Reviewerly/exHarmony huggingface dataset:
Think Globally, Act Locally: Electronic Resources and Collection Development

Despite numerous cooperative collection development endeavors, the building of library collections has remained a highly individual and local practice. The physicality of bound volumes has posed a distinct limitation on our ability to share collections, although libraries have made huge strides in recent years. Electronic resources and the ability to digitize our physical holdings offer the potential to redirect our investments in collection building to the creation of a global network that would serve an international community of scholars. By facilitating the creation of discipline-based portals to knowledge resources, librarians can channel their efforts to the benefit of many without sacrificing the quality of local relationships. To accomplish this, libraries need to develop collectively built and managed Web sites that supplant the need for autonomous, selector-created webliographies and that greatly expand the number of sources that can be identified and described. Selectors, freed of the individual responsibility to shoulder the increasingly heavy and ultimately unsustainable load of tracking a proliferation of resources in a variety of formats and states of publication, can turn their attention to the capture of more elusive, but important, material; to more detailed evaluation of the use of information resources; toward improvements in the user interface of portals; or toward the transformation of scholarly communication, with the discipline-based portal serving as a magnet for attracting new forms of scholarly thought and research.
Path Planning on Grids: The Effect of Vertex Placement on Path Length

Video-game designers often tessellate continuous 2-dimensional terrain into a grid of blocked and unblocked square cells. The three main ways to calculate short paths on such a grid are to determine truly shortest paths, shortest vertex paths and shortest grid paths, listed here in decreasing order of computation time and increasing order of resulting path length. We show that, for both vertex and grid paths on both 4-neighbor and 8-neighbor grids, placing vertices at cell corners rather than at cell centers tends to result in shorter paths. We quantify the advantage of cell corners over cell centers theoretically with tight worst-case bounds on the ratios of path lengths, and empirically on a large set of benchmark test cases. We also quantify the advantage of 8-neighbor grids over 4-neighbor grids.
The Computational Complexity of Angry Birds and Similar Physics-Simulation Games

This paper presents several proofs for the computational complexity of the popular physics-based puzzle game AngryBirds. By using a combination of different gadgets within this game’s environment, we can demonstrate that the problem of solving Angry Birds levels is NP-hard. Proof of NP-hardness is by reduction from a known NP-complete problem, in this case 3-SAT. In addition, we are able to show that the original version of Angry Birds is within NP and therefore alsoNP-complete. These proofs can be extended to other physics-based games with similar mechanics.
Decreasing the Size of the Restricted Boltzmann Machine

In this letter, we propose a method to decrease the number of hidden units of the restricted Boltzmann machine while avoiding a decrease in the performance quantified by the Kullback-Leibler divergence. Our algorithm is then demonstrated by numerical simulations.
Optimal las vegas approximate near neighbors in lp

We show that approximate near neighbor search in high dimensions can be solved in a Las Vegas fashion (i.e., without false negatives) for lp (1 ≤ p ≤ 2) while matching the performance of optimal locality-sensitive hashing. Specifically, we construct a data-independent Las Vegas data structure with query time O(dnρ) and space usage O(dn1+ρ) for (r, cr)-approximate near neighbors in Rd under the lp norm, where ρ = 1/cp+o(1). Furthermore, we give a Las Vegas locality-sensitive filter construction for the unit sphere that can be used with the data-dependent data structure of Andoni et al. (SODA 2017) to achieve optimal space-time tradeoffs in the data-dependent setting. For the symmetric case, this gives us a data-dependent Las Vegas data structure with query time O(dnρ) and space usage O(dn1+ρ) for (r, cr)-approximate near neighbors in Rd under the lp norm, where ρ = 1/(2cp − 1) + o(1).Our data-independent construction improves on the recent Las Vegas data structure of Ahle (FOCS 2017) for lp when 1
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support

Autonomous robotics and artificial intelligence techniques can be used to support human personnel in the event of critical incidents. These incidents can pose great danger to human life. Some examples of such assistance include: multi-robot surveying of the scene; collection of sensor data and scene imagery, real-time risk assessment and analysis; object identification and anomaly detection; and retrieval of relevant supporting documentation such as standard operating procedures (SOPs). These incidents, although often rare, can involve chemical, biological, radiological/nuclear or explosive (CBRNE) substances and can be of high consequence. Real-world training and deployment of these systems can be costly and sometimes not feasible. For this reason, we have developed a realistic 3D model of a CBRNE scenario to act as a testbed for an initial set of assisting AI tools that we have developed (This research has received funding from the European Union’s Horizon 2020 Programme under grant agreement No. 700264.).
Scene layout priming relies primarily on low-level features rather than scene layout

The ability to perceive and remember the spatial layout of a scene is critical to understanding the visual world, both for navigation and for other complex tasks that depend upon the structure of the current environment. However, surprisingly little work has investigated how and when scene layout information is maintained in memory. One prominent line of work investigating this issue is a scene-priming paradigm (e.g., Sanocki & Epstein, 1997), in which different types of previews are presented to participants shortly before they judge which of two regions of a scene is closer in depth to the viewer. Experiments using this paradigm have been widely cited as evidence that scene layout information is stored across brief delays and have been used to investigate the structure of the representations underlying memory for scene layout. In the present experiments, we better characterize these scene-priming effects. We find that a large amount of visual detail rather than the presence of depth information is necessary for the priming effect; that participants show a preview benefit for a judgment completely unrelated to the scene itself; and that preview benefits are susceptible to masking and quickly decay. Together, these results suggest that “scene priming” effects do not isolate scene layout information in memory, and that they may arise from low-level visual information held in sensory memory. This broadens the range of interpretations of scene priming effects and suggests that other paradigms may need to be developed to selectively investigate how we represent scene layout information in memory.
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI

AutoML has become a popular service that is provided by most leading cloud service providers today. In this paper, we focus on the AutoML problem from the \emph{service provider's perspective}, motivated by the following practical consideration: When an AutoML service needs to serve {\em multiple users} with {\em multiple devices} at the same time, how can we allocate these devices to users in an efficient way? We focus on GP-EI, one of the most popular algorithms for automatic model selection and hyperparameter tuning, used by systems such as Google Vizer. The technical contribution of this paper is the first multi-device, multi-tenant algorithm for GP-EI that is aware of \emph{multiple} computation devices and multiple users sharing the same set of computation devices. Theoretically, given $N$ users and $M$ devices, we obtain a regret bound of $O((\text{\bf {MIU}}(T,K) + M)\frac{N^2}{M})$, where $\text{\bf {MIU}}(T,K)$ refers to the maximal incremental uncertainty up to time $T$ for the covariance matrix $K$. Empirically, we evaluate our algorithm on two applications of automatic model selection, and show that our algorithm significantly outperforms the strategy of serving users independently. Moreover, when multiple computation devices are available, we achieve near-linear speedup when the number of users is much larger than the number of devices.
Lower bounds for oblivious data structures

An oblivious data structure is a data structure where the memory access patterns reveals no information about the operations performed on it. Such data structures were introduced by Wang et al. [ACM SIGSAC'14] and are intended for situations where one wishes to store the data structure at an untrusted server. One way to obtain an oblivious data structure is simply to run a classic data structure on an oblivious RAM (ORAM). Until very recently, this resulted in an overhead of ω(lg n) for the most natural setting of parameters. Moreover, a recent lower bound for ORAMs by Larsen and Nielsen [CRYPTO'18] show that they always incur an overhead of at least Ω(lg n) if used in a black box manner. To circumvent the ω(lg n) overhead, researchers have instead studied classic data structure problems more directly and have obtained efficient solutions for many such problems such as stacks, queues, deques, priority queues and search trees. However, none of these data structures process operations faster than Θ(lg n), leaving open the question of whether even faster solutions exist. In this paper, we rule out this possibility by proving Ω(lg n) lower bounds for oblivious stacks, queues, deques, priority queues and search trees.
Targeted Syntactic Evaluation of Language Models

We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM’s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.
Human Activity Recognition in Smart-Home Environments for Health-Care Applications

With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of cognitive decline. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This talk presents our latest research efforts on these topics. In particular, the talk will cover: a) novel unobtrusive sensing solutions, b) hybrid ADLs recognition methods and c) techniques to detect abnormal behaviors at a fine granularity. We will discuss those challenges reporting our experience and identifying critical aspects which still need to be investigated.
Colorless green recurrent networks dream hierarchically

Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (The colorless green ideas I ate with the chair sleep furiously), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence.
Real-Time Planning as Decision-Making under Uncertainty

In real-time planning, an agent must select the next action to take within a fixed time bound. Many popular real-time heuristic search methods approach this by expanding nodes using time-limited A* and selecting the action leading toward the frontier node with the lowest f value. In this paper, we reconsider real-time planning as a problem of decision-making under uncertainty. We propose treating heuristic values as uncertain evidence and we explore several backup methods for aggregating this evidence. We then propose a novel lookahead strategy that expands nodes to minimize risk, the expected regret in case a non-optimal action is chosen. We evaluate these methods in a simple synthetic benchmark and the sliding tile puzzle and find that they outperform previous methods. This work illustrates how uncertainty can arise even when solving deterministic planning problems, due to the inherent ignorance of time-limited search algorithms about those portions of the state space that they have not computed, and how an agent can benefit from explicitly metareasoning about this uncertainty.
Spatial carrier phase shifting method for the phase recovery from two interferograms with closed fringes

A procedure is developed to recover the phase from two interferograms with a non-constant phase shift between them. This method may find broad applications when environmental disturbances do not allow implementing traditional phase shifting techniques which normally require three or more images. A spatial carrier phase shifting method is implemented to analyze the two measured temporal phase shifted interferometric images. This approach is very fast, avoids the use of fringe normalization techniques, preserves good tolerance against small (λ/20) or non-constant phase shifts, background and modulation variations and noise as tested in real and simulated data.
Removal of Visual Disruption Caused by Rain Using Cycle-Consistent Generative Adversarial Networks

This paper addresses the problem of removing rain disruption from images for outdoor vision systems. The Cycle-Consistent Generative Adversarial Network (CycleGAN) is proposed as a more promising rain removal algorithm, as compared to the state-of-the-art Image De-raining Conditional Generative Adversarial Network (ID-CGAN). The CycleGAN has an advantage in its ability to learn the underlying relationship between the rain and rain-free domain without the need of paired domain examples. Based on rain physical properties and its various phenomena, five broad categories of real rain distortions are proposed in this paper. For a fair comparison, both networks were trained on the same set of synthesized rain-and-ground-truth image-pairs provided by the ID-CGAN work, and subsequently tested on real rain images which fall broadly under these five categories. The comparison results demonstrated that the CycleGAN is superior in removing real rain distortions.
Exploring the Reasons Behind the Good Performance of Opposition-Based Learning

In the face of these diverse forms of opposition existed widely in real-world contexts, as a novel concept in computational intelligence, opposition-based learning (OBL) was originally introduced to accelerate the population-based algorithm. In addition, its superiority has been proved mathematically and experimentally. Two key elements (function prototype and algorithm design) that influence the algorithm performance, however, has not yet fully discussed in the past decade. In this paper, two OBL strategies are reexamined in respect of function prototype and algorithm design. In the first part of this paper, considering the position relationship between the optimal solution and the center point, some well-known benchmark functions are divided into three categories. Then, quasi-opposition-based differential evolution (QODE) is investigated by two approaches: solving several benchmark functions of various function types and solving the same functions with a different optimal solution. The numerical experiments reveal that “smart” matching between the benchmark functions and the QOBL is an important factor to the good performance of QODE. In the second part of this paper, a novel individual-based embedding method is proposed to coincide with the classical definition of opposition-based optimization. Then, two opposition-based differential evolution algorithms are compared to discuss the differences between the two embedding methods. The experimental results confirm that the convergence differences stem from the embedding method chosen in the OBL scheme rather than the utilization rate of opposite points. Furthermore, the impacts caused by various function types and jumping rate are also discussed.
Machine Learning for Disaster Risk Management

Evidence-driven disaster risk management (DRM) relies upon many different data types, information sources, and types of models to be effective. Tasks such as weather modelling, earthquake fault line rupture, or the development of dynamic urban exposure measures involve complex science and large amounts of data from a range of sources. Even experts can struggle to develop models that enable the understanding of the potential impacts of a hazard on the built environment and society.In this context, this guidance note explores how new approaches in machine learning can provide newways of looking into these complex relationships and provide more accurate, efficient, and useful answers. The goal of this document is to provide a concise, demystifying reference that readers, from project managers to data scientists, can use to better understand how machine learning can be applied in disaster risk management projects. There are many sources of information on this complex and evolving set of technologies. Therefore, this guidance note is aimed to be as focused as possible, providing basic information and DRM-specific case studies and directing readers to additional resources including online videos, infographics, courses, and articles for further reference. A machine learning (ML) algorithm is a type of computer program that learns to perform specific tasks based on various data inputs or rules provided by its designer. Machine learning is a subset of artificial intelligence (AI), but the two terms are often used interchangeably. For a thorough discussion of the differences and similarities of the terms ML and AI, see Section 2. As the name implies, an ML algorithm’s purpose is to “learn” from previous data and output a result that adds information and insight that was not previously known. This approach enables actions to be taken on the information gathered from the data; sometimes in near real time, like suggested web search results, and sometimes with longer term human input, like many of the DRM case studies presented in this document. Over the past few decades, there has been an enormous increase incomputational capacity and speed and available sensor data, exponentially increasing the volume of available data for analysis. This has allowed the capabilities of ML algorithms to advance to nearly ubiquitous impact on many aspects of society. Machine learning and artificial intelligencehave become household terms, crossing from academia and specialized industry applications into everyday interactions with technology—from image, sound, and voice recognition features of our smartphones to seamlessly recommending items in online shopping, from mail sorting to ranking results of a search engine. The same technology is being leveraged to answer bigger questions in society, including questions about sustainable development, humanitarian assistance, and disaster risk management.
SHAMANN: Shared Memory Augmented Neural Networks

Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an image-to-image mapping. While these methods effectively exploit global image context, the learning and computational complexities are high. We propose shared memory augmented neural network actors as a dynamically scalable alternative. Based on a decomposition of the image into a sequence of local patches, we train such actors to sequentially segment each patch. To further increase the robustness and better capture shape priors, an external memory module is shared between different actors, providing an implicit mechanism for image information exchange. Finally, the patch-wise predictions are aggregated to a complete segmentation mask. We demonstrate the benefits of the new paradigm on a challenging lung segmentation problem based on X-Ray images, as well as on two synthetic tasks based on MNIST. On the X-Ray data, our method achieves state-of-the-art accuracy with a significantly reduced model size, 3–5 times compared to reference methods. In addition, we reduce the number of failure cases by at least half.