to handle the proposed optimization model, which can be implemented efficiently by training the CNN denoiser prior. Numerical experiments are conducted and show that the proposed algorithms can greatly recover damaged spherical images and achieve the best performance over purely using deep learning denoiser and plug-and-play model.

Internet analysis is a major challenge due to the volume and rate of network traffic. In lieu of analyzing traffic as raw packets, network analysts often rely on compressed network flows (netflows) that contain the start time, stop time, source, destination, and number of packets in each direction. However, many traffic analyses benefit from temporal aggregation of multiple simultaneous netflows, which can be computationally challenging. To alleviate this concern, a novel netflow compression and resampling method has been developed leveraging GraphBLAS hyperspace traffic matrices that preserve anonymization while enabling subrange analysis. Standard multitemporal spatial analyses are then performed on each subrange to generate detailed statistical aggregates of the source packets, source fan-out, unique links, destination fan-in, and destination packets of each subrange which can then be used for background modeling and anomaly detection. A simple file format based on GraphBLAS sparse matrices is developed for storing these statistical aggregates. This method is scale tested on the MIT SuperCloud using a 50 trillion packet netflow corpus from several hundred sites collected over several months. The resulting compression achieved is significant (<0.1 bit per packet) enabling extremely large netflow analyses to be stored and transported. The single node parallel performance is analyzed in terms of both processors and threads showing that a single node can perform hundreds of simultaneous analyses at over a million packets/sec (roughly equivalent to a 10 Gigabit link)., Comment: arXiv admin note: text overlap with arXiv:2203.13934, arXiv:2108.06653, arXiv:2008.00307

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and application in multiple fields such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efficiency in real-world applications. In this work, we survey the state-of-the-art methods that target long-term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS., Comment: 20 pages, 10 figures. Submitted to IEEE T-RO survey paper

In this paper, we present a semi-implicit numerical solver for a first order hyperbolic formulation of two-phase flow with surface tension and viscosity. The numerical method addresses several complexities presented by the PDE system in consideration: (i) The presence of involution constraints of curl type in the governing equations requires explicit enforcement of the zero-curl property of certain vector fields (an interface field and a distortion field); the problem is eliminated entirely by adopting a set of compatible curl and gradient discrete differential operators on a staggered grid, allowing to preserve the Schwartz identity of cross-derivatives exactly. (ii) The evolution equations feature highly nonlinear stiff algebraic source terms which are used for the description of viscous interactions as emergent behaviour of an elasto-plastic solid in the stiff strain relaxation limit; such source terms are reliably integrated with an efficient semi-analytical technique. (iii) In the low-Mach number regime, standard explicit Godunov-type schemes lose efficiency and accuracy; the issue is addressed by means of a simple semi-implicit, pressure-based, split treatment of acoustic and non-acoustic waves, again using staggered grids that recover the implicit solution for a single scalar field (the pressure) through a sequence of symmetric-positive definite linear systems that can be efficiently solved via the conjugate gradient method.

New spectrometric (HERCULES) and ground-based multi-colour photometric data on the multiple star V410 Puppis are combined with satellite photometry (HIPPARCOS and TESS), as well as historic astrometric observations. Absolute parameters for V410 Pup Aab are derived: $M_{Aa}$ = $3.15 \pm 0.10$, $M_{Ab}$ = $1.83 \pm 0.08$ (M$_{\odot}$); $R_{Aa}$ = $2.12 \pm 0.10$, $R_{Ab}$ = $1.52 \pm 0.08$ (R$_\odot$); $a$ = $6.57 \pm 0.04$ R$_\odot$; $T_{Aa}$ = $12500 \pm 1000$, $T_{Ab}$ = $9070 \pm 800$ (K), and photometric distance $350 \pm 10$ (pc). We report the discovery of a low-amplitude SPB variation in the light curve and also indications of an accretion structure around V410 Pup B as well as emission cores in V410 Pup C. We argue that V410 Pup is probably a young formation connected with the Vela 2 OB Association. The combined evidence allows an age in the range 7-25 Myr from comparisons with standard stellar evolution modelling., Comment: 14 pages, 15 figures, 12 tables. MNRAS (accepted)

A proportional iterative learning control (P-ILC) for linear models of an existing hybrid stroke rehabilitation scheme is implemented for elbow extension/flexion during a rehabilitative task. Owing to transient error growth problem of P-ILC, a learning derivative constraint controller was included to ensure that the controlled system does not exceed a predefined velocity limit at every trial. To achieve this, linear transfer function models of the robot end-effector interaction with a stroke subject (plant) and muscle response to stimulation controllers were developed. A straight-line point-point trajectory of 0 - 0.3 m range served as the reference task space trajectory for the plant, feedforward, and feedback stimulation controllers. At each trial, a SAT-based bounded error derivative ILC algorithm served as the learning constraint controller. Three control configurations were developed and simulated. The system performance was evaluated using the root means square error (RMSE) and normalized RMSE. At different ILC gains over 16 iterations, a displacement error of 0.0060 m was obtained when control configurations were combined., Comment: 15 pages, 16 Figures

This study uses supervised machine learning (SML) and explainable artificial intelligence (AI) to model, predict and understand human decision-making during skillful joint-action. Long short-term memory networks were trained to predict the target selection decisions of expert and novice actors completing a dyadic herding task. Results revealed that the trained models were expertise specific and could not only accurately predict the target selection decisions of expert and novice herders but could do so at timescales that preceded an actor's conscious intent. To understand what differentiated the target selection decisions of expert and novice actors, we then employed the explainable-AI technique, SHapley Additive exPlanation, to identify the importance of informational features (variables) on model predictions. This analysis revealed that experts were more influenced by information about the state of their co-herders compared to novices. The utility of employing SML and explainable-AI techniques for investigating human decision-making is discussed.

The paper presents a receding horizon planning and control strategy for quadrotor-type \ac{mav}s to navigate reactively and intercept a moving target in a cluttered unknown and dynamic environment. Leveraging a lightweight short-range sensor that generates a point-cloud within a relatively narrow and short \ac{fov}, and an \acs{ssd}-MobileNet based Deep neural network running on board the \ac{mav}, the proposed motion planning and control strategy produces safe and dynamically feasible \ac{mav} trajectories within the sensor \acs{fov}, which the vehicle uses to autonomously navigate, pursue, and intercept its moving target. This task is completed without reliance on a global planner or prior information about the environment or the moving target. The effectiveness of the reported planner is demonstrated numerically and experimentally in cluttered indoor and outdoor environments featuring maximum speeds of up to 4.5-5~m/s.

We report the discovery of a double-peaked Lyman-$\alpha$ (Ly$\alpha$) emitter (LAE) at $z=3.2177\pm0.0001$ in VLT/MUSE data. The galaxy is strongly lensed by the galaxy cluster RXC~J0018.5+1626 recently observed in the RELICS survey, and the double-peaked Ly$\alpha$ emission is clearly detected in the two counter images in the MUSE field-of-view. We measure a relatively high Ly$\alpha$ rest-frame equivalent width (EW) of $\mathrm{EW}_{\mathrm{Ly}\alpha,0}=(63\pm2)\,\mathring{\mathrm{A}}$. Additional near-infrared (NIR) spectroscopy allows us to measure the H$\beta$, [OIII]$\lambda4959\,\mathring{\mathrm{A}}$ and [OIII]$\lambda5007\,\mathring{\mathrm{A}}$ emission lines, which show moderate rest-frame EWs of the order of a few $\sim10-100\,\mathring{\mathrm{A}}$, an [OIII]$\lambda5007\,\mathring{\mathrm{A}}$/H$\beta$ ratio of $4.8\pm0.7$, and a lower limit on the [OIII]/[OII] ratio of $>5.6$. The galaxy has very blue UV-continuum slopes of $\beta_{\mathrm{FUV}}=-2.23\pm0.06$ and $\beta_{\mathrm{NUV}}=-3.0\pm0.2$, and is magnified by factors $\mu\sim7-10$ in each of the two images, thus enabling a view into a low-mass ($M_{\star}\simeq10^{7.5}\,\mathrm{M}_{\odot}$) high-redshift galaxy analog. Notably, the blue peak of the Ly$\alpha$ profile is significantly stronger than the red peak, which suggests an inflow of matter and possibly very low HI column densities in its circumgalactic gas. Combined with the high lensing magnification and image multiplicity, these properties make this galaxy a prime candidate for follow-up observations to search for LyC emission and constrain the LyC photon escape fraction., Comment: Updated to match published version

Long range detection is a cornerstone of defense in many operating domains (land, sea, undersea, air, space, ..,). In the cyber domain, long range detection requires the analysis of significant network traffic from a variety of observatories and outposts. Construction of anonymized hypersparse traffic matrices on edge network devices can be a key enabler by providing significant data compression in a rapidly analyzable format that protects privacy. GraphBLAS is ideally suited for both constructing and analyzing anonymized hypersparse traffic matrices. The performance of GraphBLAS on an Accolade Technologies edge network device is demonstrated on a near worse case traffic scenario using a continuous stream of CAIDA Telescope darknet packets. The performance for varying numbers of traffic buffers, threads, and processor cores is explored. Anonymized hypersparse traffic matrices can be constructed at a rate of over 50,000,000 packets per second; exceeding a typical 400 Gigabit network link. This performance demonstrates that anonymized hypersparse traffic matrices are readily computable on edge network devices with minimal compute resources and can be a viable data product for such devices., Comment: Accepted to IEEE HPEC, Outstanding Paper Award, 8 pages, 8 figures, 1 table, 70 references. arXiv admin note: text overlap with arXiv:2108.06653, arXiv:2008.00307, arXiv:2203.10230

The Internet has become a critical component of modern civilization requiring scientific exploration akin to endeavors to understand the land, sea, air, and space environments. Understanding the baseline statistical distributions of traffic are essential to the scientific understanding of the Internet. Correlating data from different Internet observatories and outposts can be a useful tool for gaining insights into these distributions. This work compares observed sources from the largest Internet telescope (the CAIDA darknet telescope) with those from a commercial outpost (the GreyNoise honeyfarm). Neither of these locations actively emit Internet traffic and provide distinct observations of unsolicited Internet traffic (primarily botnets and scanners). Newly developed GraphBLAS hyperspace matrices and D4M associative array technologies enable the efficient analysis of these data on significant scales. The CAIDA sources are well approximated by a Zipf-Mandelbrot distribution. Over a 6-month period 70\% of the brightest (highest frequency) sources in the CAIDA telescope are consistently detected by coeval observations in the GreyNoise honeyfarm. This overlap drops as the sources dim (reduce frequency) and as the time difference between the observations grows. The probability of seeing a CAIDA source is proportional to the logarithm of the brightness. The temporal correlations are well described by a modified Cauchy distribution. These observations are consistent with a correlated high frequency beam of sources that drifts on a time scale of a month., Comment: 8 pages, 8 figures, 2 tables, 59 references; accepted to GrAPL 2022. arXiv admin note: substantial text overlap with arXiv:2108.06653

In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer flooding. The computational approach is based on an adaptive training procedure of a neural network that directly approximates an input-output map of the underlying PDE-constrained optimization problem. The training process thereby focuses on the construction of an accurate surrogate model solely related to the optimization path of an outer iterative optimization loop. True evaluations of the objective function are used to finally obtain certified results. Numerical experiments are given to evaluate the accuracy and efficiency of the approach for a heterogeneous five-spot benchmark problem.

Design of de novo biological sequences with desired properties, like protein and DNA sequences, often involves an active loop with several rounds of molecule ideation and expensive wet-lab evaluations. These experiments can consist of multiple stages, with increasing levels of precision and cost of evaluation, where candidates are filtered. This makes the diversity of proposed candidates a key consideration in the ideation phase. In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round. We also propose a scheme to incorporate existing labeled datasets of candidates, in addition to a reward function, to speed up learning in GFlowNets. We present empirical results on several biological sequence design tasks, and we find that our method generates more diverse and novel batches with high scoring candidates compared to existing approaches., Comment: ICML 2022. 15 pages, 3 figures. Code available at: https://github.com/MJ10/BioSeq-GFN-AL. Updated GFP results

Automating the recognition of outcomes reported in clinical trials using machine learning has a huge potential of speeding up access to evidence necessary in healthcare decision-making. Prior research has however acknowledged inadequate training corpora as a challenge for the Outcome detection (OD) task. Additionally, several contextualized representations like BERT and ELMO have achieved unparalleled success in detecting various diseases, genes, proteins, and chemicals, however, the same cannot be emphatically stated for outcomes, because these models have been relatively under-tested and studied for the OD task. We introduce "EBM-COMET", a dataset in which 300 PubMed abstracts are expertly annotated for clinical outcomes. Unlike prior related datasets that use arbitrary outcome classifications, we use labels from a taxonomy recently published to standardize outcome classifications. To extract outcomes, we fine-tune a variety of pre-trained contextualized representations, additionally, we use frozen contextualized and context-independent representations in our custom neural model augmented with clinically informed Part-Of-Speech embeddings and a cost-sensitive loss function. We adopt strict evaluation for the trained models by rewarding them for correctly identifying full outcome phrases rather than words within the entities i.e. given an outcome "systolic blood pressure", the models are rewarded a classification score only when they predict all 3 words in sequence, otherwise, they are not rewarded. We observe our best model (BioBERT) achieve 81.5\% F1, 81.3\% sensitivity and 98.0\% specificity. We reach a consensus on which contextualized representations are best suited for detecting outcomes from clinical-trial abstracts. Furthermore, our best model outperforms scores published on the original EBM-NLP dataset leader-board scores.

In this paper, we study deep neural networks (DNNs) for solving high-dimensional evolution equations with oscillatory solutions. Different from deep least-squares methods that deal with time and space variables simultaneously, we propose a deep adaptive basis Galerkin (DABG) method, which employs the spectral-Galerkin method for the time variable of oscillatory solutions and the deep neural network method for high-dimensional space variables. The proposed method can lead to a linear system of differential equations having unknown DNNs that can be trained via the loss function. We establish a posterior estimates of the solution error, which is bounded by the minimal loss function and the term $O(N^{-m})$, where $N$ is the number of basis functions and $m$ characterizes the regularity of the e'quation. We also show that if the true solution is a Barron-type function, the error bound converges to zero as $M=O(N^p)$ approaches to infinity, where $M$ is the width of the used networks, and $p$ is a positive constant. Numerical examples, including high-dimensional linear evolution equations and the nonlinear Allen-Cahn equation, are presented to demonstrate the performance of the proposed DABG method is better than that of existing DNNs.

In this paper, a massive multiple-input-multiple-output (mMIMO) testbed that is capable of mimicking realistic 5G new radio (NR) base station (BS) beamforming performance has been utilised to gather experimental-based evidence of 5G BS RF-EMF exposure within a real-world indoor environment. The mMIMO testbed has up to 128 RF channels with user-programmable software defined radio (SDR) capability. The stochastic nature of the 5G NR mMIMO system has been statistically assessed by evaluating the spatial variation of the RF-EMF exposure surrounding the mMIMO testbed when taking into account different beam profiles and data rates. Several other factors that influence the RF-EMF of mMIMO system have also being considered., Comment: 5 pages, 9 figures, conference - 15th European Conference on Antennas and Propagation (EuCAP 2021)

This paper presents the development of a Simultaneous Localization and Mapping (SLAM) based Autonomous Navigation system. The motivation for this study was to find a solution for navigating interior spaces autonomously. Interior navigation is challenging as it can be forever evolving. Solving this issue is necessary for multitude of services, like cleaning, the health industry, and in manufacturing industries. The focus of this paper is the description of the SLAM-based software architecture developed for this proposed autonomous system. A potential application of this system, oriented to a smart wheelchair, was evaluated. Current interior navigation solutions require some sort of guiding line, like a black line on the floor. With this proposed solution, interiors do not require renovation to accommodate this solution. The source code of this application has been made open source so that it could be re-purposed for a similar application. Also, this open-source project is envisioned to be improved by the broad open-source community upon past its current state.

Over the past few years, there has been significant interest in video action recognition systems and models. However, direct comparison of accuracy and computational performance results remain clouded by differing training environments, hardware specifications, hyperparameters, pipelines, and inference methods. This article provides a direct comparison between fourteen off-the-shelf and state-of-the-art models by ensuring consistency in these training characteristics in order to provide readers with a meaningful comparison across different types of video action recognition algorithms. Accuracy of the models is evaluated using standard Top-1 and Top-5 accuracy metrics in addition to a proposed new accuracy metric. Additionally, we compare computational performance of distributed training from two to sixty-four GPUs on a state-of-the-art HPC system., Comment: Accepted for publication at IEEE HPEC 2020

Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network traffic. Large-scale measurements of networks are computationally challenging. Building on prior work in interactive supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have developed an efficient method for computing a wide variety of streaming network quantities on diverse time scales. Applying these methods to 100,000,000,000 anonymized source-destination pairs collected at a network gateway reveals many previously unobserved scaling relationships. These observations provide new insights into normal network background traffic that could be used for anomaly detection, AI feature engineering, and testing theoretical models of streaming networks., Comment: 6 pages, 6 figures,3 tables, 49 references, accepted to IEEE HPEC 2020

Long Short-Term Memory (LSTM) neural networks have been widely used for time series forecasting problems. However, LSTMs are prone to overfitting and performance reduction during test phases. Several different regularization techniques have been shown in literature to prevent overfitting problems in neural networks. In this paper, first, we introduce application of kernel flow methods for time series forecasting in general. Afterward, we examine the effectiveness of applying kernel flow regularization on LSTM layers to avoid overfitting problems. We describe a regularization method by applying kernel flow loss function on LSTM layers. In experimental results, we show that kernel flow outperforms baseline models on time series forecasting benchmarks. We also compare the effect of dropout and kernel flow regularization techniques on LSTMs. The experimental results illustrate that kernel flow achieves similar regularization effect to dropout. It also shows that the best results is obtained using both kernel flow and dropout regularizations with early stopping on LSTM layers on some time series datasets (e.g. power-load demand forecasts).

In this paper, we propose a novel method for solving high-dimensional spectral fractional Laplacian equations. Using the Caffarelli-Silvestre extension, the $d$-dimensional spectral fractional equation is reformulated as a regular partial differential equation of dimension $d+1$. We transform the extended equation as a minimal Ritz energy functional problem and search for its minimizer in a special class of deep neural networks. Moreover, based on the approximation property of networks, we establish estimates on the error made by the deep Ritz method. Numerical results are reported to demonstrate the effectiveness of the proposed method for solving fractional Laplacian equations up to ten dimensions. Technically, in this method, we design a special network-based structure to adapt to the singularity and exponential decaying of the true solution. Also, A hybrid integration technique combining Monte Carlo method and sinc quadrature is developed to compute the loss function with higher accuracy.

The Internet has never been more important to our society, and understanding the behavior of the Internet is essential. The Center for Applied Internet Data Analysis (CAIDA) Telescope observes a continuous stream of packets from an unsolicited darkspace representing 1/256 of the Internet. During 2019 and 2020 over 40,000,000,000,000 unique packets were collected representing the largest ever assembled public corpus of Internet traffic. Using the combined resources of the Supercomputing Centers at UC San Diego, Lawrence Berkeley National Laboratory, and MIT, the spatial temporal structure of anonymized source-destination pairs from the CAIDA Telescope data has been analyzed with GraphBLAS hierarchical hypersparse matrices. These analyses provide unique insight on this unsolicited Internet darkspace traffic with the discovery of many previously unseen scaling relations. The data show a significant sustained increase in unsolicited traffic corresponding to the start of the COVID19 pandemic, but relatively little change in the underlying scaling relations associated with unique sources, source fan-outs, unique links, destination fan-ins, and unique destinations. This work provides a demonstration of the practical feasibility and benefit of the safe collection and analysis of significant quantities of anonymized Internet traffic., Comment: 8 pages, 9 figures, 2 tables, 43 references, accepted to IEEE HPEC 2021. arXiv admin note: substantial text overlap with arXiv:2008.00307