OpenRaiser/Intern-Atlas huggingface dataset:
The Global Impact of AI-Artificial Intelligence: Recent Advances and Future Directions, A Review
Venue: arXiv cs.CR 2023
Year: 2023

Artificial intelligence (AI) is an emerging technology that has the potential to transform many aspects of society, including the economy, healthcare, and transportation. This article synthesizes recent research literature on the global impact of AI, exploring its potential benefits and risks. The article highlights the implications of AI, including its impact on economic, ethical, social, security & privacy, and job displacement aspects. It discusses the ethical concerns surrounding AI development, including issues of bias, security, and privacy violations. To ensure the responsible development and deployment of AI, collaboration between government, industry, and academia is essential. The article concludes by emphasizing the importance of public engagement and education to promote awareness and understanding of AI's impact on society at large.
Validating Properties of RIS Channel Models with Prototypical Measurements
Venue: arXiv cs.NI 2023
Year: 2023

The integration of Reconfigurable Intelligent Surfaces (RIS) holds substantial promise for revolutionizing 6G wireless networks, offering unprecedented capabilities for real-time control over communication environments. However, determining optimal RIS configurations remains a pivotal challenge, necessitating the development of accurate analytical models. While theoretically derived models provide valuable insights, their potentially idealistic assumptions do not always translate well to practical measurements. This becomes especially problematic in mobile environments, where signals arrive from various directions. This study deploys an RIS prototype on a turntable, capturing the RIS channels' dependency on the angle of incoming signals. The difference between theory and practice is bridged by refining a model with angle-dependent reflection coefficients. The improved model exhibits a significantly closer alignment with real-world measurements. Analysis of the reflect coefficients reveals that non-perpendicular receiver angles can induce an additional attenuation of up to -14.5dB. Additionally, we note significant phase shift deviations, varying for each reflect element.
What Large-Scale Publication and Citation Data Tell Us About International Research Collaboration in Europe: Changing National Patterns in Global Contexts
Venue: arXiv cs.DL 2023
Year: 2023

This study analyzes the unprecedented growth of international research collaboration (IRC) in Europe during the period 2009-2018 in terms of coauthorship and citation distribution of globally indexed publications. The results reveal the dynamics of this change, as growing IRC moves European systems away from institutional collaboration, with stable and strong national collaboration. Domestic output has remained flat. The growth in publications in major European systems is almost entirely attributable to internationally coauthored papers. A comparison of trends within the four complementary collaboration modes clearly reveals that the growth of European science is driven solely by internationally co-authored papers. With the emergence of global network science, which diminishes the role of national policies in IRC and foregrounds the role of scientists, the individual scientists willingness to collaborate internationally is central to advancing IRC in Europe. Scientists collaborate internationally when it enhances their academic prestige, scientific recognition, and access to research funding, as indicated by the credibility cycle, prestige maximization, and global science models. The study encompassed 5.5 million Scopus-indexed articles, including 2.2 million involving international collaboration.
Generating Future Observations to Estimate Grasp Success in Cluttered Environments
Venue: arXiv cs.RO 2023
Year: 2023

End-to-end self-supervised models have been proposed for estimating the success of future candidate grasps and video predictive models for generating future observations. However, none have yet studied these two strategies side-by-side for addressing the aforementioned grasping problem. We investigate and compare a model-free approach, to estimate the success of a candidate grasp, against a model-based alternative that exploits a self-supervised learnt predictive model that generates a future observation of the gripper about to grasp an object. Our experiments demonstrate that despite the end-to-end model-free model obtaining a best accuracy of 72%, the proposed model-based pipeline yields a significantly higher accuracy of 82%.
Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles
Venue: arXiv cs.RO 2023
Year: 2023

LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.
Wheel Odometry-Based Localization for Autonomous Wheelchair
Venue: arXiv cs.RO 2023
Year: 2023

Localization is a fundamental requirement for an autonomous vehicle system. One of the most often used systems for autonomous vehicle localization is the global positioning system (GPS). Nevertheless, the functionality of GPS is strongly dependent on the availability of satellites, making it unreliable in some situations. As a result, autonomous vehicles must possess autonomous self-localization capabilities to ensure their independent operation. Odometry techniques are employed to achieve vehicle localization by predicting the vehicle position and orientation based on sensor measurements of the vehicle motion. One of the approaches employed in odometry is known as wheel odometry. Wheel odometry has a lower degree of reliance on the surrounding environment than visual odometry and laser odometry. This study aims to evaluate the performance of wheel odometry implementation for an autonomous wheelchair in the context of the localization process. The differential drive kinematic model is employed to determine the predicted pose of a wheelchair. This prediction is derived from the measurement of the linear and angular velocity of the wheelchair. Several experiments have been conducted to evaluate the performance of wheel odometry-based localization. Prior to experimenting, calibration procedures have also been performed to ensure accurate measurements of the sensor.
A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in a Quadratic Number of Moves
Venue: arXiv cs.CG 2024
Year: 2024

In the modular robot reconfiguration problem, we are given $n$ cube-shaped modules (or robots) as well as two configurations, i.e., placements of the $n$ modules so that their union is face-connected. The goal is to find a sequence of moves that reconfigures the modules from one configuration to the other using "sliding moves," in which a module slides over the face or edge of a neighboring module, maintaining connectivity of the configuration at all times. For many years it has been known that certain module configurations in this model require at least $Ω(n^2)$ moves to reconfigure between them. In this paper, we introduce the first universal reconfiguration algorithm -- i.e., we show that any $n$-module configuration can reconfigure itself into any specified $n$-module configuration using just sliding moves. Our algorithm achieves reconfiguration in $O(n^2)$ moves, making it asymptotically tight. We also present a variation that reconfigures in-place, it ensures that throughout the reconfiguration process, all modules, except for one, will be contained in the union of the bounding boxes of the start and end configuration.
Many-Help-One Problem for Gaussian Sources with a Tree Structure on their Correlation
Venue: arXiv cs.IT 2024
Year: 2024

In this paper we consider the separate coding problem for $L+1$ correlated Gaussian memoryless sources. We deal with the case where $L$ separately encoded data of sources work as side information at the decoder for the reconstruction of the remaining source. The determination problem of the rate distortion region for this system is the so called many-help-one problem and has been known as a highly challenging problem. The author determined the rate distortion region in the case where the $L$ sources working as partial side information are conditionally independent if the remaining source we wish to reconstruct is given. This condition on the correlation is called the CI condition. In this paper we extend the author's previous result to the case where $L+1$ sources satisfy a kind of tree structure on their correlation. We call this tree structure of information sources the TS condition, which contains the CI condition as a special case. In this paper we derive an explicit outer bound of the rate distortion region when information sources satisfy the TS condition. We further derive an explicit sufficient condtion for this outer bound to be tight. In particular, we determine the sum rate part of the rate distortion region for the case where information sources satisfy the TS condition. For some class of Gaussian sources with the TS condition we derive an explicit recursive formula of this sum rate part.
Reduction of the graph isomorphism problem to equality checking of $n$-variables polynomials and the algorithms that use the reduction
Venue: arXiv cs.DM 2024
Year: 2024

The graph isomorphism problem is considered. We assign modified $n$-variable characteristic polynomials for graphs and reduce the graph isomorphism problem to the problem of the polynomials isomorphism. It is required to find out, is there such a numbering of the second graph's vertices that the polynomials of the graphs are equal. We present algorithms for the graph isomorphism problem that use the reduction. We prove the propositions that justify the possibility of numerical realization of the algorithms for the general case of the graph isomorphism problem. The algorithms perform equality checking of graphs polynomials. We show that probability of obtaining a wrong solution of the graph isomorphism problem by comparing values of graph polynomials is negligible if the mantissa length is sufficiently large. Since, for a graph on $n$ vertices, the graph polynomial has $2^n$ coefficients, its value at some point cannot be evaluated directly for large enough $n$. We show that we can check the equality of the polynomials at some points without direct evaluation of the polynomials values at these points. We prove that it is required $O(n^4)$ elementary machine operations and machine numbers with mantissas length $O(n^2)$ to check equality of the values for the graphs on $n$ vertices. For the worst, it needs an exponential from $n$ time to solve the graph isomorphism problem instance using the presented approach, but in practice, it is efficient even for well known computationally hard instances of the graph isomorphism problem.
Memory Based Collaborative Filtering with Lucene
Venue: arXiv cs.IR 2024
Year: 2024

Memory Based Collaborative Filtering is a widely used approach to provide recommendations. It exploits similarities between ratings across a population of users by forming a weighted vote to predict unobserved ratings. Bespoke solutions are frequently adopted to deal with the problem of high quality recommendations on large data sets. A disadvantage of this approach, however, is the loss of generality and flexibility of the general collaborative filtering systems. In this paper, we have developed a methodology that allows one to build a scalable and effective collaborative filtering system on top of a conventional full-text search engine such as Apache Lucene.
Deep Feature Consistent Variational Autoencoder
Venue: arXiv cs.CV 2024
Year: 2024

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.
A Structured Approach to the development of Solutions in Excel
Venue: arXiv cs.SE 2024
Year: 2024

Spreadsheets offer a supremely successful democratisation platform, placing the manipulation and presentation of numbers within the grasp of users that have little or no mathematical expertise or IT experience. What appears to be almost completely lacking within a "normal" solution built using Excel default settings is the deployment of any structure that extends beyond a single-cell formula. The structural elements that allow conventional code to scale without escalating errors appear to be absent. This paper considers the use of controversial or lesser-used techniques to create a coherent solution strategy in which the problem is solved by a sequence of formulas resembling the steps of a programmed language.
Beyond Outerplanarity
Venue: arXiv cs.DM 2024
Year: 2024

We study straight-line drawings of graphs where the vertices are placed in convex position in the plane, i.e., \emph{convex drawings}. We consider two families of graph classes with convex drawings: \emph{outer $k$-planar} graphs, where each edge is crossed by at most $k$ other edges; and, \emph{outer $k$-quasi-planar} graphs where no $k$ edges can mutually cross. We show that the outer $k$-planar graphs are $\lfloor3.5\sqrt{k}\rfloor$-degenerate, and consequently that every outer $k$-planar graph can be colored with $\lfloor3.5\sqrt{k}\rfloor + 1$ colors. We further show that every outer $k$-planar graph has a balanced vertex separator of size at most $2k+3$. For each fixed $k$, these small balanced separators allow us to test outer $k$-planarity in quasi-polynomial time, e.g., this implies that none of these recognition problems is NP-hard unless the Exponential Time Hypothesis fails. We also show that the class of outer $k$-quasi-planar graphs and the class of planar graphs are incomparable. Finally, we restrict outer $k$-planar and outer $k$-quasi-planar drawings to \emph{full} drawings (where no crossing appears on the boundary of the outer face) and to \emph{closed} drawings (where the vertex sequence on the boundary of the outer face is a Hamiltonian cycle in the graph). For each $k$, we express \emph{closed outer $k$-planarity} and \emph{closed outer $k$-quasi-planarity} in \emph{extended monadic second-order logic}. Due to a result of Wood and Telle (New York J. Math., 2007) every outer $k$-planar graph has treewidth at most $3k+11$. Thus, Courcelle's theorem implies that closed outer $k$-planarity is linear time testable. We leverage this result to further show that full outer $k$-planarity can also be tested in linear time.
On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners
Venue: arXiv cs.SI 2024
Year: 2024

Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.
Multiple Models for Recommending Temporal Aspects of Entities
Venue: arXiv cs.IR 2024
Year: 2024

Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.
Nonexistence of generalized bent functions and the quadratic norm form equations
Venue: arXiv cs.IT 2024
Year: 2024

We present a new result on the nonexistence of generalized bent functions (GBFs)from (Z/tZ)^n to Z/tZ (called type [n, t]) for a large class. Assume p is an odd prime number. By showing certain quadratic norm form equations having no integral points, we obtain a universalresult on the nonexistence of GBFs with type [n,2p^e] when p and n satisfy a certain inequality, and by computational methods with a widely accepted hypothesis, Generalized Riemann Hypothesis, we also achieve some results on the nonexistence of GBFs for relatively small p.
Polynomial Root-Finding and Algebraic Eigenvalue Problem
Venue: arXiv cs.NA 2024
Year: 2024

Univariate polynomial root-finding has been studied for four millennia and very intensively in the last decades. Our new near-optimal root-finders approximate all zeros of a polynomial p almost as fast as one accesses its coefficients with the precision required for the solution within a prescribed error bound. Furthermore, our root-finders can be applied to a black box polynomial, defined by an oracle (black box subroutine) for its evaluation rather than by its coefficients. Due to this feature our root-finders support approximation of the eigenvalues of a matrix in a record Las Vegas expected bit operation time and are particularly fast for a polynomial that can be evaluated fast such as the sum of a few shifted monomials or a Mandelbrot-like polynomial defined by a recurrence. Our divide and conquer algorithm of ACM STOC 1995 is the only other known near-optimal polynomial root-finder, but it extensively uses the coefficients, is quite involved, and has never been implemented, while according to extensive numerical experiments with standard test polynomials, already a slower initial implementation of our new root-finders competes with user's choice package of root-finding subroutines MPSolve and supersedes it more and more significantly as the degree of a polynomial grows large. We elaborate upon the design and analysis of our algorithms, comment on their potential heuristic acceleration, and briefly cover polynomial root-finding by means of functional iterations. Our techniques can be of independent interest.
On the Predictability of non-CGM Diabetes Data for Personalized Recommendation
Venue: arXiv cs.CY 2024
Year: 2024

With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven (aka. machine learning) based models on patient-level blood glucose prediction; with measurements are taken only periodically (i.e., after several hours). To this end, we propose several post-prediction methods to account for the noise nature of these data, that marginally improves the performance of the end system.
Hard Non-Monotonic Attention for Character-Level Transduction
Venue: arXiv cs.CL 2024
Year: 2024

Character-level string-to-string transduction is an important component of various NLP tasks. The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different alphabets. Recent approaches have used sequence-to-sequence models with an attention mechanism to learn which parts of the input string the model should focus on during the generation of the output string. Both soft attention and hard monotonic attention have been used, but hard non-monotonic attention has only been used in other sequence modeling tasks such as image captioning (Xu et al., 2015), and has required a stochastic approximation to compute the gradient. In this work, we introduce an exact, polynomial-time algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings, showing that hard attention models can be viewed as neural reparameterizations of the classical IBM Model 1. We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention. Code is available at https://github. com/shijie-wu/neural-transducer.