Miracle-dz/UniFAR-FTUs huggingface dataset:
Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation

The goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics that use a pretrained language model to estimate the information content shared between a document and its summary. These metrics are a modern take on the Shannon Game, a method for summary quality scoring proposed decades ago, where we replace human annotators with language models. We also view these metrics as an extension of BLANC, a recently proposed approach to summary quality measurement based on the performance of a language model with and without the help of a summary. Using transformer based language models, we empirically verify that our metrics achieve state-of-the-art correlation with human judgement of the summary quality dimensions of both coherence and relevance, as well as competitive correlation with human judgement of consistency and fluency.
Metagratings for Perfect Mode Conversion in Rectangular Waveguides: Theory and Experiment

We present a complete design scheme, from theoretical formulation to experimental validation, exploiting the versatility of metagratings (MGs) for designing a rectangular waveguide (RWG) TE 10 - TE 20 mode converter (MC). MG devices, formed by sparse periodically positioned polarizable particles (meta-atoms), were mostly used to date for beam manipulation applications. In this paper, we show that the appealing diﬀraction engineering features of the MGs in such typical free-space periodic scenarios can be utilized to eﬃciently mould ﬁelds inside waveguides (WGs). In particular, we derive an analytical model allowing harnessing of the MG concept for realization of perfect mode conversion in RWGs. Conveniently, the formalism considers a printed-circuit-board (PCB) MG terminating the RWG, operating as a reﬂect-mode MC. Following the typical MG synthesis approach, the model directly ties the meta-atom position and geometry with the modal reﬂection coeﬃcients, enabling resolution of the detailed fabrication-ready design by enforcement of the functionality constraints: elimination of the fundamental TE 10 reﬂection and power conservation (passive lossless MG). This reliable semianaltyical scheme, veriﬁed via full-wave simulations and laboratory measurements, establishes a simple and eﬃcient alternative to common RWG MCs, typically requiring challenging deformation of the WG designed through time-consuming full-wave optimization. In addition, it highlights the immense potential MGs encompass for a wide variety of applications beyond beam manipulation.
Extractive Summarization of Call Transcripts

Automatic text summarization is one of the most challenging and interesting problems in natural language processing (NLP). Text summarization is the process of extracting the most important information from the text and presenting it concisely in fewer sentences. Call transcript involves textual description of a phone conversation between a customer (caller) and agent(s) (customer representatives). Call transcripts pose unique challenges that are not adequately addressed by most open-source automatic text summarizers, which are developed to summarize continuous texts such as articles and stories. This paper presents an indigenously developed method that combines topic modeling and sentence selection with punctuation restoration in condensing ill-punctuated or un-punctuated call transcripts to produce more readable summaries. This unique combination is what distinguishes the proposed summarizer from other text summarizers. Extensive testing, evaluation and comparisons, with an open-source, state-of-the-art extractive summarizer using three different pre-trained language models, have demonstrated the efficacy of this summarizer for call transcript summarization. The summaries generated by the proposed summarizer are shown to be more compelling and useful based on multiple criteria.
There and Back Again: Self-supervised Multispectral Correspondence Estimation

Across a wide range of applications, from autonomous vehicles to medical imaging, multi-spectral images provide an opportunity to extract additional information not present in color images. One of the most important steps in making this information readily available is the accurate estimation of dense correspondences between different spectra.Due to the nature of cross-spectral images, most correspondence solving techniques for the visual domain are simply not applicable. Furthermore, most cross-spectral techniques utilize spectra-specific characteristics to perform the alignment. In this work, we aim to address the dense correspondence estimation problem in a way that generalizes to more than one spectrum. We do this by introducing a novel cycle-consistency metric that allows us to self-supervise. This, combined with our spectra-agnostic loss functions, allows us to train the same network across multiple spectra.We demonstrate our approach on the challenging task of dense RGB-FIR correspondence estimation. We also show the performance of our unmodified network on the cases of RGB-NIR and RGB-RGB, where we achieve higher accuracy than similar self-supervised approaches. Our work shows that cross-spectral correspondence estimation can be solved in a common framework that learns to generalize alignment across spectra.
The square lattice Ising model on the rectangle III: Hankel and Toeplitz determinants

Based on the results obtained in (Hucht 2017 J. Phys. A: Math. Theor. 50 065201), we show that the partition function of the anisotropic square lattice Ising model on the L × M rectangle, with open boundary conditions in both directions, is given by the determinant of an M/2 × M/2 Hankel matrix, that equivalently can be written as the Pfaffian of a skew-symmetric M × M Toeplitz matrix. The M − 1 independent matrix elements of both matrices are Fourier coefficients of a certain symbol function, which is given by the ratio of two characteristic polynomials. These polynomials are associated to the different directions of the system, encode the respective boundary conditions, and are directly related through the symmetry of the considered Ising model under exchange of the two directions. The results can be generalized to other boundary conditions and are well suited for the analysis of finite-size scaling functions in the critical scaling limit using Szegő’s theorem.
Spatial Response Identification Enables Robust Experimental Ultrasound Computed Tomography

Ultrasound computed tomography techniques have the potential to provide clinicians with 3-D, quantitative and high-resolution information of both soft and hard tissues such as the breast or the adult human brain. Their practical application requires accurate modeling of the acquisition setup: the spatial location, orientation, and impulse response (IR) of each ultrasound transducer. However, the existing calibration methods fail to accurately characterize these transducers unless their size can be considered negligible when compared with the dominant wavelength, which reduces signal-to-noise ratios below usable levels in the presence of high-contrast tissues such as the skull. In this article, we introduce a methodology that can simultaneously estimate the location, orientation, and IR of the ultrasound transducers in a single calibration. We do this by extending spatial response identification (SRI), an algorithm that we have recently proposed to estimate transducer IRs. Our proposed methodology replaces the transducers in the acquisition device with a surrogate model whose effective response matches the experimental data by fitting a numerical model of wave propagation. This results in a flexible and robust calibration procedure that can accurately predict the behavior of the ultrasound acquisition device without ever having to know where the real transducers are or their individual IR. Experimental results using a ring acquisition system show that SRI produces calibrations of significantly higher quality than standard methodologies across all transducers, both in transmission and in reception. Experimental full-waveform inversion (FWI) reconstructions of a tissue-mimicking phantom demonstrate that SRI generates more accurate reconstructions than those produced with standard calibration techniques.
Page Table Management for Heterogeneous Memory Systems

Modern enterprise servers are increasingly embracing tiered memory systems with a combination of low latency DRAMs and large capacity but high latency non-volatile main memories (NVMMs) such as Intel’s Optane DC PMM. Prior works have focused on efficient placement and migration of data on a tiered memory system, but have not studied the optimal placement of page tables. Explicit and efficient placement of page tables is crucial for large memory footprint applications with high TLB miss rates because they incur dramatically higher page walk latency when page table pages are placed in NVMM. We show that (i) page table pages can end up on NVMM even when enough DRAM memory is available and (ii) page table pages that spill over to NVMM due to DRAM memory pressure are not migrated back later when memory is available in DRAM. We study the performance impact of page table placement in a tiered memory system and propose an efficient and transparent page table management technique that (i) applies different placement policies for data and page table pages, (ii) introduces a differentiating policy for page table pages by placing a small but critical part of the page table in DRAM, and (iii) dynamically and judiciously manages the rest of the page table by transparently migrating the page table pages between DRAM and NVMM. Our implementation on a real system equipped with Intel’s Optane NVMM running Linux reduces the page table walk cycles by 12% and total cycles by 20% on an average. This improves the runtime by 20% on an average for a set of synthetic and real-world large memory footprint applications when compared with various default Linux kernel techniques.
Towards an Understanding of Why and How ICT Projects Are Initiated: Analysis via Repertory Grid

Contemporary business innovation relies increasingly on information and communications technology (ICT) solutions. As ICT initiatives are generally implemented via projects the management of ICT projects has come under increasing scrutiny. ICT projects continue to fail; as a result, while research in ICT project management has indeed increased, many challenges for research and practice remain. Many studies have addressed the execution and management of ICT projects and the many factors that might relate to project outcomes. Very few, however, have considered ICT project initiation and the crucial decisions made at that very early, pre-life cycle stage. The primary intent of this research is therefore to investigate ICT projects with a particular focus on their initiation. In doing so we wished to understand why ICT projects are started, and how they are moved from idea or proposal to supported reality. A combination of semi-structured interviews and the repertory grid data collection and analysis method was employed to investigate and validate the motivating factors that influence individual IT Managers’ project initiation decisions and the methods they use to transition from idea to enacted project. Eighteen participants representing six medium-sized organizations were interviewed. A total of forty-nine recent ICT projects were identified and considered by these eighteen managers. A rich data set was collected and in-depth analysis was conducted. The results showed that there are indeed multiple underlying reasons for the decisions made at this early stage and that there are some especially common decision drivers. Some were expected, in the sense that they mapped to recommended best practice. For instance, most projects are motivated by a desire to achieve efficiencies or cost savings, and their potential tends to be assessed using cost benefit analysis. Other results were more surprising – competitor pressure was not a common driver for ICT project initiation in our analysis. Unsurprisingly, formal evaluation methods are 1 Corresponding author more frequently used to assess project proposals when those projects are larger and higher profile.
Piecewise linear iterated function systems on the line of overlapping construction

In this paper we consider iterated function systems (IFS) on the real line consisting of continuous piecewise linear functions. We assume some bounds on the contraction ratios of the functions, but we do not assume any separation condition. Moreover, we do not require that the functions of the IFS are injective, but we assume that their derivatives are separated from zero. We prove that if we fix all the slopes but perturb all other parameters, then for all parameters outside of an exceptional set of less than full packing dimension, the Hausdorff dimension of the attractor is equal to the exponent which comes from the most natural system of covers of the attractor.
Neural Networks for Semantic Gaze Analysis in XR Settings

Virtual-reality (VR) and augmented-reality (AR) technology is increasingly combined with eye-tracking. This combination broadens both fields and opens up new areas of application, in which visual perception and related cognitive processes can be studied in interactive but still well controlled settings. However, performing a semantic gaze analysis of eye-tracking data from interactive three-dimensional scenes is a resource-intense task, which so far has been an obstacle to economic use. In this paper we present a novel approach which minimizes time and information necessary to annotate volumes of interest (VOIs) by using techniques from object recognition. To do so, we train convolutional neural networks (CNNs) on synthetic data sets derived from virtual models using image augmentation techniques. We evaluate our method in real and virtual environments, showing that the method can compete with state-of-the-art approaches, while not relying on additional markers or preexisting databases but instead offering cross-platform use.
Toward Compact Deep Neural Networks via Energy-Aware Pruning

Despite the remarkable performance, modern deep neural networks are inevitably accompanied by a significant amount of computational cost for learning and deployment, which may be incompatible with their usage on edge devices. Recent efforts to reduce these overheads involve pruning and decomposing the parameters of various layers without performance deterioration. Inspired by several decomposition studies, in this paper, we propose a novel energy-aware pruning method that quantifies the importance of each filter in the network using nuclear-norm (NN). Proposed energy-aware pruning leads to state-of-the-art performance for Top-1 accuracy, FLOPs, and parameter reduction across a wide range of scenarios with multiple network architectures on CIFAR-10 and ImageNet after fine-grained classification tasks. On toy experiment, without fine-tuning, we can visually observe that NN has a minute change in decision boundaries across classes and outperforms the previous popular criteria. We achieve competitive results with 40.4/49.8% of FLOPs and 45.9/52.9% of parameter reduction with 94.13/94.61% in the Top-1 accuracy with ResNet-56/110 on CIFAR-10, respectively. In addition, our observations are consistent for a variety of different pruning setting in terms of data size as well as data quality which can be emphasized in the stability of the acceleration and compression with negligible accuracy loss.
Dynamic Grasping with Reachability and Motion Awareness

Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning needs to be adaptive and in real time, the delay in computation makes prediction necessary. In this paper, we present a dynamic grasping framework that is reachability-aware and motion-aware. Specifically, we model the reachability space of the robot using a signed distance field which enables us to quickly screen unreachable grasps. Also, we train a neural network to predict the grasp quality conditioned on the current motion of the target. Using these as ranking functions, we quickly filter a large grasp database to a few grasps in real time. In addition, we present a seeding approach for arm motion generation that utilizes solution from previous time step. This quickly generates a new arm trajectory that is close to the previous plan and prevents fluctuation. We implement a recurrent neural network (RNN) for modelling and predicting the object motion. Our extensive experiments demonstrate the importance of each of these components and we validate our pipeline on a real robot.
Robust State Estimation and Integrity Monitoring within Multi-Sensor Navigation System

—In autonomous applications, global navigation satellite systems (GNSS) aided inertial navigation system (INS) utilizing an extended Kalman ﬁlter (EKF) is the most widely investigated solution for high-rate and high-accurate vehicle states estimation. However, such navigation system suffers from poor parameterization, environment disturbances, human error, or even software and hardware failures under worst-case scenarios. In this paper, a novel scheme of multi-sensor navigation system is proposed, contributing to following research questions: 1) How to provide a reliable state estimation under minor system aberrations, i.e. improve the robustness of navigation system against e.g. inappropriate parameterization or environment disturbances; 2) How to provide system integrity against worst-case scenarios, i.e. signiﬁcant system aberrations or even failures. The proposed scheme involves extended H ∞ ﬁlter (EHF) for robustness enhancement, zonotope for protection level (PL) generation of the navigation solution and vehicle dynamic model aided fault detection (FD) of the inertial sensor. The designed approach is validated using the recorded data from an experimental platform called ’IRT-Buggy’, which is an electrical land vehicle. The results show that the proposed scheme provides reliable integrity monitoring and accurate state estimation, under both real-world and artiﬁcial abnormalities and shows signiﬁcant advantages against conventional ’GNSS+INS+EKF’ approach.
KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle

Computational notebooks have become the tool of choice for many data scientists and practitioners for performing analyses and disseminating results. Despite their increasing popularity, the research community cannot yet count on a large, curated dataset of computational notebooks. In this paper, we fill this gap by introducing KGTorrent, a dataset of Python Jupyter notebooks with rich metadata retrieved from Kaggle, a platform hosting data science competitions for learners and practitioners with any levels of expertise. We describe how we built KGTorrent, and provide instructions on how to use it and refresh the collection to keep it up to date. Our vision is that the research community will use KGTorrent to study how data scientists, especially practitioners, use Jupyter Notebook in the wild and identify potential shortcomings to inform the design of its future extensions.
Probing Fluorine Interactions in a Polyhydroxylated Environment: Conservation of a C–F···H–C Recognition Motif in Presence of O–H···O Hydrogen Bonds

Three conformationally locked fluorinated polycyclitols have been specially crafted on a rigid trans-decalin backbone, employing a surprisingly facile pyridine-poly(hydrogen fluoride)-mediated stereospecific epoxide ring opening as the key reaction. Molecular design of the three fluorinated probes under study focused on providing an efficient platform for (a) evaluating the ability of covalently bonded fluorine, vis-a-vis the isosteric hydroxy group, to act as a H-bond acceptor and (b) examining the possibility for an organic fluorine moiety, placed suitably in a spatially invariant position, to engage an 1,3-diaxial OH functionality in a purported intramolecular O-H···F hydrogen bond. The present endeavour reveals that C(sp 3 )-F···H-C(sp 3 ) hydrogen bonds, though weak and lesser investigated, can indeed be observed and supramolecular recognition motifs, involving such interactions, can be conserved even in crystal structures laden with stronger O-H···O hydrogen bonds.
Why do Black Holes Trace Bulges (&Central Surface Densities), Instead of Galaxies as a Whole?

Previous studies of fueling black holes (BHs) in galactic nuclei have argued (on scales∼ 0.01−1000pc) accretion is dynamical with inflow rates Ṁ ∼ ηMgas/tdyn in terms of gas mass Mgas, dynamical time tdyn, and some η. But these models generally neglected expulsion of gas by stellar feedback, or considered extremely high densities where expulsion is inefficient. Studies of star formation, however, have shown on sub-kpc scales the expulsion efficiency fwind = Mejected/Mtotal scales with the gravitational acceleration as (1− fwind)/ fwind ∼ āgrav/〈ṗ/m∗〉 ∼ Σeff/Σcrit where āgrav ≡ GMtot(< r)/r2 and 〈 ṗ/m∗〉 is the momentum injection rate from young stars. Adopting this as the simplest correction for stellar feedback, η→ η (1− fwind), we show this provides a more accurate description of simulations with stellar feedback at low densities. This has immediate consequences, predicting the slope and normalization of the MBH−σ and MBH−Mbulge relation, LAGN−SFR relations, and explanations for outliers in compact Es. Most strikingly, because star formation simulations show expulsion is efficient ( fwind ∼ 1) below total-mass surface density Mtot/π r2 < Σcrit ∼ 3× 109 M kpc−2 (where Σcrit = 〈ṗ/m∗〉/(πG)), BH mass is predicted to specifically trace host galaxy properties above a critical surface brightness Σcrit (B-band μcrit B ∼ 19magarcsec−2). This naturally explains why BH masses preferentially reflect bulge properties or central surface-densities (e.g. Σ1 kpc), not ‘total’ galaxy properties.
Current and Future High Density FRAM Technology

Current high density FRAM devices have been fabricated by developing several novel integration technologies such as capacitor technology and process technology. The process technology minimizes the integration degradation using stable BC scheme, encapsulating barrier layer (EBL), and effective etching curing process. The capacitor technology generates robust ferroelectric capacitors to be immune to any integration damage by taking advantage of new ferroelectric films and CVD deposition technique. Future FRAM technology will be focused on etchless scheme, nano-scale ferroelectric films, and three-dimensional capacitor scheme using CVD deposition techniques for noble metal electrode with excellent step coverage, which will produces high density 256 Mb FRAM and beyond.
Bhattacharyya Parameter of Monomial Codes for the Binary Erasure Channel: From Pointwise to Average Reliability

Monomial codes were recently equipped with partial order relations, a fact that allowed researchers to discover structural properties and efficient algorithm for constructing polar codes. Here, we refine the existing order relations in the particular case of the binary erasure channel. The new order relation takes us closer to the ultimate order relation induced by the pointwise evaluation of the Bhattacharyya parameter of the synthetic channels, which is still a partial order relation. To overcome this issue, we appeal to a related technique from network theory. Reliability network theory was recently used in the context of polar coding and more generally in connection with decreasing monomial codes. In this article, we investigate how the concept of average reliability is applied for polar codes designed for the binary erasure channel. Instead of minimizing the error probability of the synthetic channels, for a particular value of the erasure parameter p, our codes minimize the average error probability of the synthetic channels. By means of basic network theory results, we determine a closed formula for the average reliability of a particular synthetic channel, that recently gain the attention of researchers.