Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Studies on the Effect of Ceramic fine aggregate on the Strength properties of Concrete

Abstract— Increased construction activity and continuous dependence on conventional materials of concrete marking are leading to scarcity of the construction material and increased construction cost. In this study an attempt has been made to find the suitability of ceramic fine aggregate as a possible substitute for conventional fine aggregate in concrete. Experiments were carried out to determine the compressive, split tensile and flexural strength of ceramic fine aggregate and comparison is made with conventional concrete. Test results indicate that, the properties of ceramic fine aggregate are well within the range of values of concrete making aggregates.
Benthic Marine Ciliate Assemblages from Southern Brazil and Their Relationship with Seasonality and Urbanization Level

Sandy beaches represent two-thirds of littoral zones worldwide. Complex factors such as winds, waves, and sediment characterize this environment. Ciliophora is one of the most diversified unicellular eukaryotic phyla and, in marine sediments, can reach 40 species per cm³. Despite its importance, knowledge about interstitial communities is still lacking. In the present work, we characterized the assemblage of interstitial ciliates from sandy beaches in the north coastal area of Rio Grande do Sul state, southern Brazil. Fourteen sampling sites distributed along the swash zone of seven beaches with different degrees of urban development were sampled over the course of one year. At each site, eight sediment samples were taken, and a total of 51 species distributed in 37 ciliate genera were identified. Classes Spirotrichea (29%) and Karyorelictea (21%) accounted for 50% of ciliate species richness, with the highest richness being observed within the context of an intermediately urbanized setting. Community analyses revealed that ciliate community is influenced by urbanization degree and season, with distinct assemblages occurring in highly urbanized areas. In beaches affected by a high level of human activity, disturbance effects increased diversity, while in areas showing a milder influence of urbanization, the level of disturbance may be low, resulting in lower richness.
Akaski series of signed Selmer groups of elliptic curves with semistable reduction at primes above p

Let $p$ be an odd prime number, and let $E$ be an elliptic curve defined over a number field $F'$ such that $E$ has semistable reduction at every prime of $F'$ above $p$ and is supersingular at at least one prime above $p$. Under appropriate hypotheses, we compute the Akashi series of the signed Selmer groups of $E$ over a $\mathbb{Z}_p^d$-extension over a finite extension $F$ of $F'$. As a by-product, we also compute the Euler characteristics of these Selmer groups.
Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive ${h}$ -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.
Regime Switching Bandits

We study a multi-armed bandit problem where the rewards exhibit regime-switching. Specifically, the distributions of the random rewards generated from all arms depend on a common underlying state modeled as a finite-state Markov chain. The agent does not observe the underlying state and has to learn the unknown transition probability matrix as well as the reward distribution. We propose an efficient learning algorithm for this problem, building on spectral method-of-moments estimations for hidden Markov models and upper confidence bound methods for reinforcement learning. We also establish $O(T^{2/3}\sqrt{\log T})$ bound on the regret of the proposed learning algorithm where $T$ is the unknown horizon. Finally, we conduct numerical experiments to illustrate the effectiveness of the learning algorithm.
Empirical spectral distributions of high-dimensional matrix-valued processes driven by fractional Brownian motion

In this article, we study a class of symmetric or Hermitian random matrices, whose entries are generated from the solution $X_t$ of stochastic differential equation driven by fractional Brownian motion with Hurst parameter $H \in (1/2,1)$. Three types of matrices are considered: (1) Wigner-type matrices; (2) matrices with entries being local sums of i.i. d. copies of $X_t$; (3) Wishart-type matrices. When the dimension of the matrix-valued processes grows to infinity, we characterize the limit of the empirical measure-valued processes of their eigenvalues. As a byproduct, we obtain pathwise Holder continuity for the solution of the SDE driven by fractional Brownian motion under proper conditions, and provide a general tightness criterion for probability measures on the space C([0, T];P(R)) of continuous measure-valued processes.
Rates of convergence to equilibrium for potlatch and smoothing processes

We analyze the local and global smoothing rates of the smoothing process and obtain convergence rates to stationarity for the dual process known as the potlatch process. For general finite graphs, we connect the smoothing and convergence rates to the spectral gap of the associated Markov chain. We perform a more detailed analysis of these processes on the torus. Polynomial corrections to the smoothing rates are obtained. They show that local smoothing happens faster than global smoothing. These polynomial rates translate to rates of convergence to stationarity in $L^2$-Wasserstein distance for the potlatch process on $\mathbb{Z}^d$.
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification

Remote sensing image scene classification is a fundamental but challenging task in understanding remote sensing images. Recently, deep learning-based methods, especially convolutional neural network-based (CNN-based) methods have shown enormous potential to understand remote sensing images. CNN-based methods meet with success by utilizing features learned from data rather than features designed manually. The feature-learning procedure of CNN largely depends on the architecture of CNN. However, most of the architectures of CNN used for remote sensing scene classification are still designed by hand which demands a considerable amount of architecture engineering skills and domain knowledge, and it may not play CNN's maximum potential on a special dataset. In this paper, we proposed an automatically architecture learning procedure for remote sensing scene classification. We designed a parameters space in which every set of parameters represents a certain architecture of CNN (i.e., some parameters represent the type of operators used in the architecture such as convolution, pooling, no connection or identity, and the others represent the way how these operators connect). To discover the optimal set of parameters for a given dataset, we introduced a learning strategy which can allow efficient search in the architecture space by means of gradient descent. An architecture generator finally maps the set of parameters into the CNN used in our experiments.
Hasse diagrams with large chromatic number

For every positive integer n , we construct a Hasse diagram with n vertices and independence number O(n3/4) . Such graphs have chromatic number Ω(n1/4) , which significantly improves the previously best‐known constructions of Hasse diagrams having chromatic number Θ(logn) . In addition, if we also require girth of at least k⩾5 , we construct such Hasse diagrams with independence number at most n1−12k−4+o(1) . The proofs are based on the existence of point‐line arrangements in the plane with many incidences and avoids certain forbidden subconfigurations, which we find of independent interest.
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video

In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video. Specifically, given an untrimmed video and a query sentence, our goal is to localize a temporal segment in the video that semantically corresponds to the query sentence, with no reliance on any temporal annotation during training. We propose a two-stage model to tackle this problem in a coarse-to-fine manner. In the coarse stage, we first generate a set of fixed-length temporal proposals using multi-scale sliding windows, and match their visual features against the sentence features to identify the best-matched proposal as a coarse grounding result. In the fine stage, we perform a fine-grained matching between the visual features of the frames in the best-matched proposal and the sentence features to locate the precise frame boundary of the fine grounding result. Comprehensive experiments on the ActivityNet Captions dataset and the Charades-STA dataset demonstrate that our two-stage model achieves compelling performance.
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation

Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Inspired by the recent progress in deep generative models, in this paper we propose a flow-based autoregressive model for graph generation called GraphAF. GraphAF combines the advantages of both autoregressive and flow-based approaches and enjoys: (1) high model flexibility for data density estimation; (2) efficient parallel computation for training; (3) an iterative sampling process, which allows leveraging chemical domain knowledge for valency checking. Experimental results show that GraphAF is able to generate 68% chemically valid molecules even without chemical knowledge rules and 100% valid molecules with chemical rules. The training process of GraphAF is two times faster than the existing state-of-the-art approach GCPN. After fine-tuning the model for goal-directed property optimization with reinforcement learning, GraphAF achieves state-of-the-art performance on both chemical property optimization and constrained property optimization.
The network paradigm as a modeling tool in regional economy: the case of interregional commuting in Greece

Network Science is an emerging discipline using the network paradigm to model communication systems as pair-sets of interconnected nodes and their linkages (edges). This paper applies this paradigm to study an interacting system in regional economy consisting of daily road transportation flows for labor purposes, the so-called commuting phenomenon. In particular, the commuting system in Greece including 39 non-insular prefectures is modeled into a complex network and it is studied using measures and methods of complex network analysis and empirical techniques. The study aims to detect the structural characteristics of the Greek interregional commuting network (GCN) and to interpret how this network is related to the regional development. The analysis highlights the effect of the spatial constraints in the structure of the GCN, it provides insights about the major road transport projects constructed the last decade, and it outlines a populationcontrolled (gravity) pattern of commuting, illustrating that high-populated regions attract larger volumes of the commuting activity, which consequently affects their productivity. Overall, this paper highlights the effectiveness of complex network analysis in the modeling of systems of regional economy, such as the systems of spatial interaction and the transportation networks, and it promotes the use of the network paradigm to the regional research.
Inference in Multi-Layer Networks with Matrix-Valued Unknowns

We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output. The hidden variables in each layer are represented as matrices. This problem applies to signal recovery via deep generative prior models, multi-task and mixed regression and learning certain classes of two-layer neural networks. A unified approximation algorithm for both MAP and MMSE inference is proposed by extending a recently-developed Multi-Layer Vector Approximate Message Passing (ML-VAMP) algorithm to handle matrix-valued unknowns. It is shown that the performance of the proposed Multi-Layer Matrix VAMP (ML-Mat-VAMP) algorithm can be exactly predicted in a certain random large-system limit, where the dimensions $N\times d$ of the unknown quantities grow as $N\rightarrow\infty$ with $d$ fixed. In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features and training samples grow to infinity but the number of hidden nodes stays fixed. The analysis enables a precise prediction of the parameter and test error of the learning.
Provenance for Regular Path Queries

Regular path queries (RPQs) the ubiquitous mechanism for querying data graphs of partially known structure. RPQs are in essence regular expressions over the edge symbols. The answer to an RPQ on a given graph (database) is the set of pairs of objects, which are connected by paths spelling words in the language of the regular path query. Often the database edges come with a weights assoaciated to them. Such weights can distances, levels of discomfort, multiplicities, etc. We model weights using semiring frameworks.
Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.
Universal bolometric corrections for active galactic nuclei over seven luminosity decades

Context. The AGN bolometric correction is a key element for understanding black hole (BH) demographics and computing accurate BH accretion histories from AGN luminosities. However, current estimates still differ from each other by up to a factor of two to three, and rely on extrapolations at the lowest and highest luminosities. Aims. Here we revisit this fundamental question by presenting general hard X-ray (KX) and optical (KO) bolometric corrections, computed by combining several AGN samples spanning the widest (about 7 dex) luminosity range ever used for this type of studies. Methods. We analysed a total of ∼1000 type 1 and type 2 AGN for which we performed a dedicated SED-fitting. Results. We provide a bolometric correction separately for type 1 and type 2 AGN; the two bolometric corrections agree in the overlapping luminosity range. Based on this we computed for the first time a universal bolometric correction for the whole AGN sample (both type 1 and type 2). We found that KX is fairly constant at log(LBOL/L⊙) < 11, while it increases up to about one order of magnitude at log(LBOL/L⊙) ∼ 14.5. A similar increasing trend has been observed when its dependence on either the Eddington ratio or the BH mass is considered, while no dependence on redshift up to z ∼ 3.5 has been found. In contrast, the optical bolometric correction appears to be fairly constant (i.e. KO ∼ 5) regardless of the independent variable. We also verified that our bolometric corrections correctly predict the AGN bolometric luminosity functions. According to this analysis, our bolometric corrections can be applied to the whole AGN population in a wide range of luminosity and redshift.
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery

This paper investigates total variation minimization in one spatial dimension for the recovery of gradient-sparse signals from undersampled Gaussian measurements. Recently established bounds for the required sampling rate state that uniform recovery of all $s$-gradient-sparse signals in ${\mathbb{R}}^n$ is only possible with $m \gtrsim \sqrt{s n} \cdot{\operatorname{PolyLog}}(n)$ measurements. Such a condition is especially prohibitive for high-dimensional problems, where $s$ is much smaller than $n$. However, previous empirical findings seem to indicate that this sampling rate does not reflect the typical behavior of total variation minimization. The present work provides a rigorous analysis that breaks the $\sqrt{s n}$-bottleneck for a large class of “natural” signals. The main result shows that non-uniform recovery succeeds with high probability for $m \gtrsim s \cdot{\operatorname{PolyLog}}(n)$ measurements if the jump discontinuities of the signal vector are sufficiently well separated. In particular, this guarantee allows for signals arising from a discretization of piecewise constant functions defined on an interval. The key ingredient of the proof is a novel upper bound for the associated conic Gaussian mean width, which is based on a signal-dependent, non-dyadic Haar wavelet transform. Furthermore, a natural extension to stable and robust recovery is addressed.
A hot terrestrial planet orbiting the bright M dwarf L 168-9 unveiled by TESS

We report the detection of a transiting super-Earth-sized planet (R= 1.39 ± 0.09R⊕) in a 1.4-day orbit around L 168-9 (TOI-134), a bright M1V dwarf (V= 11,K= 7.1) located at 25.15 ± 0.02 pc. The host star was observed in the first sector of the Transiting Exoplanet Survey Satellite (TESS) mission. For confirmation and planet mass measurement purposes, this was followed up with ground-based photometry, seeing-limited and high-resolution imaging, and precise radial velocity (PRV) observations using the HARPS andMagellan/PFS spectrographs. By combining the TESS data and PRV observations, we find the mass of L 168-9 b to be 4.60 ± 0.56M⊕and thus the bulk density to be 1.74−0.33+0.44times higher than that of the Earth. The orbital eccentricity is smaller than 0.21 (95% confidence). This planet is a level one candidate for the TESS mission’s scientific objective of measuring the masses of 50 small planets, and it is one of the most observationally accessible terrestrial planets for future atmospheric characterization.
Ten Social Dimensions of Conversations and Relationships

Decades of social science research identified ten fundamental dimensions that provide the conceptual building blocks to describe the nature of human relationships. Yet, it is not clear to what extent these concepts are expressed in everyday language and what role they have in shaping observable dynamics of social interactions. After annotating conversational text through crowdsourcing, we trained NLP tools to detect the presence of these types of interaction from conversations, and applied them to 160M messages written by geo-referenced Reddit users, 290k emails from the Enron corpus and 300k lines of dialogue from movie scripts. We show that social dimensions can be predicted purely from conversations with an AUC up to 0.98, and that the combination of the predicted dimensions suggests both the types of relationships people entertain (conflict vs. support) and the types of real-world communities (wealthy vs. deprived) they shape.
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

We introduce ‘POLAR’ — a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold – hot, soft – hard). The core idea of our approach is to transform existing, pre-trained word embeddings via semantic differentials to a new “polar” space with interpretable dimensions defined by such polar opposites. Our framework also allows for selecting the most discriminative dimensions from a set of polar dimensions provided by an oracle, i.e., an external source. We demonstrate the effectiveness of our framework by deploying it to various downstream tasks, in which our interpretable word embeddings achieve a performance that is comparable to the original word embeddings. We also show that the interpretable dimensions selected by our framework align with human judgement. Together, these results demonstrate that interpretability can be added to word embeddings without compromising performance. Our work is relevant for researchers and engineers interested in interpreting pre-trained word embeddings.
Observation of multifractality of light in prime number arrays.

Many natural patterns and shapes, such as meandering coastlines, clouds, or turbulent flows, exhibit a characteristic complexity mathematically described by fractal geometry. In recent years, the engineering of self-similar structures in photonics and nano-optics technology enabled the manipulation of light states beyond periodic or disordered systems, adding novel functionalities to complex optical media with applications to nano-devices and metamaterials. Here, we extend the reach of fractal "photonics" by experimentally demonstrating multifractality of light in engineered arrays of dielectric nanoparticles. Our findings stimulate fundamental questions on the nature of transport and localization of wave excitations with multi-scale fluctuations beyond what is possible in traditional fractal systems. Moreover, our approach establishes structure-property relationships that can readily be transferred to planar semiconductor electronics and to artificial atomic lattices, enabling the exploration of novel quantum phases and many-body effects that emerge directly from fundamental structures of algebraic number theory.