Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Rocky Reefs on Structure of Benthic Macrofauna in Nearby Soft-sediments

Abstract Influences of rocky reefs on composition and abundances of species in assemblages of macrofauna in nearby subtidal, soft-sediments were examined by sampling at three distances from four rocky reefs, three natural and one man-made, in Botany Bay, NSW, Australia. There was significant spatial variation in structure of assemblages between transects and among reefs. The proportion of coarse material in sediments was generally larger close to, than far from, reefs. Assemblages sampled close to reefs were generally different in structure from those sampled away from rocky reefs. In multivariate analyses, assemblages close to reefs were spatially more variable than were those far from reefs. Analyses demonstrated that there were more species close to, than far from, the reefs; polychaetes of the family Syllidae were more abundant far from, than near to, rocky reefs. Artificial and natural rocky reefs influenced nearby benthic assemblages in sediments and various models are discussed to explain the patterns detected, especially those concerning predation and characteristics of the sediments.
Evaluation of Thermoplastic Marking Materials

Due to the harsh conditions of airport environments, frequent repainting of existing waterborne pavement markings is required. This painting is expensive and affects life-cycle costs. A thermoplastic marking material has been identified as an alternative to the existing waterborne material. The purpose of this research effort was to determine if this thermoplastic marking material is as effective as the current waterborne material in terms of its retro-reflectivity, chromaticity, friction properties, and its adherence to the airport pavement surface. Two types of thermoplastic materials were applied on asphalt and concrete surfaces at the Federal Aviation Administration William J. Hughes Technical Center and were evaluated for 1 year starting in June 2006. These materials were also applied on concrete pavement surfaces at the Newark Liberty International Airport and evaluated for 1 year starting in August 2006. One thermoplastic material was 60-mil thick with Type I and III beads and was applied on a heated surface; the other material was 90-mil thick with Type I and IV beads and was applied on cold surface. Retro-reflectivity was measured using a retroreflectometer; a spectrophotometer was used to measure chromaticity, a Dyna-Meter Pull-Off tester was used to measure adherence strength, and a Saab Surface Friction Tester was used to measure friction properties. Most measurements were taken on a monthly basis. The results showed that the retro-reflectivity characteristics of thermoplastic marking materials were acceptable. The chromaticity of the thermoplastic was within tolerance for white, red, yellow, and black. The average friction readings recorded on thermoplastic were significantly lower than those taken on bare pavement and about 50% less than waterborne paint. The adherence showed that preparation is necessary for a good bond. The tensile strength of the bond between the thermoplastic marking material and hot-mix asphalt was acceptable. The tensile strength of the bond between the thermoplastic marking material and Portland cement concrete was acceptable when an additional adhesive was applied. Based on the result of this evaluation, thermoplastic marking material is recommended for taxiways.
Mössbauer spectra of goethite: evidence for structural imperfections

Summary Goethites of well-crystallized appearance from a sedimentary ore deposit have been studied by Mössbauer spectroscopy and other methods following HCl corrosion, particle size fractionation, and annealing treatments. The goethites contained an appreciable amount of Si, but were not significantly Al-substituted. The observed broad and asymmetric resonant lines of the Mössbauer spectra are considered to result from this Si which, interspersed in the goethite structure, would interrupt magnetic coupling and give rise to a reduced and inhomogeneous magnetic hyperfine field.
Trust-Aware Motion Planning for Human-Robot Collaboration under Distribution Temporal Logic Specifications

Recent work has considered trust-aware decision making for human-robot collaboration (HRC) with a focus on model learning. In this paper, we are interested in enabling the HRC system to complete complex tasks specified using temporal logic that involve human trust. Since human trust in robots is not observable, we adopt the widely used partially observable Markov decision process (POMDP) framework for modelling the interactions between humans and robots. To specify the desired behaviour, we propose to use syntactically co-safe linear distribution temporal logic (scLDTL), a logic that is defined over predicates of states as well as belief states of partially observable systems. The incorporation of belief predicates in scLDTL enhances its expressiveness while simultaneously introducing added complexity. This also presents a new challenge as the belief predicates must be evaluated over the continuous (infinite) belief space. To address this challenge, we present an algorithm for solving the optimal policy synthesis problem. First, we enhance the belief MDP (derived by reformulating the POMDP) with a probabilistic labelling function. Then a product belief MDP is constructed between the probabilistically labelled belief MDP and the automaton translation of the scLDTL formula. Finally, we show that the optimal policy can be obtained by leveraging existing point-based value iteration algorithms with essential modifications. Human subject experiments with 21 participants on a driving simulator demonstrate the effectiveness of the proposed approach.
Estimation of the FR4 Microwave Dielectric Properties at Cryogenic Temperature for Quantum-Chip-Interface PCBs Design

Ad hoc interface printed circuit boards (PCBs) are today the standard connection between cryogenic cabling and quantum chips. Besides low-loss and low-temperature-dependent-dielectric-permittivity materials, Flame Resistance n.4 (FR4) provides a low-cost solution for the fabrication of cryogenic PCBs. Here, we report on an effective way to evaluate the dielectric performance of an FR4 laminate used as a substrate for cryogenic microwave PCBs. We designed a coplanar waveguide <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>/2 open-circuit series resonator, and we fabricated the PCB using a low-cost manufacturing process, obtaining in-plane geometric features with maximum variations of 50–<inline-formula> <tex-math notation="LaTeX">$100~\mu \text{m}$ </tex-math></inline-formula> compared to the PCB design. Such a geometry allows to exploit the resonance peak of the resonator to measure the variation of the complex permittivity as a function of the temperature. The resonance peak frequency was used to estimate the real permittivity, achieving a sensitivity of −470 MHz and a resolution of <inline-formula> <tex-math notation="LaTeX">$1.2\times 10^{-2}$ </tex-math></inline-formula>. Similarly, the resonance peak magnitude was involved in the extrapolation of the loss tangent, reaching a sensitivity of ~−337 dB and a resolution of <inline-formula> <tex-math notation="LaTeX">$1.6\times 10^{-4}$ </tex-math></inline-formula>. For the FR4 laminate used, we estimated a 9% reduction of the real permittivity and a 70% reduction of the loss tangent in the temperature range from 300 K to 4 K. The proposed approach can be immediately extended to the detection of cryogenic temperature-dependent dielectric performance of any kind of substrate.
A path-norm toolkit for modern networks: consequences, promises and challenges

This work introduces the first toolkit around path-norms that fully encompasses general DAG ReLU networks with biases, skip connections and any operation based on the extraction of order statistics: max pooling, GroupSort etc. This toolkit notably allows us to establish generalization bounds for modern neural networks that are not only the most widely applicable path-norm based ones, but also recover or beat the sharpest known bounds of this type. These extended path-norms further enjoy the usual benefits of path-norms: ease of computation, invariance under the symmetries of the network, and improved sharpness on layered fully-connected networks compared to the product of operator norms, another complexity measure most commonly used. The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet.
Emissive cathode immersed in a plasma: plasma–cathode interactions, operation and stability

Thermionic emission from a polycrystalline tungsten emissive cathode immersed in a magnetized plasma column is investigated experimentally and numerically. Electrical and optical measurements of the cathode temperature show a highly inhomogeneous cathode temperature profile due to plasma–cathode interactions. The spatially and temporally resolved cathode temperature profile provides an in-depth understanding of the thermionic electron current, in excellent agreement with experimental data. The plasma-cathode coupling leads to a sharp and heterogeneous rise in temperature along the cathode, which can eventually lead to unstable cathode operation, with divergent current growth. A detailed thermal modeling accurately reproduces the experimental measurements, and allows to quantify precisely the relative importance of heating and cooling mechanisms in the operation of the cathode immersed in the plasma. Numerical resolution of the resulting integro-differential equation highlights the essential role of heterogeneous ohmic heating and the importance of ion bombardment heating in the emergence of unstable regimes. Detailed thermal modelling enables operating regimes to be predicted in excellent agreement with experimental results.
A Resource-efficient FIR Filter Design Based on an RAG Improved Algorithm

In modern digital filter chip design, efficient resource utilization is a hot topic. Due to linear phase characteristics of FIR filters, a pulsed fully parallel structure can be applied to attack the problem. In order to further reduce hardware resource consumption especially caused by multiplication function, an improved RAG algorithm is proposed. Filters with different orders and for different algorithms are compared, and the experimental results show that the improved RAG algorithm is excellent in terms of logic resource utilization, resource allocation, running speed, and power consumption under different application scenarios. The proposed algorithm invokes a better circuit structure for FIR filter, it gives full play to resource allocation strategy and reduces logic resource consumption. The proposed circuit is faster and more stable, and suitable for a variety of complex application scenarios.
The Fisher-Rao geometry of CES distributions

When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric. The geometry induced on the parameters by this metric is then referred to as the Fisher-Rao information geometry. Interestingly, this yields a point of view that allows for leveragingmany tools from differential geometry. After a brief introduction about these concepts, we will present some practical uses of these geometric tools in the framework of elliptical distributions. This second part of the exposition is divided into three main axes: Riemannian optimization for covariance matrix estimation, Intrinsic Cram\'er-Rao bounds, and classification using Riemannian distances.
Optimal Estimator for Linear Regression with Shuffled Labels

This paper considers the task of linear regression with shuffled labels, i.e., $\mathbf Y = \mathbf \Pi \mathbf X \mathbf B + \mathbf W$, where $\mathbf Y \in \mathbb R^{n\times m}, \mathbf Pi \in \mathbb R^{n\times n}, \mathbf X\in \mathbb R^{n\times p}, \mathbf B \in \mathbb R^{p\times m}$, and $\mathbf W\in \mathbb R^{n\times m}$, respectively, represent the sensing results, (unknown or missing) corresponding information, sensing matrix, signal of interest, and additive sensing noise. Given the observation $\mathbf Y$ and sensing matrix $\mathbf X$, we propose a one-step estimator to reconstruct $(\mathbf \Pi, \mathbf B)$. From the computational perspective, our estimator's complexity is $O(n^3 + np^2m)$, which is no greater than the maximum complexity of a linear assignment algorithm (e.g., $O(n^3)$) and a least square algorithm (e.g., $O(np^2 m)$). From the statistical perspective, we divide the minimum $snr$ requirement into four regimes, e.g., unknown, hard, medium, and easy regimes; and present sufficient conditions for the correct permutation recovery under each regime: $(i)$ $snr \geq \Omega(1)$ in the easy regime; $(ii)$ $snr \geq \Omega(\log n)$ in the medium regime; and $(iii)$ $snr \geq \Omega((\log n)^{c_0}\cdot n^{{c_1}/{srank(\mathbf B)}})$ in the hard regime ($c_0, c_1$ are some positive constants and $srank(\mathbf B)$ denotes the stable rank of $\mathbf B$). In the end, we also provide numerical experiments to confirm the above claims.
On Fulfilling the Exigent Need for Automating and Modernizing Logistics Infrastructure in India: Enabling AI-based Integration, Digitalization, and Smart Automation of Industrial Parks and Robotic Warehouses

To stay competitive, the Low- or Middle-Income Countries (LMICs) need to embrace Industry 4.0 and Logistics 4.0. This requires government-level interventions and policy-making to incentivize quality product solutions and drive innovation in traditionally resistant economic sectors. In this position paper, we support the establishment of Smart Industrial Parks (SIPs) with a focus on enhancing operational efficiencies and bringing together MSMEs and startups targeting niche clientele with innovative Industry 4.0 solutions. SIPs along with the phased deployment of well-planned robotic automation technologies shall enable bringing down India's untenable logistics costs. Toward the successful execution of SIPs, we are required to implement the efficient allocation of manufacturing resources and capabilities within SIPs. Thus, we emphasize the importance of efficient resource utilization, collaboration, and technology adoption in industrial parks to promote industrial development and economic growth. We advocate the use of a cloud-based cyber-physical system for real-time data access and analysis in SIPs. Such centralized cloud-based monitoring of factory floors, warehouses, and industrial units using IoT infrastructure shall improve decision-making, efficiency, and safety. Digital Twins (DTs), which are cyber-replicas of physical systems, could play a significant role in enabling simulation, optimization, and real-time monitoring of smart manufacturing and distributed manufacturing systems. However, there are several challenges involved in implementing DTs in distributed manufacturing systems, such as defining data schemas and collaboration protocols, ensuring interoperability, the need for effective authentication technology, distributed machine learning models, and scalability to manage multiple DTs.
Distributed Deterministic Exact Minimum Weight Cycle and Multi Source Shortest Paths in Near Linear Rounds in CONGEST model

We present new deterministic algorithms for computing distributed weighted minimum weight cycle (MWC) in undirected and directed graphs and distributed weighted all nodes shortest cycle (ANSC) in directed graphs. Our algorithms for these problems run in $\tilde{O}(n)$ rounds in the CONGEST model on graphs with arbitrary non-negative edge weights, matching the lower bound up to polylogarithmic factors. Before our work, no near linear rounds deterministic algorithms were known for these problems. The previous best bound for solving these problems deterministically requires an initial computation of all pairs shortest paths (APSP) on the given graph, followed by post-processing of $O(n)$ rounds, and in total takes $\tilde{O}(n^{4/3})$ rounds, using deterministic APSP~\cite{AR-SPAA20}. The main component of our new $\tilde{O}(n)$ rounds algorithms is a deterministic technique for constructing a sequence of successive blocker sets. These blocker sets are then treated as source nodes to compute $h$-hop shortest paths, which can then be used to compute candidate shortest cycles whose hop length lies in a particular range. The shortest cycles can then be obtained by selecting the cycle with the minimum weight from all these candidate cycles. Additionally using the above blocker set sequence technique, we also obtain $\tilde{O}(n)$ rounds deterministic algorithm for the multi-source shortest paths problem (MSSP) for both directed and undirected graphs, given that the size of the source set is at most $\sqrt{n}$. This new result for MSSP can be a step towards obtaining a $o(n^{4/3})$ rounds algorithm for deterministic APSP. We also believe that our new blocker set sequence technique may have potential applications for other distributed algorithms.
Model-Checking in the Loop Model-Based Testing for Automotive Operating Systems

While vehicles have primarily been controlled through mechanical means in years past, an increasing number of embedded control systems are being installed and used, keeping pace with advances in electronic control technology and performance. Automotive systems consist of multiple components developed by a range of vendors. To accelerate developments in embedded control systems, industrial standards such as AUTOSAR are being defined for automotive systems, including the design of operating system and middleware technologies. Crucial to ensuring the safety of automotive systems, the operating system is foundational software on which many automotive applications are executed. In this paper, we propose an integrated model-based method for verifying automotive operating systems; our method is called Model-Checking in the Loop Model-Based Testing (MCIL-MBT). In MCIL-MBT, we create a model that formalizes specifications of automotive operating systems and verifies the specifications via model-checking. Next, we conduct model-based testing with the verified model to ensure that a specific operating system implementation conforms to the model. These verification and testing stages are iterated over until no flaws are detected. Our method has already been introduced to an automotive system supplier and an operating system vendor. Through our approach, we successfully identified flaws that were not detected by conventional review and testing methods.
Rotation of a fiber in simple shear flow of a dilute polymer solution

The motion of a freely rotating prolate spheroid in a simple shear flow of a dilute polymeric solution is examined in the limit of large particle aspect ratio, $\kappa$. A regular perturbation expansion in the polymer concentration, $c$, a generalized reciprocal theorem, and slender body theory to represent the velocity field of a Newtonian fluid around the spheroid are used to obtain the $\mathcal{O}(c)$ correction to the particle's orientational dynamics. The resulting dynamical system predicts a range of orientational behaviors qualitatively dependent upon $c\cdot De$ ($De$ is the imposed shear rate times the polymer relaxation time) and $\kappa$ and quantitatively on $c$. At a small but finite $c\cdot De$, the particle spirals towards a limit cycle near the vorticity axis for all initial conditions. Upon increasing $\kappa$, the limit cycle becomes smaller. Thus, ultimately the particle undergoes a periodic motion around and at a small angle from the vorticity axis. At moderate $c\cdot De$, a particle starting near the flow-gradient plane departs it monotonically instead of spirally, as this plane (a limit cycle at smaller $c\cdot De$) obtains a saddle and an unstable node. The former is close to the flow direction. Upon further increasing $c\cdot De$, the saddle-node changes to a stable node. Therefore, depending upon the initial condition, a particle may either approach a periodic orbit near the vorticity axis or obtain a stable orientation near the flow direction. Upon further increasing $c\cdot De$, the limit cycle near the vorticity axis vanishes, and the particle aligns with the flow direction for all starting orientations.
Multi-label residual weighted learning for individualized combination treatment rule

Individualized treatment rules (ITRs) have been widely applied in many fields such as precision medicine and personalized marketing. Beyond the extensive studies on ITR for binary or multiple treatments, there is considerable interest in applying combination treatments. This paper introduces a novel ITR estimation method for combination treatments incorporating interaction effects among treatments. Specifically, we propose the generalized $\psi$-loss as a non-convex surrogate in the residual weighted learning framework, offering desirable statistical and computational properties. Statistically, the minimizer of the proposed surrogate loss is Fisher-consistent with the optimal decision rules, incorporating interaction effects at any intensity level - a significant improvement over existing methods. Computationally, the proposed method applies the difference-of-convex algorithm for efficient computation. Through simulation studies and real-world data applications, we demonstrate the superior performance of the proposed method in recommending combination treatments.
NP2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks

How to utilize the pseudo labels has always been a research hotspot in machine learning. However, most methods use pseudo labels as supervised training, and lack of valid assessing for their accuracy. Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism. Aiming to address the first issue, we found through a large number of experiments that the pseudo labels are more accurate if they are selected by not overlapping partial labels and defined as negative node pairs relations. Therefore, considering the extraction based on pseudo and partial labels, negative edges are constructed between two nodes by the negative pseudo partial labels extraction (NP$^2$E) module. With that, a signed graph are built containing highly accurate pseudo labels information from the original graph, which effectively assists GNN in learning at the message-passing level, provide one solution to the second issue. Empirical results about link prediction and node classification tasks on several benchmark datasets demonstrate the effectiveness of our method. State-of-the-art performance is achieved on the both tasks.
The influence of coordinated behavior on toxicity

In the intricate landscape of social media genuine content dissemination may be altered by a number of threats. Coordinated Behavior (CB), defined as orchestrated efforts by entities to deceive or mislead users about their identity and intentions, emerges as a tactic to exploit or manipulate online discourse. This study delves into the relationship between CB and toxic conversation on Twitter. Using a dataset of 11 million tweets from 1 million users preceding the 2019 UK General Elections, we show that users displaying CB typically disseminate less harmful content, irrespective of political affiliation. However, distinct toxicity patterns emerge among different CB cohorts. Compared to their non-CB counterparts, CB participants show marginally elevated toxicity levels only when considering their original posts. We further show the effects of CB-driven toxic content on non-CB users, gauging its impact based on political leanings. Our findings suggest a nuanced but statistically significant influence of CB on digital discourse.