Miracle-dz/UniFAR-FTUs huggingface dataset:
How to use Persistent Memory in your Database

Persistent or Non Volatile Memory (PMEM or NVM) has recently become commercially available under several configurations with different purposes and goals. Despite the attention to the topic, we are not aware of a comprehensive empirical analysis of existing relational database engines under different PMEM configurations. Such a study is important to understand the performance implications of the various hardware configurations and how different DB engines can benefit from them. To this end, we analyze three different engines (PostgreSQL, MySQL, and SQLServer) under common workloads (TPC-C and TPC-H) with all possible PMEM configurations supported by Intel's Optane NVM devices (PMEM as persistent memory in AppDirect mode and PMEM as volatile memory in Memory mode). Our results paint a complex picture and are not always intuitive due to the many factors involved. Based on our findings, we provide insights on how the different engines behave with PMEM and which configurations and queries perform best. Our results show that using PMEM as persistent storage usually speeds up query execution, but with some caveats as the I/O path is not fully optimized. Additionally, using PMEM in Memory mode does not offer any performance advantage despite the larger volatile memory capacity. Through the extensive coverage of engines and parameters, we provide an important starting point for exploiting PMEM in databases and tuning relational engines to take advantage of this new technology.
VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion

Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view information by global averaging, thus losing fine detail, or they can heuristically cluster views for local fusion, thus restricting their ability to consider all views jointly. Our key insight is that greater detail can be retained without restricting view diversity by learning a view-fusion function conditioned on camera pose and image content. We propose to learn this multi-view fusion using a transformer. To this end, we introduce VoRTX, 1 an end-to-end volumetric 3D reconstruction network using transformers for wide-baseline, multi-view feature fusion. Our model is occlusion-aware, leveraging the transformer architecture to predict an initial, projective scene geometry estimate. This estimate is used to avoid back-projecting image features through surfaces into occluded regions. We train our model on ScanNet and show that it produces better reconstructions than state-of-the-art methods. We also demonstrate generalization without any fine-tuning, outperforming the same state-of-the-art methods on two other datasets, TUM-RGBD and ICL-NUIM.
Triple-band high-temperature superconducting microstrip filter based on multimode split ring resonator

A compact triple-band high-temperature superconducting (HTS) YBa2Cu3Oy microstrip bandpass filter using multimode split ring resonator (SRR) is presented in this letter. Also, its properties and equivalent circuit models are investigated by even- and odd-mode analysis. Moreover, design method of the proposed triple-band HTS filter for the applications of global positioning system at 1.57 GHz, worldwide interoperability for microwave access at 3.5 GHz, and wireless local area networks at 5.8 GHz is discussed. The centre frequencies and the bandwidths of the three passbands can be allocated properly choosing the dimension parameters of the multimode SRR. In addition, four transmission zeros are produced to improve the selectivity of this filter.
Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices

WiFi fingerprinting-based indoor localization on smartphones is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. Unfortunately, the transmitted signal characteristics from independently maintained WiFi access points (APs) vary greatly over time. Moreover, some of the WiFi APs visible at the initial deployment phase may be replaced or removed over time. These factors are often ignored and cause gradual and catastrophic degradation of indoor localization accuracy post-deployment, over weeks and months. We propose a Siamese neural encoder-based framework that offers up to 40% reduction in degradation of localization accuracy over time compared to the state-of-the-art in the area, without requiring any re-training.
Building astroBERT, a language model for Astronomy & Astrophysics

The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e.g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search. For example, a query for"results from the Planck mission"should be able to distinguish between all the various meanings of Planck (person, mission, constant, institutions and more) without further clarification from the user. At ADS, we are applying modern machine learning and natural language processing techniques to our dataset of recent astronomy publications to train astroBERT, a deeply contextual language model based on research at Google. Using astroBERT, we aim to enrich the ADS dataset and improve its discoverability, and in particular we are developing our own named entity recognition tool. We present here our preliminary results and lessons learned.
Extending the GTAP Data Base and Model to Cover Domestic Support Issues using the EU as Example

The EU Single Farm Payment (SFP) is currently distributed in proportion to primary factor shares in version 8 of the GTAP database. In this paper, we investigate whether this way of modeling the EU SFP makes a difference in analyzing agricultural policy reforms. To do so, we create alternative versions of the GTAP database to compare the effects with the default setting in GTAP. Employing OECD data, along with the GTAP framework, we vary the assumptions about the allocation of the SFP. In the process, we demonstrate how to alter and update the GTAP database to implement domestic support of OECD PSE tables. We provide a detailed overview supplemented with assumptions of payment allocation, shock calculations and in particular, the Altertax procedure to update value flows and price equations extended in the GTAP model. Subsequently, we illustrate the impact of those assumptions by simulating a 100% removal of the SFP using the deviating versions of GTAP database. This sensitivity analysis reveals strong differences in results, but particularly in production responses of food and agricultural sectors that decrease with an increasing degree of decoupling. Furthermore, our analysis shows that the effect on welfare and the trade balance decreases with an increasing degree of decoupling. This experiment shows that the allocation of the SFP can have strong impacts on simulation results.
Impact Angle Control of Local Intense dB/dt Variations During Shock‐Induced Substorms

The impact of interplanetary shocks on the magnetosphere can trigger magnetic substorms that intensify auroral electrojet currents. These currents enhance ground magnetic field perturbations (dB/dt), which in turn generate geomagnetically induced currents (GICs) that can be detrimental to power transmission infrastructure. We perform a comparative study of dB/dt variations in response to two similarly strong shocks, but with one being nearly frontal and the other highly inclined. Multi‐instrument analyses by the Time History of Events and Macroscale Interactions during Substorms (THEMIS) and Los Alamos National Laboratory spacecraft show that nightside substorm‐time energetic particle injections are more intense and occur faster in the case of the nearly head‐on impact. The same trend is observed in dB/dt variations recorded by THEMIS ground magnetometers. THEMIS all‐sky imager data show a fast and clear poleward auroral expansion in the first case, which does not clearly occur in the second case. Strong field‐aligned currents computed with the spherical elementary current system (SECS) technique occur in both cases, but the current variations resulting from the inclined shock impact are weaker and slower compared to the nearly frontal case. SECS analyses also reveal that geographic areas with dB/dt surpassing the thresholds 1.5 and 5 nT/s, usually linked to high‐risk GICs, are larger and occur earlier due to the symmetric compression caused by the nearly head‐on impact. These results, with profound space weather implications, suggest that shock impact angles affect the geospace driving conditions and the location and intensity of the subsequent dB/dt variations during substorm activity.
Clustering mixtures with almost optimal separation in polynomial time

We consider the problem of clustering mixtures of mean-separated Gaussians in high dimensions. We are given samples from a mixture of k identity covariance Gaussians, so that the minimum pairwise distance between any two pairs of means is at least Δ, for some parameter Δ > 0, and the goal is to recover the ground truth clustering of these samples. It is folklore that separation Δ = Θ (√logk) is both necessary and sufficient to recover a good clustering (say with constant or 1/poly(k) error), at least information theoretically. However, the estimators which achieve this guarantee are inefficient. We give the first algorithm which runs in polynomial time, and which almost matches this guarantee. More precisely, we give an algorithm which takes polynomially many samples and time, and which can successfully recover a good clustering, so long as the separation is Δ = Ω (log1/2 + c k), for any c > 0. Previously, polynomial time algorithms were only known for this problem when the separation was polynomial in k, and all algorithms which could tolerate poly logk separation required quasipolynomial time. We also extend our result to mixtures of translations of a distribution which satisfies the Poincaré inequality, under additional mild assumptions. Our main technical tool, which we believe is of independent interest, is a novel way to implicitly represent and estimate high degree moments of a distribution, which allows us to extract important information about high-degree moments without ever writing down the full moment tensors explicitly.
Duals of Feynman Integrals, 2: Generalized Unitarity

The first paper of this series introduced objects (elements of twisted relative cohomology) that are Poincaré dual to Feynman integrals. We show how to use the pairing between these spaces – an algebraic invariant called the intersection number – to express a scattering amplitude over a minimal basis of integrals, bypassing the generation of integrationby-parts identities. The initial information is the integrand on cuts of various topologies, computable as products of on-shell trees, providing a systematic approach to generalized unitarity. We give two algorithms for computing the multi-variate intersection number. As a first example, we compute 4and 5-point gluon amplitudes in generic spacetime dimension. We also examine the 4-dimensional limit of our formalism and provide prescriptions for extracting rational terms. ar X iv :2 11 2. 00 05 5v 1 [ he pth ] 3 0 N ov 2 02 1
Analysis of the nonlinear dynamics of a chirping-frequency Alfv\'en mode in a Tokamak equilibrium

Chirping Alfv\'{e}n modes are considered as potentially harmful in burning Tokamak plasmas. In this paper, the nonlinear evolution of a single-toroidal-number chirping mode is analysed by numerical particle simulation. This analysis can be simplified if the different resonant phase-space structures can be investigated as isolated ones. This can be done adopting a coordinate system that includes two constants of motion. In our simulations, we adopt as constants of motion, the magnetic momentum and the initial particle coordinates. For each resonant structure, a density-flattening region is formed around the respective resonance radius, with radial width that increases as the mode amplitude grows. It is delimited by two large negative density gradients, drifting inward and outward. With constant mode frequency, this density flattening would be responsible for the exhausting of the drive when large negative density gradients leave the resonance region. The frequency chirping, however, causes the resonance radius and the resonance region to drift inward. This drift delays the moment in which the inner density gradient reaches the inner boundary of the resonance region. On the other side, the island reconstitutes around the new resonance radius; as a consequence, the large negative density gradient further moves inward. This process continues as long as it allows to keep the large gradient within the resonance region. When this is no longer possible, the resonant structure ceases to be effective in driving the mode. To further grow, the mode has to tap a different resonant structure, possibly making use of additional frequency variations.
Auto-Regressive Approximations to Non-stationary Time Series, with Inference and Applications

Understanding the time-varying structure of complex temporal systems is one of the main challenges of modern time series analysis. In this paper, we show that every uniformly-positive-definite-in-covariance and sufficiently short-range dependent non-stationary and nonlinear time series can be well approximated globally by a white-noise-driven auto-regressive (AR) process of slowly diverging order. To our best knowledge, it is the first time such a structural approximation result is established for general classes of non-stationary time series. A high dimensional $\mathcal{L}^2$ test and an associated multiplier bootstrap procedure are proposed for the inference of the AR approximation coefficients. In particular, an adaptive stability test is proposed to check whether the AR approximation coefficients are time-varying, a frequently-encountered question for practitioners and researchers of time series. As an application, globally optimal short-term forecasting theory and methodology for a wide class of locally stationary time series are established via the method of sieves.
MonoScene: Monocular 3D Semantic Scene Completion

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its semantics. Our framework relies on successive 2D and 3D UNets, bridged by a novel 2D-3D features projection inspired by optics, and introduces a 3D context relation prior to enforce spatio-semantic consistency. Along with architectural contributions, we introduce novel global scene and local frustums losses. Experiments show we outperform the literature on all metries and datasets while hallucinating plausible scenery even beyond the camera field of view. Our code and trained models are available at https://github.com/cv-rits/MonoScene.
Spurious Valleys, Spurious Minima and NP-hardness of Sparse Matrix Factorization With Fixed Support

The problem of approximating a dense matrix by a product of sparse factors is a fundamental problem for many signal processing and machine learning tasks. It can be decomposed into two subproblems: ﬁnding the position of the non-zero coeﬃcients in the sparse factors, and determining their values. While the ﬁrst step is usually seen as the most challenging one due to its combinatorial nature, this paper focuses on the second step, referred to as sparse matrix approximation with ﬁxed support. First, we show its NP-hardness, while also presenting a nontrivial family of supports making the problem practically tractable with a dedicated algorithm. Then, we investigate the landscape of its natural optimization formulation, proving the absence of spurious local valleys and spurious local minima, whose presence could prevent local optimization methods to achieve global optimality. The advantages of the proposed algorithm over state-of-the-art ﬁrst-order optimization methods are discussed.
Forgetting Leads to Chaos in Attractor Networks

Attractor networks are an influential theory for memory storage in brain systems [1–4]. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In this work, we study a sparsely connected attractor network where memories are learned according to a Hebbian synaptic plasticity rule. After recapitulating known results for the continuous, sparsely connected Hopfield model [5], we investigate a model in which new memories are learned continuously and old memories are forgotten, using an online synaptic plasticity rule. We show that for a forgetting time scale that optimizes storage capacity, the qualitative features of the network’s memory retrieval dynamics are age-dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations. Therefore, fixed-point and chaotic attractors co-exist in the network phase space. The network presents a continuum of statistically distinguishable memory states, where chaotic fluctuations appear abruptly above a critical age and then increase gradually until the memory disappears. We develop a dynamical mean field theory (DMFT) to analyze the age-dependent dynamics and compare the theory with simulations of large networks. We compute the optimal forgetting time-scale for which the number of stored memories is maximized. We found that the maximum age at which memories can be retrieved is given by an instability at which old memories destabilize and the network converges instead to a more recent one. Our numerical simulations show that a high-degree of sparsity is necessary for the DMFT to accurately predict the network capacity. Finally, our theory provides specific predictions for delay response tasks with aging memoranda. In particular, it predicts a higher degree of temporal fluctuations in retrieval states associated with older memories, and it also predicts fluctuations should be faster in older memories. Our theory of attractor networks that continuously learn new information at the price of forgetting old memories can account for the observed diversity of retrieval states in the cortex, and in particular the strong temporal fluctuations of cortical activity.
The definition of partnering as a Wittgenstein family‐resemblance concept

This article on partnering and family‐resemblance makes two contributions to the debate about the definition of partnering in construction. The first is a distinction between general prerequisites, components and goals when discussing the concept. In order to understand what is specific about partnering the focus should be on the components, which are identified through a literature review. The second contribution is to apply Ludwig Wittgenstein's idea of family‐resemblance to the partnering concept. His idea is that a complex concept can be understood as a network of overlapping similarities. From the literature review it is concluded that there are two necessary components in partnering – trust and mutual understanding – and that a number of different components can be added to form a specific variant of partnering. This provides a new method to define the vague and multifaceted concept of partnering in a flexible and structured way.
Easy Grasp: A novel hybrid-driven manual medical instrument for laparoscopic surgery

A novel compact manual instrument for laparoscopic surgery, Easy Grasp, is proposed and the prototype is developed. It is a hybridization of cable and link drives. The instrument conforms to the ergonomic engineering and it can realize roll, pitch, and yaw motions. Owing to the parallelograms and universal joints, the motions outside the abdomen can be mapped to the end-effector motions inside the abdomen exactly. The workspace of the wrist-like joint is a submanifold of special orthogonal group and is evaluated by the matrix exponential parameterization. The structure of the instrument is designed optimally on the basis of the kinematic analysis together with the global condition index. The instrument can not only satisfy the needs of basic tasks, but also be used to accomplish difficult tasks such as suturing and knot-tying. Experimental results show that the instrument works as well as commercial products. The ease of use will make the instrument effective in laparoscopic surgery. The structure of the instrument is a prototype for other manual instruments and provides a framework for instrument design.
Lightweight Distributed Gaussian Process Regression for Online Machine Learning

In this paper, we study the problem where a group of agents aim to collaboratively learn a common static latent function through streaming data. We propose a lightweight distributed Gaussian process regression (GPR) algorithm that is cognizant of agents' limited capabilities in communication, computation and memory. Each agent independently runs agent-based GPR using local streaming data to predict test points of interest; then the agents collaboratively execute distributed GPR to obtain global predictions over a common sparse set of test points; finally, each agent fuses results from distributed GPR with agent-based GPR to refine its predictions. By quantifying the transient and steady-state performances in predictive variance and error, we show that limited inter-agent communication improves learning performances in the sense of Pareto. Monte Carlo simulation is conducted to evaluate the developed algorithm.
Learning Aided Auctioning-Based Spectrum Access System in a Wireless Optical Network

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution/5G based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.
Realistic Job Previews for a Sample of Navy and Marine Corps Occupations: Development of Prototypes

Abstract : This research developed prototypic realistic job previews (RJPs) for a sample of entry-level Navy and Marine Corps occupations. The research reported here is expected to benefit the recruiting branches of the Navy and Marine Corps as well as the research community. In addition to their use in recruiting, RJPs such as those produced in this effort, have potential for a number of other uses (e.g., at schools, job fairs, armed forces exhibits, and in stand-alone systems at shopping malls, etc). (kr)