Miracle-dz/UniFAR-FTUs huggingface dataset:
Namely, Painless: A unifying approach to safe programming with first-order syntax with binders. (Une approche unifiante pour programmer sûrement avec de la syntaxe du premier ordre contenant des lieurs)

This dissertation describes a novel approach to safe meta-programming. A meta-program is a program which processes programs or similar data. Compilers and theorem provers are prime examples of meta-programs which could benefit from this approach. To this end, this work focuses on the representation of names and binders in data structures. Programming errors are really easy to make with usual techniques. We propose an abstract interface to names and binders that rules out these errors. This interface is implemented as a library in Agda. It allows defining and manipulating term representations in nominal style. Thanks to abstraction, other styles are supported as well: the de Bruijn style, the combinations of these styles, and more. Whereas indexing the types of names and terms with a natural number is a well-known technique to better control de Bruijn indices, we index them with worlds. Worlds are at the same time more precise and more abstract than natural numbers. Via logical relations and parametricity, we are able to demonstrate in what sense our library is safe, and to obtain theorems for free about world-polymorphic functions. For instance, we prove that a world-polymorphic term transformation function must commute with any renaming of the free variables. The proof is entirely carried out in Agda. The usability of our technique is shown on several examples including normalization by evaluation which is known to be challenging. We show that our world-indexed approach can express a wide range of data types by embedding several definition languages from the literature.
Deep Learning-Driven Edge Video Analytics: A Survey

—Video, as a key driver in the global explosion of digital information, can create tremendous beneﬁts for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, trafﬁc control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classiﬁcation, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. A dedicated venue for collecting and summarizing the latest advances of EVA is highly desired by the community. Besides, the basic concepts of EVA (e.g., deﬁnition, architectures, etc.) are ambiguous and neglected by these surveys due to the rapid development of this domain. A thorough clariﬁcation is needed to facilitate a consensus on these concepts. To ﬁll in these gaps, we conduct a comprehensive survey of the recent efforts on EVA. In this paper, we ﬁrst review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.
Slip Detection for Grasp Stabilization With a Multifingered Tactile Robot Hand

Tactile sensing is used by humans when grasping to prevent us dropping objects. One key facet of tactile sensing is slip detection, which allows a gripper to know when a grasp is failing and take action to prevent an object being dropped. This study demonstrates the slip detection capabilities of the recently developed Tactile Model O (T-MO) robotic hand by using support vector machines to detect slip and test multiple slip scenarios including responding to the onset of slip in real time with 11 different objects in various grasps. In this article, we demonstrate the benefits of slip detection in grasping by testing two real-world scenarios: adding weight to destabilize a grasp and using slip detection to lift up objects at the first attempt. The T-MO is able to detect when an object is slipping, react to stabilize the grasp, and be deployed in real-world scenarios. This shows the T-MO is a suitable platform for autonomous grasping by using reliable slip detection to ensure a stable grasp in unstructured environments.
Posterior Regularisation on Bayesian Hierarchical Mixture Clustering

We study a recent inferential framework, named posterior regularisation, on the Bayesian hierarchical mixture clustering (BHMC) model. This framework facilitates a simple way to impose extra constraints on a Bayesian model to overcome some weakness of the original model. It narrows the search space of the parameters of the Bayesian model through a formalism that imposes certain constraints on the features of the found solutions. In this paper, in order to enhance the separation of clusters, we apply posterior regularisation to impose max-margin constraints on the nodes at every level of the hierarchy. This paper shows how the framework integrates with BHMC and achieves the expected improvements over the original Bayesian model.
Erosion and tectonics at the margins of continental plateaus

The topography across the eastern margin of the central Andean plateau north of 18°S (Beni region) bears a strong resemblance to the topography of the southern margin of the Tibetan plateau (Nepal Himalaya), with both regions featuring a steep frontal slope and high peaks at the plateau edge. In contrast, the topography of the eastern margin of the central Andean plateau south of 18°S (Pilcomayo region) tapers toward the foreland more gently and has no line of high peaks at the margin. Both the Himalayan and the Beni regions have been the sites for large amounts of denudation, and in both regions, geologic evidence suggests that erosion has been sufficiently vigorous for the physiographic plateau margin to have retreated toward the plateau interior during the Neogene. We hypothesize that the steep frontal slope and high peaks of the Beni region and Himalayan front largely reflect the high orographic precipitation and high erosion rates occurring in these regions and that the more gentle topography of the semiarid Pilcomayo region reflects a tectonic landform only slightly modified by erosion. We propose that orographic precipitation impinging on a plateau margin will generally tend to drop moisture low on the slope, eroding back the plateau while enhancing or maintaining the steep long-wavelength slope. A numerical model coupling orographic precipitation, erosion, and tectonic uplift demonstrates the plausibility of this hypothesis. The erosional efflux in both the Beni and Nepal Himalaya have been considerable, and simple mass balance calculations for the Himalaya suggest that during the Neogene, the erosional mass efflux has generally outpaced the tectonic mass influx. This contrasts with the apparent prior domination of tectonic influx and may reflect a decrease in the rate of tectonic addition during the same period, and/or increased late Cenozoic erosion rates.
Protocol Support for High Availability of IKEv2/IPsec

The IPsec protocol suite is widely used for the deployment of virtual private networks (VPNs). In order to make such VPNs highly available, more scalable and failure-resistant, these VPNs are implemented as IPsec High Availability (HA) clusters. However there are many issues in IPsec HA clustering, and in particular in IKEv2 clustering. An earlier document, "IPsec Cluster Problem Statement", enumerates the issues encountered in the IKEv2/IPsec HA cluster environment. This document attempts to resolve these issues with the least possible change to the protocol. This document proposes an extension to the IKEv2 protocol to solve the main issues of "IPsec Cluster Problem Statement" in the commonly deployed hot-standby cluster, and provides implementation advice for other issues. The main issues to be solved are the synchronization of IKEv2 Message ID counters, and of IPsec Replay Counters.
Effects of prolonged southward interplanetary magnetic field on low‐latitude ionospheric electron density

The present work describes the low‐latitude ionospheric variability during an unusually prolonged (~33 h) geomagnetically disturbed condition that prevailed during 15–16 July 2012. The low‐latitude electron density in summer hemisphere, investigated using ground‐ and satellite‐based observations, responded to this by generating strong negative ionospheric storm on 16 July. The maximum electron density on 16 July over Indian low latitudes was reduced by more than 50% compared to that on a geomagnetically quiet day (14 July 2012). In contrast to the extreme reduction in total electron content (TEC) in the Northern Hemisphere, TEC from a winter hemispheric station revealed substantial (~23 total electron content unit, 1 TECU = 1016 el m−2) enhancements on the same day. This contrasting hemispherical response in TEC is suggested to be due to the combined effects of strong interhemispheric and solar‐driven day‐night winds. Further, very weak equatorial electrojet (EEJ) strength on 16 July indicated that the westward electric field perturbations in the low‐latitude ionosphere were possibly due to the disturbance dynamo effect associated with meridional circulation from polar to equatorial latitudes. Interestingly, despite reduction in the integrated EEJ strength on 15 July, the low‐latitude electron density showed substantial enhancement, highlighting the significant effect of the positive ionospheric storm on the low‐latitude ionosphere. The roles of electrodynamical/neutral‐dynamical and compositional disturbances are discussed in view of these observations to understand low‐latitude ionospheric response when geomagnetic disturbance persists for longer duration.
Aridity on the Canadian Plains

The Prairie Ecozone is the only major region of Canada where drought is a landscape hazard; aridity is linked to soil erosion. Management of prairie ecosystems and soil landscapes therefore requires an understanding of past and future trends and variability in regional aridity. We used instrumental and paleoclimatic records to define a regional baseline for prairie aridity, to evaluate the utility of modern climate normals (i.e. 1961-1990) as a benchmark for future climatic change, and to provide a historical context for a range of General Circulation Model (GCM) forecasts of regional aridity. A warm-dry scenario derived from the Canadian GCM projects a significant increase in the area of subhumid and semiarid climate. Tree rings and diatom-inferred lake salinity record prolonged arid events and show that the climate normal period of 1961-1990 may have been the most benign climate of the past 750 years. The climate of the 20th century was anomalous in terms of the absence of sustained drought. Because both lake and tree-ring analyses recorded an abrupt amelioration of climatic conditions near the start of the instrumental record, we suggest that the immediate impacts of future global warming may be to return the prairies to past conditions in which persistent aridity was recorded for intervals of decades or longer.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining

Recent research on time-series self-supervised models shows great promise in learning semantic representations. However, it has been limited to small-scale datasets, e.g., thousands of temporal sequences. In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e.g., millions of temporal sequences. We adopt the Transformer architecture by first partitioning the input into non-overlapping windows. Each window is then characterized by its normalized shape and two scalar values denoting the mean and standard deviation within each window. To embed scalar values that may possess arbitrary numerical scales to high-dimensional vectors, we propose a numerically multi-scaled embedding module enumerating all possible scales for the scalar values. The model undergoes pretraining using the proposed numerically multi-scaled embedding with a simple contrastive objective on a large-scale dataset containing over a million sequences. We study its transfer performance on a number of univariate and multivariate classification benchmarks. Our method exhibits remarkable improvement against previous representation learning approaches and establishes the new state of the art, even compared with domain-specific non-learning-based methods.
Passive microwave airborne measurements of the sea surface response at 89 and 157 GHz

Microwave measurements of the ocean-roughened surface have been collected during several recent international experiments using an airborne radiometer observing at 89 and 157 GHz. The purpose of this project is to test and validate the sea emissivity model required for the future humidity sounder, advanced microwave sounder unit B, over a wide range of atmospheric and surface conditions. In this paper, the measurements are statistically analyzed and compared with a geometric optics model with special emphasis on the sensitivity to the input parameters. This model is shown to provide good overall agreement with the data when coupled to the wave slope description of Cox and Munk (1954), the liquid water dielectric permittivity of Liebe et al. (1991), and the foam coverage of Monahan and Lu (1990), after increasing the theoretical predictions by a bias of about 2.5 K at 89 GHz and 1.7 K at 157 GHz. In addition, an empirical emissivity algorithm derived from low-frequency observations (Hollinger, 1971; Stogryn, 1972) and widely used for satellite retrieval purposes is shown to be inappropriate for use at millimeter frequencies.
Kqueue - A Generic and Scalable Event Notification Facility

Applications running on a UNIX platform need to be notified when some activity occurs on a socket or other descriptor, and this is traditionally done with the select() or poll() system calls. However, it has been shown that the performance of these calls does not scale well with an increasing number of descriptors. These interfaces are also limited in the respect that they are unable to handle other potentially interesting activities that an application might be interested in, these might include signals, file system changes, and AIO completions. This paper presents a generic event delivery mechanism, which allows an application to select from a wide range of event sources, and be notified of activity on these sources in a scalable and efficient manner. The mechanism may be extended to cover future event sources without changing the application interface.
Approximation algorithms for NP-hard problems in combinatorial optimization

This thesis broadly focuses on the design and analysis of algorithmic tools leading to approximation algorithms with provably good performance guarantees. We were especially interested in problems that have highly practical importance yet possess a fundamental structure, such as integer covering, network design, graph partitioning, and discrete location problems. In particular, our main contributions revolve around algorithmic ideas and proof methods that are based on mathematical programming, polyhedral combinatorics, duality, and randomization. The highlights of this work can be briefly described as follows: 1. We devise the first polylogarithmic approximation for the generalized connectivity problem. 2. We present the first non-trivial approximation for the k-Steiner forest problem. 3. We propose the first non-trivial approximation for the k-generalized connectivity problem. 4. We improve on the currently best approximation for directed Steiner network. 5. We present a unified framework for approximating partial covering problems, and demonstrate the applicability of our method in diverse settings.
Three long‐lived anticyclonic eddies in the northern South China Sea

[1] In August 2007, three long-lived anticyclonic eddies (ACE1, ACE2, and ACE3) were detected by both satellite sea level anomaly (SLA) map and in situ measurements in the northern South China Sea (SCS). ACE3 had a two-core (ACE3(1) and ACE3(2)) structure. In situ stations along 18 degrees N almost cut through the centers of ACE2 and ACE3 (2). Near the centers of ACE2 and ACE3(2), mean temperature and sound velocity are similar to 0.65 degrees C and similar to 2 m s(-1) larger than those in their surrounding areas, respectively, while mean salinity and density are similar to 0.02 psu and similar to 0.15 m(3) s (1) smaller than those in their surrounding areas due to downwelling near the eddy cores. The depths of maximum and minimum salinity near the eddy cores are similar to 65 m and similar to 35 m larger than those in their surrounding areas. The vertical depth with current speed larger than 0.05 m s (1) can reach similar to 900 m. Their detailed evolutionary processes were depicted based on the variation of geostrophic currents and the trajectories of five drifting buoys. ACE1 lasted 147 days, while ACE2 and ACE3 lasted 168 days and 210 days, respectively. ACE1 had a smaller mean SLA (18.8 cm) in its lifetime than ACE2 (21.8 cm) and ACE3 (25.3 cm) but had a larger negative mean vorticity (-7.7 x 10(-6) s(-1)) than ACE2 (-7.0 x 10(-6) s(-1)) and ACE3 (-7.0 x 10(-6) s(-1)). One short-lived anticyclonic eddy that split from ACE2 and another one that merged with ACE3 both had a smaller SLA, negative vorticity, and diameter than ACE2 and ACE3, respectively.
A Quantum-Statistical Approach Toward Robot Learning by Demonstration

Statistical machine learning approaches have been at the epicenter of the ongoing research work in the field of robot learning by demonstration over the past few years. One of the most successful methodologies used for this purpose is a Gaussian mixture regression (GMR). In this paper, we propose an extension of GMR-based learning by demonstration models to incorporate concepts from the field of quantum mechanics. Indeed, conventional GMR models are formulated under the notion that all the observed data points can be assigned to a distinct number of model states (mixture components). In this paper, we reformulate GMR models, introducing some quantum states constructed by superposing conventional GMR states by means of linear combinations. The so-obtained quantum statistics-inspired mixture regression algorithm is subsequently applied to obtain a novel robot learning by demonstration methodology, offering a significantly increased quality of regenerated trajectories for computational costs comparable with currently state-of-the-art trajectory-based robot learning by demonstration approaches. We experimentally demonstrate the efficacy of the proposed approach.
Designing High Performance Web-Based Computing Services to Promote Telemedicine Database Management System

Many web computing systems are running real time database services where their information change continuously and expand incrementally. In this context, web data services have a major role and draw significant improvements in monitoring and controlling the information truthfulness and data propagation. Currently, web telemedicine database services are of central importance to distributed systems. However, the increasing complexity and the rapid growth of the real world healthcare challenging applications make it hard to induce the database administrative staff. In this paper, we build an integrated web data services that satisfy fast response time for large scale Tele-health database management systems. Our focus will be on database management with application scenarios in dynamic telemedicine systems to increase care admissions and decrease care difficulties such as distance, travel, and time limitations. We propose three-fold approach based on data fragmentation, database websites clustering and intelligent data distribution. This approach reduces the amount of data migrated between websites during applications' execution; achieves cost-effective communications during applications' processing and improves applications' response time and throughput. The proposed approach is validated internally by measuring the impact of using our computing services' techniques on various performance features like communications cost, response time, and throughput. The external validation is achieved by comparing the performance of our approach to that of other techniques in the literature. The results show that our integrated approach significantly improves the performance of web database systems and outperforms its counterparts.
Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology

We present a Systems Biology Toolbox for the widely used general purpose mathematical software MATLAB. The toolbox offers systems biologists an open and extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological and biochemical systems. Additionally it is well suited for educational purposes. The toolbox supports the Systems Biology Markup Language (SBML) by providing an interface for import and export of SBML models. In this way the toolbox connects nicely to other SBML-enabled modelling packages. Models are represented in an internal model format and can be described either by entering ordinary differential equations or, more intuitively, by entering biochemical reaction equations. The toolbox contains a large number of analysis methods, such as deterministic and stochastic simulation, parameter estimation, network identification, parameter sensitivity analysis and bifurcation analysis.
TomoGAN: Low-Dose X-Ray Tomography with Generative Adversarial Networks

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions. Here we present TomoGAN, a novel denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions, as at smaller length scales where higher radiation doses are required to resolve sample features. Our trained model, unlike other machine-learning-based solutions, is generic: it can be applied to many datasets collected at varying experimental conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases, angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose. Evaluation with both simulated and experimental datasets shows that our approach can reduce noise in reconstructed images significantly, improving the structural similarity score for simulation and experimental data with ground truth from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with simultaneous iterative reconstruction.