Miracle-dz/UniFAR-FTUs huggingface dataset:
On rich points and incidences with restricted sets of lines in 3-space

Let L be a set of n lines in R3 that is contained, when represented as points in the four-dimensional Plücker space of lines in R3, in an irreducible variety T of constant degree which is non-degenerate with respect to L (see below). We show: (1) If T is two-dimensional, the number of r-rich points (points incident to at least r lines of L) is O(n4/3+ε/r2), for r ⩾ 3 and for any ε > 0, and, if at most n1/3 lines of L lie on any common regulus, there are at most O(n4/3+ε) 2-rich points. For r larger than some sufficiently large constant, the number of r-rich points is also O(n/r). As an application, we deduce (with an ε-loss in the exponent) the bound obtained by Pach and de Zeeuw [16] on the number of distinct distances determined by n points on an irreducible algebraic curve of constant degree in the plane that is not a line nor a circle. (2) If T is two-dimensional, the number of incidences between L and a set of m points in R3 is O(m + n). (3) If T is three-dimensional and nonlinear, the number of incidences between L and a set of m points in R3 is O ( m3/5n3/5 + (m11/15n2/5 + m1/3n2/3)s1/3 + m + n ) , provided that no plane contains more than s of the points. When s = O(min{n3/5/m2/5, m1/2}), the bound becomes O(m3/5n3/5 + m + n). As an application, we prove that the number of incidences between m points and n lines in R4 contained in a quadratic hypersurface (which does not contain a hyperplane) is O(m3/5n3/5 +m+n). The proofs use, in addition to various tools from algebraic geometry, recent bounds on the number of incidences between points and algebraic curves in the plane. 2012 ACM Subject Classification Theory of computation → Computational geometry
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding

Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information. As such, every text understanding tool should be capable of recognizing acronyms in text (i.e., acronym identification) and also finding their correct meaning (i.e., acronym disambiguation). As most of the prior works on these tasks are restricted to the biomedical domain and use unsupervised methods or models trained on limited datasets, they fail to perform well for scientific document understanding. To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively. The two shared tasks have attracted 52 and 43 participants, respectively. While the submitted systems make substantial improvements compared to the existing baselines, there are still far from the human-level performance. This paper reviews the two shared tasks and the prominent participating systems for each of them.
Towards an Automatic System for Extracting Planar Orientations from Software Generated Point Clouds

In geology, a key activity is the characterisation of geological structures (surface formation topology and rock units) using Planar Orientation measurements such as Strike, Dip and Dip Direction. In general these measurements are collected manually using basic equipment; usually a compass/clinometer and a backboard, recorded on a map by hand. Various computing techniques and technologies, such as Lidar, have been utilised in order to automate this process and update the collection paradigm for these types of measurements. Techniques such as Structure from Motion (SfM) reconstruct of scenes and objects by generating a point cloud from input images, with detailed reconstruction possible on the decimetre scale. SfM-type techniques provide advantages in areas of cost and usability in more varied environmental conditions, while sacrificing the extreme levels of data fidelity. Here is presented a methodology of data acquisition and a Machine Learning-based software system: GeoStructure, developed to automate the measurement of orientation measurements. Rather than deriving measurements using a method applied to the input images, such as the Hough Transform, this method takes measurements directly from the reconstructed point cloud surfaces. Point cloud noise is mitigated using a Mahalanobis distance implementation. Significant structure is characterised using a k-nearest neighbour region growing algorithm, and final surface orientations are quantified using the plane, and normal direction cosines.
Intelligent Vector-based Customer Segmentation in the Banking Industry

Customer Segmentation is the process of dividing customers into groups based on common characteristics. An intelligent Customer Segmentation will not only enable an organization to effectively allocate marketing resources (e.g., Recommender Systems in the Banking sector) but also it will enable identifying the customer cohorts that are most likely to benefit from a specific policy (e.g., to discover diverse patient groups in the Health sector). While there has been a significant improvement in approaches to Customer Segmentation, the main challenge remains to be the understanding of the reasons behind the segmentation need. This task is challenging as it is subjective and depends on the goal of segmentation as well as the analyst's perspective. To address this challenge, in this paper, we present an intelligent vector-based customer segmentation approach. The proposed approach will leverage feature engineering to enable analysts to identify important features (from a pool of features such as demographics, geography, psychographics, behavioral, and more) and feed them into a neural embedding framework named Customer2Vec. The Customer2Vec combines the neural network classification and clustering methods as supervised and unsupervised learning techniques to embed the customer vector. We adopt a typical scenario in the Banking Sector to highlight how Customer2Vec significantly improves the quality of the segmentation and detecting customer similarities.
Design and Comparative analysis of a Two-Stage Ultra-Low-Power Subthreshold Operational Amplifier in 180nm, 90nm, and 45nm technology

In this paper, a two-stage ultra-low-power operational amplifier is designed, and a comparative analysis of the proposed subthreshold complementary amplifier is presented between 180nm, 90nm, and 45nm CMOS technology. The proposed operational amplifier is compared across several different parameters to determine the optimal design. It achieves a maximum gain of around 75 dB and a phase margin of 76°, dissipating just 140nW with a supply voltage of 0.5 V which is well suited for biomedical applications that require low power and high gain. The proposed operational amplifier has been designed using a SPICE-based circuit simulator.
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction

In recent years, manifold methods have moved into focus as tools for dimension reduction. Assuming that the high-dimensional data actually lie on or close to a low-dimensional nonlinear manifold, these methods have shown convincing results in several settings. This manifold assumption is often reasonable for functional data, i.e., data representing continuously observed functions, as well. However, the performance of manifold methods recently proposed for tabular or image data has not been systematically assessed in the case of functional data yet. Moreover, it is unclear how to evaluate the quality of learned embeddings that do not yield invertible mappings, since the reconstruction error cannot be used as a performance measure for such representations. In this work, we describe and investigate the specific challenges for nonlinear dimension reduction posed by the functional data setting. The contributions of the paper are three-fold: First of all, we define a theoretical framework which allows to systematically assess specific challenges that arise in the functional data context, transfer several nonlinear dimension reduction methods for tabular and image data to functional data, and show that manifold methods can be used successfully in this setting. Secondly, we subject performance assessment and tuning strategies to a thorough and systematic evaluation based on several different functional data settings and point out some previously undescribed weaknesses and pitfalls which can jeopardize reliable judgment of embedding quality. Thirdly, we propose a nuanced approach to make trustworthy decisions for or against competing nonconforming embeddings more objectively.
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines

We provide a robust defence to adversarial attacks on discriminative algorithms. Neural networks are naturally vulnerable to small, tailored perturbations in the input data that lead to wrong predictions. On the contrary, generative models attempt to learn the distribution underlying a dataset, making them inherently more robust to small perturbations. We use Boltzmann machines for discrimination purposes as attack-resistant classifiers, and compare them against standard state-of-the-art adversarial defences. We find improvements ranging from 5% to 72% against attacks with Boltzmann machines on the MNIST dataset. We furthermore complement the training with quantum-enhanced sampling from the D-Wave 2000Q annealer, finding results comparable with classical techniques and with marginal improvements in some cases. These results underline the relevance of probabilistic methods in constructing neural networks and demonstrate the power of quantum computers, even with limited hardware capabilities. This work is dedicated to the memory of Peter Wittek.
Scheduling Coflows With Dependency Graph

Applications in data-parallel computing typically consist of multiple stages. In each stage, a set of intermediate parallel data flows (<italic>Coflow</italic>) is produced and transferred between servers to enable starting of next stage. While there has been much research on scheduling isolated coflows, the dependency between coflows in multi-stage jobs has been largely ignored. In this paper, we consider scheduling coflows of multi-stage jobs represented by general <italic>DAG</italic>s (Directed Acyclic Graphs) in a shared data center network, so as to minimize the total weighted completion time of jobs. This problem is significantly more challenging than the traditional coflow scheduling, as scheduling even a single multi-stage job to minimize its completion time is shown to be NP-hard. In this paper, we propose a polynomial-time algorithm with approximation ratio of <inline-formula> <tex-math notation="LaTeX">$O(\mu \log (m)/\log (\log (m)))$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> is the maximum number of coflows in a job and <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is the number of servers. For the special case that the jobs’ underlying dependency graphs are <italic>rooted trees</italic>, we modify the algorithm and improve its approximation ratio. To verify the performance of our algorithms, we present simulation results using real traffic traces that show up to 53% improvement over the prior approach. We conclude the paper by providing a result concerning an optimality gap for scheduling coflows with general DAGs.
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the set-up as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines.
Learning to Play Imperfect-Information Games by Imitating an Oracle Planner

We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundreds of years of experience to produce competitive agents. Our approach is based on model-based planning. We tackle the problem of partial observability by first building an (oracle) planner that has access to the full state of the environment and then distilling the knowledge of the oracle to a (follower) agent which is trained to play the imperfect-information game by imitating the oracle’s choices. We experimentally show that planning with naive Monte Carlo tree search does not perform very well in large combinatorial action spaces. We, therefore, propose planning with a fixed-depth tree search and decoupled TS for action selection. We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman and the follower policy successfully learns to implement them by training on a few hundred battles.
SN 2014C: VLBI image shows a shell structure and decelerated expansion

We report on new Very Long Baseline Interferometry radio measurements of supernova 2014C in the spiral galaxy NGC 7331, made with the European VLBI Network ∼5 yr after the explosion, as well as on flux density measurements made with the Jansky Very Large Array (VLA). SN 2014C was an unusual supernova, initially of Type Ib, but over the course of ∼1 yr it developed strong Hα lines, implying the onset of strong interaction with some H-rich circumstellar medium (CSM). The expanding shock-front interacted with a dense shell of circumstellar material during the first year, but has now emerged from the dense shell and is expanding into the lower density CSM beyond. Our new VLBI observations show a relatively clear shell structure and continued expansion with some deceleration, with a suggestion that the deceleration is increasing at the latest times. Our multi-frequency VLA observations show a relatively flat powerlaw spectrum with Sν ∝ ν−0.56±0.03, and show no decline in the radio luminosity since t ∼ 1 yr.
SIMDRAM: a framework for bit-serial SIMD processing using DRAM

Processing-using-DRAM has been proposed for a limited set of basic operations (i.e., logic operations, addition). However, in order to enable full adoption of processing-using-DRAM, it is necessary to provide support for more complex operations. In this paper, we propose SIMDRAM, a flexible general-purpose processing-using-DRAM framework that (1) enables the efficient implementation of complex operations, and (2) provides a flexible mechanism tosupport the implementation of arbitrary user-defined operations. The SIMDRAM framework comprises three key steps. The first step builds an efficient MAJ/NOT representation of a given desired operation. The second step allocates DRAM rows that are reserved for computation to the operation’s input and output operands, and generates the required sequence of DRAM commands to perform the MAJ/NOT implementation of the desired operation in DRAM. The third step uses the SIMDRAM control unit located inside the memory controller to manage the computation of the operation from start to end, by executing the DRAM commands generated in the second step of the framework. We design the hardware and ISA support for SIMDRAM framework to (1) address key system integration challenges, and (2) allow programmers to employ new SIMDRAM operations without hardware changes. We evaluate SIMDRAM for reliability, area overhead, throughput, and energy efficiency using a wide range of operations and seven real-world applications to demonstrate SIMDRAM’s generality. Our evaluations using a single DRAM bank show that (1) over 16 operations, SIMDRAM provides 2.0X the throughput and 2.6X the energy efficiency of Ambit, a state-of-the-art processing-using-DRAM mechanism; (2) over seven real-world applications, SIMDRAM provides 2.5X the performance of Ambit. Using 16 DRAM banks, SIMDRAM provides (1) 88X and 5.8X the throughput, and 257X and 31X the energy efficiency, of a CPU and a high-end GPU, respectively, over 16 operations; (2) 21X and 2.1X the performance of the CPU and GPU, over seven real-world applications. SIMDRAM incurs an area overhead of only 0.2% in a high-end CPU.
Separation of phenol from model oils with quaternary ammonium salts via forming deep eutectic solvents

A variety of quaternary ammonium salts have been used to efficiently separate phenols from model oils, which avoids the use of mineral alkali and acids that produce phenol-containing waste water. It was found that some quaternary ammonium salts showed high phenol-removal efficiencies, which could reach as high as 99.9%. The separation mechanism was also discussed.
Evolving the narrative for protecting a rapidly changing ocean, post‐COVID‐19

Abstract The ocean is the linchpin supporting life on Earth, but it is in declining health due to an increasing footprint of human use and climate change. Despite notable successes in helping to protect the ocean, the scale of actions is simply not now meeting the overriding scale and nature of the ocean's problems that confront us. Moving into a post‐COVID‐19 world, new policy decisions will need to be made. Some, especially those developed prior to the pandemic, will require changes to their trajectories; others will emerge as a response to this global event. Reconnecting with nature, and specifically with the ocean, will take more than good intent and wishful thinking. Words, and how we express our connection to the ocean, clearly matter now more than ever before. The evolution of the ocean narrative, aimed at preserving and expanding options and opportunities for future generations and a healthier planet, is articulated around six themes: (1) all life is dependent on the ocean; (2) by harming the ocean, we harm ourselves; (3) by protecting the ocean, we protect ourselves; (4) humans, the ocean, biodiversity, and climate are inextricably linked; (5) ocean and climate action must be undertaken together; and (6) reversing ocean change needs action now. This narrative adopts a ‘One Health’ approach to protecting the ocean, addressing the whole Earth ocean system for better and more equitable social, cultural, economic, and environmental outcomes at its core. Speaking with one voice through a narrative that captures the latest science, concerns, and linkages to humanity is a precondition to action, by elevating humankind's understanding of our relationship with ‘planet Ocean’ and why it needs to become a central theme to everyone's lives. We have only one ocean, we must protect it, now. There is no ‘Ocean B’.
Effects of Metformin on the Gut Microbiota in Obesity and Type 2 Diabetes Mellitus

Abstract Metformin is a first-line treatment for type 2 diabetes mellitus (T2DM); however, its underlying mechanism is not fully understood. Gut microbiota affect the development and progression of T2DM. In recent years, an increasing number of studies has focused on the relationship between metformin and gut microbiota, suggesting that metformin might exert part of its hypoglycemic effect through these microbes. However, most of these results were not consistent due to the complex composition of the microbiota, the differences between species, the large variation between individuals, and the differences in experimental design, bringing great obstacle for our better understanding of the effects of metformin on the gut microbiota. Here, we reviewed the published papers concerning about the impacts of metformin on the gut microbiota of mice, rats, and humans with obesity or T2DM, and summarized the changes of gut microbiota composition caused by metformin and the possible underlying hypoglycemic mechanism which is related to gut microbiota. It was found that the proportions of some microbiota, such as phyla Bacteroidetes and Verrucomicrobia and genera Akkermansia, Bacteroides and Escherichia, were significantly affected by metformin in several studies. Metformin may exert part of hypoglycemic effects by altering the gut microbiota in ways that maintain the integrity of the intestinal barrier, promote the production of short-chain fatty acids (SCFAs), regulate bile acid metabolism, and improve glucose homeostasis.
Race and Mental Health Among Older Adults: Within- and Between-Group Comparisons

© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Special Issue: Race and Mental Health: Editorial
Contrast sensitivity in Idiopathic Intracranial Hypertension

Background: Idiopathic Intracranial Hypertension (IIH) is a disease of elevated intracranial pressure without any known cause. Visual dysfunction is the major morbidity of this disease but not much is known about the way the contrast sensitivity (CS) function is affected. Objective: This prospective, interventional study attempted to evaluate the change in central and peripheral contrast sensitivity, after treatment in patients diagnosed with IIH. Materials and methods: Twenty eyes of 10 IIH patients underwent an internet based, Spaeth Richman Contrast Sensitivity (SPARCS) test. Average and quadrant wise SPARCS scores were compared at presentation (treatment naïve), 1-month post treatment and 3 months post treatment. Results: The average SPARCS scores pre-treatment, 1-month post-treatment and at 3 months post treatment were 68.8 + 10.16, 74.45 + 11.17 and 75.7 + 10.81 respectively. At 3 months visit, the average SPARCS score was nearly comparable to the average score in normal Indian subjects, observed in a previous study of ours. Quadrant wise change in contrast sensitivity from first visit to third visit was significant in superonasal (p=0.003), inferonasal (p=0.029) and inferotemporal (p= 0.007) quadrants. Discussion: Effect of IIH on visual system is still a relatively unexplored area, especially in the Indians. Not many studies have concentrated on its impact on central as well as peripheral CS. Previous studies have hinted at a possible role of CS as a better indicator of visual dysfunction than other parameters. Conclusions: IIH affects both central and peripheral contrast sensitivity and therapy results in the improvement of contrast deficit. Poor contrast can possibly be explained by relatively more involvement of Magnocellular pathway over the Parvocellular pathway. Abbreviations: IIH = Idiopathic Intracranial Hypertension, CS = Contrast Sensitivity, SPARCS = Spaeth Richman Contrast Sensitivity Test, BMI = Body Mass Index, MC = Magnocellular pathway, PC = Parvocellular pathway