Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEW Fifty years of wildland ﬁ re science in Canada 1

: We celebrate the 50th anniversary of the Canadian Journal of Forest Research by re ﬂ ecting on the considerable progress accomplished in select areas of Canadian wildland ﬁ re science over the past half century. Speci ﬁ cally, we discuss key developments and contributions in the creation of the Canadian Forest Fire Danger Rating System; the relationships between wildland ﬁ re and weather, climate, and climate change; ﬁ re ecology; operational decision support; and wildland ﬁ re management. We also discuss the evolution of wildland ﬁ re management in Banff National Park as a case study. We conclude by discussing some possible directions in future Canadian wildland ﬁ re research including the further evaluation of ﬁ re severity measurements and effects; the ef ﬁ cacy of fuel management treatments; climate change effects and mitigation; further re ﬁ nement of models per-taining to ﬁ re risk analysis, ﬁ re behaviour, and ﬁ re weather; and the integration of forest management and ecological restoration with wildland ﬁ re risk reduction. Throughout the paper, we reference many contributions published in the Canadian Journal of Forest Research , which has been at the forefront of international wildland ﬁ re science. la Méthode canadienne d ’ évaluation des dangers d ’ incendie de forêt, la climatologie-météorologie des incendies, le changement climatique, l ’ écologie des incendies et la gestion opérationnelle des incendies. Nous présentons, à titre d ’ exemple, une étude de cas sur l ’ évolution de la gestion des incendies dans le parc national Banff. Nous concluons en discutant des orientations des recherches futures sur les incendies de forêt au Canada, notamment pour ce qui est de l ’ évaluation future de la grav-ité des incendies et de leurs effets, de l ’ ef ﬁ cacité des traitements de gestion des combustibles et des effets et de l ’ atténuation du changement climatique, ainsi que du développement de l ’ analyse des risques d ’ incendie de même que des modèles de comporte-ment des incendies. Nous constatons également qu ’ il est toujours nécessaire de mieux intégrer la gestion des forêts et la restauration écologique à la réduction des risques d ’ incendie. Tout au long de l ’ article, nous faisons référence aux nombreuses contributions pub-liées dans la Revue canadienne de recherche forestière , qui a été à la pointe de la science internationale en matière d ’ incendies de forêt. Mots-clés : parc national Banff, Méthode canadienne d ’ évaluation des dangers d ’ incendie de forêt, écologie de feu, feux de forêts.
On the Local Behavior of the Mappings with Non-Bounded Characteristics

The present paper is devoted to the study of space mappings, which are more general than quasiregular mappings. The questions of the behavior of differentiable mappings having the so--called $N,$ $N^{-1},$ $ACP$ and $ACP^{-1}$ -- properties are studied in the work. Under some additional conditions, it is showed that the modulus of such mappings $f$ can be more than each degree of logarithmic function at every neighborhood of the isolated essential singularity of $f. $
Year 13 or first-year university : a holistic learning design that attempts to combine elements of secondary and tertiary learning and teaching

For many years, Australian universities have been accepting students into their courses, including Science, with inadequate mathematical backgrounds. In addition to this lack of mathematical preparation, students are illprepared for the demands of independent learning as required by university courses. Thus many students are enrolling in university courses without basic numeracy skills and furthermore, they lack the ability to cope with the requirements of self-directed learning. This results in students being totally overwhelmed by their first few weeks experience at university which can result in significant ‘drop-out’ rates. This report describes a learning design used in the delivery of a firstyear mathematics unit that attempts to remediate numeracy skills and develop the independent learning skills required by the ‘traditional’ university experience.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Dec 7, 2021 Revised May 25, 2022 Accepted Jun 20, 2022 Traffic flow prediction is an integral part of the intelligent transportation system (ITS) that helps in making well-informed decisions. Traffic flow prediction helps in alleviating traffic congestion as well as in some connected vehicles applications such as resources allocation. However, most of the existing models do not consider external factors such as weather data. Traffic flow in road networks is affected by weather conditions which affects the periodicity of traffic. These effects introduce some irregularity to the traffic pattern, making traffic flow prediction a challenging issue. In this paper, we present a detailed investigation on the impact of weather data on different traffic flow prediction models. The investigation presented in this paper demonstrates how adding weather data could improve the models’ prediction accuracy and efficiency.
Detecting Logical Bugs of DBMS with Coverage-based Guidance

Database management systems (DBMSs) are critical components of modern data-intensive applications. Developers have adopted many testing techniques to detect DBMS bugs such as crashes and assertion failures. However, most previous efforts cannot detect logical bugs that make the DBMS return incorrect results. Recent work proposed several oracles to identify incorrect results, but they rely on rule-based expression generation to synthesize queries without any guidance. In this paper, we propose to combine coverage-based guidance, validity-oriented mutations and oracles to detect logical bugs in DBMS systems. Specifically, we first design a set of general APIs to decouple the logic of fuzzers and oracles, so that developers can easily port fuzzing tools to test DBMSs and write new oracles for existing fuzzers. Then, we provide validity-oriented mutations to generate high-quality query statements in order to find more logical bugs. Our prototype, SQLRight , outperforms existing tools that only rely on oracles or code coverage. In total, SQLRight detects 18 logical bugs from two well-tested DBMSs, SQLite and MySQL . All bugs have been confirmed and 14 of them have been fixed. Abstract This artifact is to help users reproduce the results we reported in our USENIX Security 2022 paper submission. We recom-mend to run the artifact on a x86-64 computer with ≥ 20 CPU cores, ≥ 600 GB of memory and ≥ 1 . 5 TB hard drive storage, and with an Ubuntu 20.04 LTS operating system. The artifact should reproduce all the Figures and Tables we reported in the paper, and thus can validate the main claims of the paper. Detailed execution steps are elaborated in the artifact README.md ﬁle.
Speci c lncRNA signatures discriminate childhood acute leukemias: a pilot study

Lorena Buono IRCCS SYNLAB-SDN Concetta Iside IRCCS SYNLAB-SDN Antonia Matteo Santobono-Pausilipon Children's Hospital, AORN Pio Stellato Santobono-Pausilipon Children's Hospital, AORN Giuliana Beneduce Santobono-Pausilipon Children's Hospital, AORN Roberta Penta Santobono-Pausilipon Children's Hospital, AORN Rosanna Parasole Santobono-Pausilipon Children's Hospital, AORN Marco Salvatore IRCCS SYNLAB-SDN Giovanni Smaldone (  giovanni.smaldone@synlab.it ) IRCCS SYNLAB-SDN Peppino Mirabelli IRCCS SYNLAB-SDN
Discrete-time noise filtering for pulse-processing in particle physics experiments.

Particle Physics is the branch of physics that studies the fundamental constituents of matter and radiation, and their mutual interactions. The main tools used by particle physicists are particle accelerators, which use electromagnetic fields to accelerate charged particles to relativistic speeds before they are made to collide inside detectors. The International Linear Collider (ILC), a next generation, 31-kilometer long linear particle accelerator, will smash electron and positron bunches at up to 500 GeV. Located at the ILC detector forward region is the BeamCal, a highly segmented calorimeter detector. The BeamCal specifications for radiation tolerance, noise, signal charge, pulse rate and occupancy pose unique challenges for the instrumentation system. Framed in the design, integration and testing of the Bean IC, a 5-channel application specific integrated circuit (ASIC) planned to meet the BeamCal instrumentation needs, this thesis presents: the development of a new mathematical framework for a design-oriented analysis of discrete-time filters in the discrete-time domain; and the design and implementation of a switched-capacitor (SC) filter for arbitrary weighting function synthesis to be included in the Bean IC, which aims to take full advantage of the introduced mathematical framework.
A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.
Coefficient-based Regularized Distribution Regression

In this paper, we consider the coefficient-based regularized distribution regression which aims to regress from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS), where the regularization is put on the coefficients and kernels are assumed to be indefinite. The algorithm involves two stages of sampling, the first stage sample consists of distributions and the second stage sample is obtained from these distributions. Asymptotic behaviors of the algorithm in different regularity ranges of the regression function are comprehensively studied and learning rates are derived via integral operator techniques. We get the optimal rates under some mild conditions, which matches the one-stage sampled minimax optimal rate. Compared with the kernel methods for distribution regression in the literature, the algorithm under consideration does not require the kernel to be symmetric and positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods, which enriches the theme of the distribution regression. To the best of our knowledge, this is the first result for distribution regression with indefinite kernels, and our algorithm can improve the saturation effect.
The Magnetic Field Environment of Active Region 12673 That Produced the Energetic Particle Events of September 2017

Forecasting solar energetic particles (SEPs), and identifying flares/coronal mass ejections (CMEs) from active regions (ARs) that will produce SEP events in advance is extremely challenging. We investigate the magnetic field environment of AR 12673, including the AR’s magnetic configuration, the surrounding field configuration in the vicinity of the AR, the decay index profile, and the footpoints of the Earth-connected magnetic field, around the time of four eruptive events. Two of the eruptive events are SEP productive (2017 September 4 at 20:00 UT and September 6 at 11:56 UT), while two are not (September 4 at 18:05 UT and September 7 at 14:33 UT). We analyzed a range of EUV and white-light coronagraph observations along with potential field extrapolations and find that the CMEs associated with the SEP-productive events either trigger null point reconnection that redirects flare-accelerated particles from the flare site to the Earth-connected field and/or have a significant expansion (and shock formation) into the open Earth-connected field. The rate of change of the decay index with height indicates that the region could produce a fast CME (v > 1500 km s−1), which it did during events 2 and 3. The AR’s magnetic field environment, including locations of open magnetic field and null points along with the magnetic field connectivity and propagation direction of the CMEs play an important role in the escape and arrival of SEPs at Earth. Other SEP-productive ARs should be investigated to determine whether their magnetic field environment and CME propagation direction are significant in the escape and arrival of SEPs at Earth.
MuLan: A Joint Embedding of Music Audio and Natural Language

Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This paper presents MuLan: a first attempt at a new generation of acoustic models that link music audio directly to unconstrained natural language music descriptions. MuLan takes the form of a two-tower, joint audio-text embedding model trained using 44 million music recordings (370K hours) and weakly-associated, free-form text annotations. Through its compatibility with a wide range of music genres and text styles (including conventional music tags), the resulting audio-text representation subsumes existing ontologies while graduating to true zero-shot functionalities. We demonstrate the versatility of the MuLan embeddings with a range of experiments including transfer learning, zero-shot music tagging, language understanding in the music domain, and cross-modal retrieval applications.
Deep Hypergraph Structure Learning

Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-based representation learning methods, such as hypergraph neural networks, highly depends on the quality of the hypergraph structure. How to generate the hypergraph structure among data is still a challenging task. Missing and noisy data may lead to"bad connections"in the hypergraph structure and destroy the hypergraph-based representation learning process. Therefore, revealing the high-order structure, i.e., the hypergraph behind the observed data, becomes an urgent but important task. To address this issue, we design a general paradigm of deep hypergraph structure learning, namely DeepHGSL, to optimize the hypergraph structure for hypergraph-based representation learning. Concretely, inspired by the information bottleneck principle for the robustness issue, we first extend it to the hypergraph case, named by the hypergraph information bottleneck (HIB) principle. Then, we apply this principle to guide the hypergraph structure learning, where the HIB is introduced to construct the loss function to minimize the noisy information in the hypergraph structure. The hypergraph structure can be optimized and this process can be regarded as enhancing the correct connections and weakening the wrong connections in the training phase. Therefore, the proposed method benefits to extract more robust representations even on a heavily noisy structure. Finally, we evaluate the model on four benchmark datasets for representation learning. The experimental results on both graph- and hypergraph-structured data demonstrate the effectiveness and robustness of our method compared with other state-of-the-art methods.
Biases to primordial non-Gaussianity measurements from CMB secondary anisotropies

Our view of the last-scattering surface in the cosmic microwave background (CMB) is obscured by secondary anisotropies, sourced by scattering, extragalactic emission and gravitational processes between recombination and observation. Whilst it is established that non-Gaussianity from the correlation between the integrated-Sachs–Wolfe (ISW) effect and gravitational lensing can significantly bias primordial non-Gaussianity (PNG) searches, recent work by Hill (2018) suggests that other combinations of secondary anisotropies can also produce significant biases. Building on that work, we use the WebSky and Sehgal et al. (2010) simulations to perform an extensive examination of possible biases to PNG measurements for the local, equilateral and orthogonal shapes. For a Planck-like CMB experiment, without foreground cleaning, we find significant biases from cosmic infrared background (CIB)-lensing and thermal Sunyaev–Zel’dovich (tSZ)-lensing bispectra for the local and orthogonal templates, and from CIB and tSZ bispectra for the equilateral template. For future experiments, such as the Simons Observatory, biases from correlations between the ISW effect and the tSZ and CIB will also become important. Finally we investigate the effectiveness of foreground-cleaning techniques to suppress these biases. We find that the majority of these biases are effectively suppressed by the internal-linear-combination method with a total bias below the 1 σ statistical error for both experiments. However, the small total bias arises from the cancellation of several 1 σ biases for Planck-like experiments and 2 σ biases for SO-like. As this cancellation is likely sensitive to the modelling, to ensure robustness against these biases, we recommend that explicit removal methods should be used.
On a Conjecture of Feige for Discrete Log-Concave Distributions

A remarkable conjecture of Feige (2006) asserts that for any collection of $n$ independent non-negative random variables $X_1, X_2, \dots, X_n$, each with expectation at most $1$, $$ \mathbb{P}(X<\mathbb{E}[X] + 1) \geq \frac{1}{e}, $$ where $X = \sum_{i=1}^n X_i$. In this paper, we investigate this conjecture for the class of discrete log-concave probability distributions and we prove a strengthened version. More specifically, we show that the conjectured bound $1/e$ holds when $X_i$'s are independent discrete log-concave with arbitrary expectation.
Variance Reduction based Experience Replay for Policy Optimization

For reinforcement learning on complex stochastic systems, it is desirable to effectively leverage the information from historical samples collected in previous iterations to accelerate policy optimization. Classical experience replay, while effective, treats all observations uniformly, neglecting their relative importance. To address this limitation, we introduce a novel Variance Reduction Experience Replay (VRER) framework, enabling the selective reuse of relevant samples to improve policy gradient estimation. VRER, as an adaptable method that can seamlessly integrate with different policy optimization algorithms, forms the foundation of our sample-efficient off-policy algorithm known as Policy Optimization with VRER (PG-VRER). Furthermore, the lack of a rigorous theoretical understanding of the experience replay method in the literature motivates us to introduce a novel theoretical framework that accounts for sample dependencies induced by Markovian noise and behavior policy interdependencies. This framework is then employed to analyze the finite-time convergence of our VRER-based policy optimization algorithm, revealing a crucial bias-variance trade-off in policy gradient estimates: the reuse of old experience introduces increased bias while simultaneously reducing gradient variance. Extensive experiments have shown that VRER offers a notable acceleration in learning optimal policies and enhances the performance of state-of-the-art (SOTA) policy optimization approaches.
Rectilinear Planarity of Partial 2-Trees

A graph is rectilinear planar if it admits a planar orthogonal drawing without bends. While testing rectilinear planarity is NP-hard in general (Garg and Tamassia, 2001), it is a long-standing open problem to establish a tight upper bound on its complexity for partial 2-trees, i.e., graphs whose biconnected components are series-parallel. We describe a new O(n^2)-time algorithm to test rectilinear planarity of partial 2-trees, which improves over the current best bound of O(n^3 \log n) (Di Giacomo et al., 2022). Moreover, for partial 2-trees where no two parallel-components in a biconnected component share a pole, we are able to achieve optimal O(n)-time complexity. Our algorithms are based on an extensive study and a deeper understanding of the notion of orthogonal spirality, introduced several years ago (Di Battista et al, 1998) to describe how much an orthogonal drawing of a subgraph is rolled-up in an orthogonal drawing of the graph.
Towards Robust Drone Vision in the Wild

The past few years have witnessed the burst of drone-based applications where computer vision plays an essential role. However, most public drone-based vision datasets focus on detection and tracking. On the other hand, the performance of most existing image superresolution methods is sensitive to the dataset, specifically, the degradation model between high-resolution and low-resolution images. In this thesis, we propose the first image superresolution dataset for drone vision. Image pairs are captured by two cameras on the drone with different focal lengths. We collect data at different altitudes and then propose pre-processing steps to align image pairs. Extensive empirical studies show domain gaps exist among images captured at different altitudes. Meanwhile, the performance of pretrained image superresolution networks also suffers a drop on our dataset and varies among altitudes. Finally, we propose two methods to build a robust image super-resolution network at different altitudes. The first feeds altitude information into the network through altitude-aware layers. The second uses one-shot learning to quickly adapt the super-resolution model to unknown altitudes. Our results reveal that the proposed methods can efficiently improve the performance of super-resolution networks at varying altitudes.
Causal Inference in Recommender Systems: A Survey and Future Directions

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.