Miracle-dz/UniFAR-FTUs huggingface dataset:
Counting whales in the ice

140 | DEC–JAN | 2008 For the first time, Australian scientists will use aircraft to count minke whales in the pack ice around Antarctica. Since 1978 the International Whaling Commission (IWC) has been counting whales in the Southern Ocean for management and conservation purposes. Each year ships, provided by Japan, cover about one-tenth of the Southern Ocean, with each survey in the unstrengthened vessels necessarily ending at the edge of the pack ice around Antarctica. Thus, every 10 years, a circumpolar snapshot of whale abundance is obtained. Surveys over the past two decades, however, suggest there has been a significant decline in minke whale abundance, leading to disputes over whether the decline is genuine, or an artefact caused by the survey technique. One theory is that changes in the ice edge boundary each year, and changes in the number of minke whales present in the pack ice beyond this boundary, could be responsible for the differences in estimates of the whales in open water. In other words, could there be more minke whales hiding under the hundreds of kilometres of pack ice (and open areas within the pack ice), where the ships can’t search? Using icebreaker ships to access the pack ice is not ideal for a number of reasons, including the whales’ responses to engine noise. In the 2007–08 Antarctic season,1 a team from the Australian Antarctic Division aim to use the Division’s two C212 fixed-wing aircraft to trial an aerial survey of the relative abundance of whales off the coastline adjacent to Casey, Australia’s closest Antarctic station, located in the Windmill Islands. The plan is to fly the planes at an altitude of about 180 m and cover at least 2500 nautical miles – flying over a mixture of pack ice and open water. The aerial survey will be conducted at the same time as the annual ship-based survey (the Southern Ocean Whale Ecosystem Research survey), covering the pack ice region that the ship cannot access. It will be done in such a way that the data from both methods can be compared. Information from the surveys will be used by the IWC, but will also contribute to the Australian Antarctic Division’s research (through the Australian Centre for Applied Marine Mammal Science) into minke whale distribution and abundance, and their interaction with other whale species and krill predators. The research feeds into conservation management, setting sustainable catch limits for whales and krill, and contributes to scientists’ understanding of ecosystem changes due to climate change. This first aerial survey will also test the reliability of this research technique. • Wendy Pyper
Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.
Federated Learning for Vehicular Networks

Machine learning (ML) has already been adopted in vehicular networks for such applications as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, the training of the ML model brings significant complexity for the data transmission between the learning model in a cloud server and the edge devices in the vehicles. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing this transmission overhead while also achieving privacy through the transmission of only the gradients of the learnable parameters rather than the whole dataset. In this article, we provide a comprehensive analysis of the usage of FL over ML in vehicular network applications to develop intelligent transportation systems. Based on the real image and lidar data collected from the vehicles, we illustrate the superior performance of FL over ML in terms of data transmission complexity for vehicular object detection application. Finally, we highlight major research issues and identify future research directions on system heterogeneity, data heterogeneity, efficient model training and reducing transmission complexity in FL based vehicular networks.
Interactions Between Deghosting and Other Processing Steps - Searching for the Optimal Processing Order

Broadband seismic processing has become a key technique to get more out of the data. Deghosting is a key part of broadband techniques, but the location in the processing flow varies significantly. The deghosting operation is performed anywhere from immediately after acquisition to imaging. We will look a the interaction between deghosting and such techniques as demultiple, debubble, residual designature and denoise techniques. We hope to shed some light on the optimal location for deghosting in the processing flow.
A modular system for collaborative desktop VR/AR with a shared workspace

We present a collaborative desktop virtual and augmented reality system with a shared physical workspace: the joint space station. It is constructed out of multiple, single-user near-field virtual or augmented reality stations using mirror-based displays. The joint space station provides high-resolution stereoscopic images and direct 3D interaction to all participants. The environment is modular, flexible, low-cost, and can be used under office working conditions. We describe the design, implementation, and application of a prototype joint space station, and discuss the merits and limitations of the concept.
Linking Coastal Disasters and Migration: A Case Study of Kutubdia Island, Bangladesh*

On 4 August 2012, the southeastern part of Kutubdia Island, Bangladesh, experienced coastal erosion and flooding associated with tidal waves, storms, and continuous heavy rainfall for two days. This provided a unique opportunity to study the relationship between rapid-onset disasters and migration decisions of individual households perpetually living under environmental risk. Using Kutubdia Island as a case study, this article examines the migration decisions of respondents in the immediate aftermath of two extreme natural events. Relevant data were primarily collected from a questionnaire survey conducted nearly two months after the occurrence of the event. The study's findings support the view that both environmental and nonenvironmental factors lead to migration. The article outlines several areas for further research on the links between migration and environmental stress.
Apache Flink™: Stream and Batch Processing in a Single Engine

Apache Flink 1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continuous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataﬂows. In this paper, we present Flink’s architecture and expand on how a (seemingly diverse) set of use cases can be uniﬁed under a single execution model.
Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data

Recent years have seen progress in the development of statistically rigorous frameworks to infer outbreak transmission trees (“who infected whom”) from epidemiological and genetic data. Making use of pathogen genome sequences in such analyses remains a challenge, however, with a variety of heuristic approaches having been explored to date. We introduce a statistical method exploiting both pathogen sequences and collection dates to unravel the dynamics of densely sampled outbreaks. Our approach identifies likely transmission events and infers dates of infections, unobserved cases and separate introductions of the disease. It also proves useful for inferring numbers of secondary infections and identifying heterogeneous infectivity and super-spreaders. After testing our approach using simulations, we illustrate the method with the analysis of the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome (SARS), providing new insights into the early stage of this epidemic. Our approach is the first tool for disease outbreak reconstruction from genetic data widely available as free software, the R package outbreaker. It is applicable to various densely sampled epidemics, and improves previous approaches by detecting unobserved and imported cases, as well as allowing multiple introductions of the pathogen. Because of its generality, we believe this method will become a tool of choice for the analysis of densely sampled disease outbreaks, and will form a rigorous framework for subsequent methodological developments.
One Hop Lookups for Peer-to-Peer Overlays

Current peer-to-peer lookup algorithms have been designed with the assumption that routing information at each member node must be kept small, so that the bookkeeping required to respond to system membership changes is also small. In this paper, we show that this assumption is unnecessary, and present a technique that maintains complete routing tables at each node. The technique is able to handle frequent membership changes and scales to large systems having more than a million nodes. The resulting peer-to-peer system is robust and can route lookup queries in just one hop, thus enabling applications that cannot tolerate the delay of multi-hop routing.
Matrix Completion with Noise via Leveraged Sampling

Many matrix completion methods assume that the data follows the uniform distribution. To address the limitation of this assumption, Chen et al. \cite{Chen20152999} propose to recover the matrix where the data follows the specific biased distribution. Unfortunately, in most real-world applications, the recovery of a data matrix appears to be incomplete, and perhaps even corrupted information. This paper considers the recovery of a low-rank matrix, where some observed entries are sampled in a \emph{biased distribution} suitably dependent on \emph{leverage scores} of a matrix, and some observed entries are uniformly corrupted. Our theoretical findings show that we can provably recover an unknown $n\times n$ matrix of rank $r$ from just about $O(nr\log^2 n)$ entries even when the few observed entries are corrupted with a small amount of noisy information. Empirical studies verify our theoretical results.
Predicting the Geometry of Metal Binding Sites from Protein Sequence

Metal binding is important for the structural and functional characterization of proteins. Previous prediction efforts have only focused on bonding state, i.e. deciding which protein residues act as metal ligands in some binding site. Identifying the geometry of metal-binding sites, i.e. deciding which residues are jointly involved in the coordination of a metal ion is a new prediction problem that has been never attempted before from protein sequence alone. In this paper, we formulate it in the framework of learning with structured outputs. Our solution relies on the fact that, from a graph theoretical perspective, metal binding has the algebraic properties of a matroid, enabling the application of greedy algorithms for learning structured outputs. On a data set of 199 non-redundant metalloproteins, we obtained precision/recall levels of 75%/46% correct ligand-ion assignments, which improves to 88%/88% in the setting where the metal binding state is known.
Difference in particle formation at a mountaintop location during spring and summer: Implications for the role of sulfuric acid and organics in nucleation

New particle formation (NPF) has been observed frequently at Storm Peak Laboratory (SPL), a high‐elevation mountaintop observatory in Colorado. A detailed analysis of field measurements taken in March and July 2012 at SPL reveals significant and interesting differences in NPF during the spring and summer months. Persistent long‐lasting NPF occurred on a daily basis in March but was absent in July. Bursts of ultrafine particles did occur frequently in July, but such bursts were short‐lasting and did not show any obvious pattern. A global chemical transport model (GEOS‐Chem) coupled with a size‐resolved advanced particle microphysics model is used to interpret in‐depth this observed nucleation phenomenon. The model captures well the observed persistent daily nucleation events in March and the absence of regional‐scale NPF in July. Model simulations indicate that aerosol precursors were dominated by H2SO4 gas in March and by low‐volatile secondary organic gases (LV‐SOGs) in July, which are consistent with previous particle composition measurements at SPL. The observed persistent daily NPF in March and the absence of regional‐scale nucleation in July at SPL indicate that H2SO4 gas plays a much more critical role in the initial nucleation process, although LV‐SOGs dominate particle growth in July. Our analysis suggests that the short‐lasting bursts of ultrafine particles observed at SPL in July are likely associated with nucleation in subgrid power plant plumes, where concentrations of H2SO4 rather than LV‐SOGs are expected to be higher.
Evaluation of PyTorch as a Data-Parallel Programming API for GPU Volume Rendering

Data-parallel programming (DPP) has attracted considerable interest from the visualization community, fostering major software initiatives such as VTK-m. However, there has been relatively little recent investigation of data-parallel APIs in higher-level languages such as Python, which could help developers sidestep the need for low-level application programming in C++ and CUDA. Moreover, machine learning frameworks exposing data-parallel primitives, such as PyTorch and TensorFlow, have exploded in popularity, making them attractive platforms for parallel visualization and data analysis. In this work, we benchmark data-parallel primitives in PyTorch, and investigate its application to GPU volume rendering using two distinct DPP formulations: a parallel scan and reduce over the entire volume, and repeated application of data-parallel operators to an array of rays. We ﬁnd that most relevant DPP primitives exhibit performance similar to a native CUDA library. However, our volume rendering implementation reveals that PyTorch is limited in expressiveness when compared to other DPP APIs. Furthermore, while render times are sufﬁcient for an early “proof of concept”, memory usage acutely limits scalability.
Handbook of Research on Text and Web Mining Technologies

Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Handbook of research on text and web mining techologies / Min Song and Yi-Fang Wu, editors. p. cm. Includes bibliographical references and index. Summary: "This handbook presents recent advances and surveys of applications in text and web mining of interests to researchers and end-users "-Provided by publisher. All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of the publisher. If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating the library's complimentary electronic access to this publication. AbstrAct In this chapter, we present a thesaurus application in the field of text mining and more specifically automatic indexing on the set of descriptors defined by a thesaurus. We begin by presenting various definitions and a mathematical thesaurus model, and also describe various examples of real world thesauri which are used in official institutions. We then explore the problem of thesaurus-based automatic indexing by describing its difficulties and distinguishing features and reviewing previous work in this area. Finally, we propose various lines of future research.
Determination of in situ attenuation from full waveform acoustic logs

Method for the determination of in situ P and S wave attenuation from full waveform acoustic logs are developed. For P waves, the peak amplitude ratios of the refracted P waves from two different receivers can be used with geometrical spreading taken into account. For S waves, owing to the contamination by the guided waves, its attenuation cannot be determined directly. Instead, S wave attenuation is determined from the attenuation of the guided waves using the partition coefficients (normalized partial derivatives of the phase velocity with respect to the body wave velocities). Analytical forms of these partition coefficients are presented here, along with examples for a number of different rock formations (granite, limestone, sandstone and soft sediments). The results show that in high velocity rocks, the fluid attenuation controls the guided wave attenuation except near the cut-off frequency of the pseudo-Rayleigh wave. For low velocity rock formations, especially in the case where the S wave velocity is lower than the fluid velocity, the S wave attenuation is the main contributor to the guided wave attenuation. Synthetic microseisgmogram calculated with the measured body wave attenuation agrees well with the actual microseismograms.
iCap: Interative Image Captioning with Predictive Text

ABSTRACT In this paper we study a brand new topic of interactive image captioning with human in the loop. Dierent from automated image captioning where a given test image is the sole input in the inference stage, we have access to both the test image and a sequence of (incomplete) user-input sentences in the interactive scenario. We formulate the problem as Visually Conditioned Sentence Completion (VCSC). For VCSC, we propose asynchronous bidirectional decoding for image caption completion (ABD-Cap). With ABD-Cap as the core module, we build iCap, a web-based interactive image captioning system capable of predicting new text with respect live input from a user. A number of experiments covering both automated evaluations and real user studies show the viability of our proposals.
A 0.8mm2 all-digital SAW-less polar transmitter in 65nm EDGE SoC

EDGE is currently the most widely used standard for data communications in mobile phones. Its proliferation has led to a need for low-cost 2.5G mobile solutions. The implementation of RF circuits in nanoscale digital CMOS with no or minimal process enhancements is one of the key obstacles limiting the complete SoC integration of cellular radio functionality with digital baseband. The key challenges for such RF integration include non-linearity of devices and circuits, device mismatches, process parameter spread, and the increasing potential for self-interference that could be induced by one function in the SoC onto another.
Abnormal events detection in crowded scenes by trajectory cluster

Abnormal events detection in crowded scenes has been a challenge due to volatility of the definitions for both normality and abnormality, the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. A novel framework was proposed for the detection of unusual events in crowded scenes using trajectories produced by moving pedestrians based on an intuition that the motion patterns of usual behaviors are similar to these of group activity, whereas unusual behaviors are not. First, spectral clustering is used to group trajectories with similar spatial patterns. Different trajectory clusters represent different activities. Then, unusual trajectories can be detected using these patterns. Furthermore, behavior of a mobile pedestrian can be defined by comparing its direction with these patterns, such as moving in the opposite direction of the group or traversing the group. Experimental results indicated that the proposed algorithm could be used to reliably locate the abnormal events in crowded scenes.
A Barrier-Lyapunov Actor-Critic Reinforcement Learning Approach for Safe and Stable Control

—Reinforcement learning (RL) has demonstrated impressive performance in various areas such as video games and robotics. However, ensuring safety and stability, which are two critical properties from a control perspective, remains a signiﬁcant challenge when using RL to control real-world systems. In this paper, we ﬁrst provide deﬁnitions of safety and stability for the RL system, and then combine the control barrier function (CBF) and control Lyapunov function (CLF) methods with the actor-critic method in RL to propose a Barrier-Lyapunov Actor-Critic (BLAC) framework which helps maintain the aforementioned safety and stability for the system. In this framework, CBF constraints for safety and CLF constraint for stability are constructed based on the data sampled from the replay buffer, and the augmented Lagrangian method is used to update the parameters of the RL-based controller. Furthermore, an additional backup controller is introduced in case the RL-based controller cannot provide valid control signals when safety and stability constraints cannot be satisﬁed simultaneously. Simulation results 1 show that this framework yields a controller that can help the system approach the desired state and cause fewer violations of safety constraints compared to baseline algorithms.
Recurrent Parameter Generators

We present a generic method for recurrently using the same parameters for many different convolution layers to build a deep network. Speciﬁcally, for a network, we create a recurrent parameter generator (RPG), from which the parameters of each convolution layer are generated. Though using recurrent models to build a deep convolutional neural network (CNN) is not entirely new, our method achieves signiﬁcant performance gain compared to the existing works. We demonstrate how to build a one-layer neural network to achieve similar performance compared to other traditional CNN models on various applications and datasets. Such a method allows us to build an arbitrarily complex neural network with any amount of parameters. For example, we build a ResNet34 with model parameters reduced by more than 400 times, which still achieves 41 . 6% ImageNet top-1 accuracy. Furthermore, we demonstrate the RPG can be applied at different scales, such as layers, blocks, or even sub-networks. Speciﬁcally, we use the RPG to build a ResNet18 network with the number of weights equivalent to one convolutional layer of a conventional ResNet and show this model can achieve 67 . 2% ImageNet top-1 accuracy. The proposed method can be viewed as an inverse approach to model compression. Rather than removing the unused parameters from a large model, it aims to squeeze more information into a small number of parameters. Extensive experiment results are provided to demonstrate the power of the proposed recurrent parameter generator.