bluuebunny/crossref_metadata_2025 huggingface dataset:
Buckling of Thin-Walled Cylindrical Shell Structures with Axially Variable Elastic Modulus Under Axial Compression
Authors: Yang Licai , Qiu Tian , Zhang Shanglin
Published: June 2023

This paper conducts the analytical investigation on the buckling of cylindrical shells with axially variable elastic modulus subjected to axial compressive load for the first time. First, it proves that the axially distributed elastic modulus can be expressed as the combination of constant and variable component. Then, governing differential equations for buckling analysis are derived and exactly solved by the combined perturbation method and Fourier analysis. Accordingly, the closed analytical solutions for the cylinder with arbitrarily variable elastic modulus are obtained, which reveal the explicit relations among buckling load, shell sizes and elastic modulus functions. Based on the presented analytical formulas, four types of elastic modulus variations for shell material which are uniform, periodic, linear and combined are studied in detail, and the results are also well verified. The derived analytical solutions in this paper can serve as benchmarks for buckling analyses of thin-walled cylinders with elastic modulus variations resulted from design, material manufacturing process, material imperfections and so on.
Longitudinal Seismic Response of a Pipe Pile Embedded in Saturated Thermoelastic Ground Subjected to Rayleigh Waves
Authors: Liu Hongbo , Dai Guoliang , Zhou Fengxi , Ouyang Haoran , Li Zhiyue , Zhu Wenbo
Published: June 2023

This work investigates the longitudinal vibration characteristics of the pipe pile in the liquid-saturated ground due to Rayleigh waves propagation with considering the influence of the nonisothermal condition on the wave motion. Firstly, the Rayleigh wavefield in the liquid-saturated thermoelastic ground is deduced theoretically with taking advantage of the Biot theory and the non-Fourier law of heat conduction. Secondly, the longitudinal frequency domain solution of the pipe pile under Rayleigh waves is derived analytically. Finally, the dependence of the pipe pile response on the physical parameter is illustrated by utilizing several numerical examples. The result reveals that increase in the thermal expansion coefficient of saturated soil significantly augments the longitudinal displacement and side friction of pile body. Increase in the permeability of saturated soil will reduce the pile head displacement. When the pile length is less than its effective length, the rise of the pile length will reduce the pile head displacement. Augment of both the outer radius and wall thickness of the pipe pile will help to reduce the pile head displacement. However, the lateral inertia effect of the pipe pile has slight influence on its longitudinal behavior under Rayleigh waves.
Dynamic Amplification Factors of an Arch Bridge Under Random Traffic Flows
Authors: Wang Bin , Xia Cuipeng , Min Quan , Zhu Shouyuan , Li Yongle
Published: June 2023

Among large-span bridges, arch bridges have relatively high stiffness, which may lead to large dynamic amplification factors (DAFs). The DAFs suggested by current codes mostly originate from common simply supported beam bridges. Previous DAF studies on dynamic vehicle–bridge solutions for arch bridges have mainly focused on one or two side-by-side vehicles. However, complex traffic flows with randomness rather than one or two vehicles act on bridges. DAFs that consider the effects of random traffic flows have not previously been reported. In this study, a random traffic–bridge vibration solution was established to explore the DAFs of arch bridge components. The randomness of the traffic parameters and road roughness was explored. The randomness of the external excitation, including traffic flow and road roughness, resulted in random dynamic responses and DAFs of the arch bridge components. Normal distributions could be used to fit the DAF distributions of each arch bridge component under random vehicle spacing, weight, speed, and road roughness. Under random traffic parameters, the coefficients of variation of the DAFs exceeded the 5% accepted level. The shortest suspenders were more sensitive to the randomness of the traffic flow parameters. Both the mean and coefficient of variation of the DAFs increased with worsening of the road conditions. The influence of the randomness of the road roughness on the DAFs of arch bridges must be considered, particularly for the shortest suspender. The 95% upper confidence limits of the DAFs for all components may be greater than the suggested values in the code.
EFFORT EVALUATION OF CHILDREN WITH CEREBRAL PALSY IN ROBOTIC TREADMILL THERAPY WITH AND WITHOUT MOTIVATION
Authors: MEDINA-SÁNCHEZ VICTOR , ARCE-GUEVARA VALDEMAR E. , BOCARD-MERAZ MÓNICA L. , VIGUERAS-GÓMEZ FLAVIO J. , CORRALES-SUÁSTEGUI PATRICIA , ARCE-SANTANA EDGAR R. , MÉNDEZ MARTIN O.
Published: June 2023

Although the task-specific robot treadmill therapies can improve gait performance of children with cerebral palsy, not all the institutions have the possibility to access to the sensor robot information to obtain quantitative measures related actual progress of the children. The aim of this study is to propose a computer vision system to investigate the effort of children with cerebral palsy during robot treadmill therapy. Ten participants diagnosed with cerebral palsy underwent single robot therapy sessions consisting of a motivated period (active: kicking a ball), followed by an unmotivated period (passive: simple walking). The outcome measure was the ankle trajectory recorded by a video camera. Indexes based on the ankle trajectory between active and passive therapy were computed: (a) the gait analysis based on the difference on a sample-by-sample manner, and b) the step cycles with dynamic time warping (DTW). Inference statistical analysis shows that the robot trajectories for passive and active therapies with the gait analysis presents statistical differences (p-[Formula: see text]. Likewise, DTW showed significant differences in robot trajectories related to passive and active therapy (p-[Formula: see text]. We found that the statistical differences are independent of the severity of central gait impairment. This suggests that information about patient involvement could be given to the therapist and could be used to assess patient participation. The method showed the potential to be used as a tool for the assessment the improvement progress.
A DEEP-LEARNING APPROACH FOR LOCATING THE INTRAMEDULLARY NAIL’S HOLES BASED ON 2D CALIBRATED FLUOROSCOPIC IMAGES
Authors: WANG FEI , JIANG JUNFENG , DENG ZIYUE , CHEN LIANG , HUANG RUI , CHEN ZHENGMING , HE KUNJIN , ZHAO XIAOMENG , LIU PENGFEI
Published: June 2023

Background: Minimally invasive surgery is widely used for managing fractures. When using the intramedullary nailing for bone fixation, surgeons must insert interlocking screws to prevent rotation of the bone fragment; however, it is difficult to determine the exact screwing position of intramedullary nails inserted into the bone. Conventionally, the distal interlocking nail surgery is performed under intermittent X-ray shooting. Nevertheless, this freehand fluoroscopic technique is technically demanding and time-consuming. Currently, the failure rate of this surgery is more than [Formula: see text], and the location error requires to be controlled within 2[Formula: see text]mm. Purpose: To develop a deep-learning approach for locating the intramedullary nail’s holes based on 2D calibrated fluoroscopic images. Methods: The projection of the hole’s axis is deeply regressed in the first step. Then, the hole’s 3D axis is derived by computing the intersection line of two planes determined by the projection of the axis and the X-ray source, respectively. The benefit of the data-driven manner is that our method can be applied to the arbitrary shape of the hole’s contour. Besides, we extract hole’s contour as the distinctive feature, so as to reduce the space of the training data in a large scale. Results: Our approach is proved to be efficient and easy to be implemented, and it has been compared with traditional location method in phantom experiments. The location accuracy error of the traditional method is [Formula: see text][Formula: see text]mm, [Formula: see text], and the location error of this method is [Formula: see text][Formula: see text]mm, [Formula: see text]. Furthermore, the traditional method takes an average of 10[Formula: see text]min to complete the location, while our method takes only 4[Formula: see text]min. In addition, to further verify the robustness of our method, we carried out a preclinical study involving different neural networks for locating the hole’s axis. Conclusion: Whether in terms of time consumption or accuracy error, our method is significantly better than traditional method, and the efficiency has been significantly improved. Therefore, our method has great clinical value. In addition, our approach has potential advantages over the X-ray guided freehand solution in terms of radiation exposure, and it has tremendous application prospects.
MULTIFRACTAL ANALYSIS OF EMG FOR CLASSIFICATION AND PROGRESSIVE ASSESSMENT OF BICEPS BRACHII MUSCLE STRENGTH
Authors: SUBHASH K. M. , JOSEPH K. PAUL
Published: June 2023

The proposed research demonstrates an attempt to introduce a systematic procedure for studying the multifractal dynamics of biceps brachii muscle actions during light exercises. The intrinsic patterns in the Surface Electromyogram (sEMG) signals were extracted by fruitfully exploiting the Multifractal features of the signal. The Multifractal features are derived from the multifractal singularity spectrum of the EMG signals. This multifractal feature vector could be utilized for signal characterization, which was successfully extended for the classification of EMG signals. Experimental verification has been done to validate the feature extraction and classification algorithm proposed in this article. A pilot study was conducted on signals from the Physionet database, which was then extended to a medium database developed with biceps brachii EMG signals of 32 healthy male subjects. From this study, we could validate that the Multifractal features fit as a differentiating multi-feature set and also for the progressive assessment of EMG signals of different classes. The observations of the proposed method revealed that the strength of multifractality and area under the spectrum increase as a result of fast movements of the forearm or increases in muscle fatigue. The classification is performed using well-recognized supervised classification algorithms such as k-Nearest Neighbor and Support Vector Machine (SVM) Classifiers. The performance analysis of the classifiers are studied on various measures such as Accuracy, Precision, Recall, F1 score. The statistical significance analysis of the feature vector was carried out by one-way ANOVA test.
Reproducing kernels of Sobolev–Slobodeckij˘ spaces via Green’s kernel approach: Theory and applications
Authors: Mohebalizadeh Hamed , Fasshauer Gregory E. , Adibi Hojatollah
Published: June 2023

This paper extends the work of Fasshauer and Ye [Reproducing kernels of Sobolev spaces via a Green kernel approach with differential operators and boundary operators, Adv. Comput. Math. 38(4) (2011) 891921] in two different ways, namely, new kernels and associated native spaces are identified as crucial Hilbert spaces in applied mathematics. These spaces include the following spaces defined in bounded domains [Formula: see text] with smooth boundary: homogeneous Sobolev–Slobodeckij̆ spaces, denoted by [Formula: see text], and Sobolev–Slobodeckij̆ spaces, denoted by [Formula: see text], where [Formula: see text]. Our goal is accomplished by obtaining the Green’s solutions of equations involving the fractional Laplacian and fractional differential operators defined through interpolation theory. We provide a proof that the Green’s kernels satisfying these problems are symmetric and positive definite reproducing kernels of [Formula: see text] and [Formula: see text], respectively. Constructing kernels in these two ways enables the characterization of functions in native spaces based on their regularity. The Galerkin/collocation method, based on these kernels, is employed to solve various fractional problems, offering explicit or simplified calculations and efficient solutions. This method yields improved results with reduced computational costs, making it suitable for complex domains.
How Can Business Schools Create and Appropriate Value in University-Based Technological Innovation?
Authors: Smirnov Dmitry S. , Willoughby Kelvin W.
Published: June 2023

Discussion in the scholarly literature about partnerships between entrepreneurs and universities for the creation of technological spinouts, and for helping universities to extract more value from their technology-related intellectual property (IP), is lively. However, the literature exhibits a gap in understanding how business schools may participate in the process of technology commercialization by facilitating the creation of intellectual property rights. In this conceptual paper, we seek to fill this gap in three ways. First, we offer some novel conceptual insights by studying the partnership between technical universities and entrepreneurs using a multi-level approach, incorporating a phenomenological research method, through the lenses of several established theoretical perspectives from the domains of economics, social science, and management: the division of labor, motivation, the nature of the firm, organization, and IP. Second, we develop a working hypothesis focused on learning reinforcement through multiple organizational levels that predicts how business schools may play a prominent role in technology commercialization, together with the theoretical conditions under which they may do so. Third, we offer an IP management model under which business schools, as such, may create and appropriate financial value by generating innovation-related IP that may be transferred to enterprises. Our research reveals a misalignment between promising approaches to university-based technological innovation suggested by normative theory and typical approaches associated with extant practice; and it also highlights a strategic issue, which is that the performance of most universities in the domain of technology transfer is disappointing. We suggest a way to address this misalignment, and this strategic issue, which is through the establishment of what we label as “Technology Innovation Laboratories” in business schools—analogous to technical laboratories usually associated with technical universities—that could generate various types of product- or service-related IP. This type of intellectual property—typically different from invention IP, and which we label here as “business IP”—could be exchanged for equity in spinouts or royalties from licensing, similar to the manner in which the invention IP of technical universities is usually commercialized.
Estimating the Licensing Probabilities in the Academic Context: An Empirical Analysis
Authors: Leite Rafael Ângelo Santos , Reis Igor Bezerra , Walter Cicero Eduardo , de Aragão Iracema Machado , Au-Yong-Oliveira Manuel , Fortes Paulo Jordão
Published: June 2023

Licensing technologies are one of the main ways to produce and bring academic research to society. Despite previous studies’ dedicated efforts to identify licensing probabilities, the question of how the expertise and prestige that a university has in a given technological field influences the licensing probabilities is still little addressed. This article aims to identify information in patent documents to estimate the probabilities of licensing technologies produced at the university. For that, we performed a data mining of licensed and unlicensed patents from an important Brazilian University (n = 1,578). We estimated the licensing probabilities using the Logistic Regression technique, based on the Maximum Likelihood Estimation. The results suggest that the variables of know-how in the main field and Technological strength in the main field are the most important/influential variables in estimating the probabilities of licensing a given patent. The main conclusion obtained from the results is that: universities, to obtain more licenses, must increase their know-how (expertise) in some technological fields, maintaining a reasonable level between specialization and diversification. Additionally, the higher the citations received (prestige/recognition) by a university in a given technological field, the greater the probability of patent licensing in that technological field. In terms of practical contributions, this study suggests that: investments in specific technological fields generate more competitive advantages for the university and, thus, more technological successes.
Merging Acoustic Black Holes and Local Resonators to Enhance Vibration Attenuation in Periodic Metamaterial Beams
Authors: Zhu Xiaosong , Yu Xiang , Mi Yongzhen , Zheng Hui
Published: June 2023

This paper studies the bandgap properties and wave attenuation mechanisms of periodic beams embedded with a combination of acoustic black holes (ABHs) and local resonators (LRs). ABH refers to a retarding structure with a decreasing, power-lawed thickness profile, which gradually reduces the local phase velocity of incoming bending waves and thus traps the structural vibration energy within a confined area. Combining LR with ABH provides a practical approach to enhance structure vibration attenuation. To characterize the combined effects of ABH and LR, an energy-based formulation that uses B-splines as admissible functions is proposed. The B-spline basis functions can be allocated in a unique way such that the power-lawed variation of the beam profile can be accurately described despite the sharp thickness reductions and strong wave fluctuations in the ABH part. The vibration characteristics of the periodic beam are investigated under two scenarios: the resonance frequency of the LRs is tuned to coincide with the passband of the beam or the stopband of the beam. Improved vibration attenuations are observed in both scenarios, but the coupling behaviors and the underlying mechanisms are drastically different. To seek a clear explanation, an equivalent model of three degrees of freedom is established. By correlating the dynamics of the equivalent model with those of the beam model, it is found that the ratio between the stiffness of the resonator and that of the host beam plays an important role in forming new bandgaps. When the resonance of the LRs occurs in the passband of the ABH beam, the new bandgaps are a super-positioned effect of the original ABH bandgap and the LR bandgap. When the resonance of the LRs occurs outside the ABH bandgap, interactions between the LRs and the host beam are greatly enhanced, leading to an interesting frequency-splitting effect that dominates the formation of new bandgaps. Finally, the vibration responses of the proposed beam are investigated through experiments.
Large algebraic integers
Authors: Simon Denis , Terracini Lea
Published: June 2023

An algebraic integer is said large if all its real or complex embeddings have absolute value larger than [Formula: see text]. An integral ideal is said large if it admits a large generator. We investigate the notion of largeness, relating it to some arithmetic invariants of the field involved, such as the regulator and the covering radius of the lattice of units. We also study its connection with the Weil height and the Bogomolov property. We provide an algorithm for testing largeness and give some applications to the construction of floor functions arising in the theory of continued fractions.
On ∞-adic and v-adic multiple zeta functions in positive characteristic
Authors: Matsuzuki Daichi
Published: June 2023

In this paper, we pursue positive characteristic analogs of the results of Furusho, Komori, Matsumoto, and Tsumura on [Formula: see text]-adic multiple [Formula: see text]-functions. We consider [Formula: see text]-adic and [Formula: see text]-adic multiple zeta functions concerned by Anglès, Ngo Dac, and Tavares Ribeiro. Our main results in this paper consist of: (1) integral expressions of special values of [Formula: see text]-adic multiple zeta functions, (2) integral expressions of [Formula: see text]-adic multiple zeta functions themselves that are similar to those of [Formula: see text]-adic multiple [Formula: see text]-functions, (3) Kummer-type congruences for the special values of [Formula: see text]-adic multiple zeta functions at integers, (4) relationships between special values of [Formula: see text]-adic and those of [Formula: see text]-adic multiple zeta functions at negative integers, and (5) orthogonal properties of multiple zeta functions and multiple zeta star functions.
A Freiman-type Theorem for restricted sumsets
Authors: Daza David , Huicochea Mario , Martos Carlos , Trujillo Carlos
Published: June 2023

Let [Formula: see text] and [Formula: see text] be nonempty finite subsets of [Formula: see text]. Freiman’s [Formula: see text] Theorem states that if [Formula: see text], then [Formula: see text] is contained in a short arithmetic progression. Freiman generalized his theorem establishing that if [Formula: see text], then [Formula: see text] and [Formula: see text] are contained in short arithmetic progressions with common difference. Take [Formula: see text] and write [Formula: see text]. There have been several attempts to generalize Freiman’s statements for restricted sumsets [Formula: see text]. In the last few years, there have been some results establishing that (under reasonable technical conditions) if [Formula: see text] for an absolute constant [Formula: see text] and [Formula: see text] such that [Formula: see text] whenever [Formula: see text], then there are arithmetic progressions [Formula: see text] and [Formula: see text] with common difference such that [Formula: see text] and [Formula: see text] are small (in terms of [Formula: see text]), [Formula: see text] and [Formula: see text] where [Formula: see text] whenever [Formula: see text]. Furthermore, in some of the papers where these results appear, it was conjectured that the best possible value of [Formula: see text] such that the same conclusion is reached is [Formula: see text]. In this paper we confirm this conjecture.
Diversity in rationally parameterized number fields
Authors: Klahn Benjamin
Published: June 2023

Let [Formula: see text] be a curve defined over [Formula: see text] and let [Formula: see text] be a non-constant rational function on [Formula: see text] of degree [Formula: see text]. For every rational number [Formula: see text] pick a point [Formula: see text] such that [Formula: see text]. In this paper, we obtain lower bounds on the number of distinct fields among [Formula: see text] with [Formula: see text] under some assumptions on [Formula: see text]. We show that if [Formula: see text] has a pole of order at least 2 or if there is a rational number [Formula: see text] such that [Formula: see text] has a zero of order at least 2, then the set [Formula: see text] contains [Formula: see text] elements. We also obtain partial results when [Formula: see text] does not have a pole of order at least two.
A global invariant and Hasse invariants at finite or real primes
Authors: Huang Maozhou
Published: June 2023

In their 2019 paper, Lee and Park presented a formula for the arithmetic Chern–Simons invariant. This formula gives a relation between this invariant and the local Hasse invariants at certain finite primes. Given a number field having real embeddings, we present alternative formulas to give relations between the arithmetic Chern–Simons invariant and the local Hasse invariants at certain primes including all real ones. As an application, we propose a new problem which concerns the existence of a certain [Formula: see text]-cocycle. If the answer to this problem is positive, the obtained statement is an analogue of the Albert–Brauer–Hasse–Noether theorem.
The Shimura–Shintani correspondence via singular theta lifts and currents
Authors: Crawford Jonathan , Funke Jens
Published: June 2023

We describe the construction and properties of a singular theta lift for the orthogonal group [Formula: see text]. We obtain locally harmonic Maass forms in the sense of Bringmann, Kane and Kohnen with singular sets along geodesics in the upper half plane. We consider these forms as currents and derive properties of the Shimura–Shintani correspondence. This work provides extensions of the theta lifts considered by Hövel and Bringmann, Kane and Viazovska.
On the analytic strong multiplicity one for SL(2)
Authors: Guan Bin
Published: June 2023

With the results on [Formula: see text] and the adjoint lift, a restricted version of analytic strong multiplicity one is proved which holds for [Formula: see text], the restriction being that the representations are required to be generic with respect to the same additive character. In this case there exists a positive constant [Formula: see text] depending on [Formula: see text] and the number field [Formula: see text] only, such that [Formula: see text] can be decided completely by its local components [Formula: see text] with norm [Formula: see text], where [Formula: see text] is the analytic conductor of [Formula: see text] with respect to the adjoint lift. A similar argument also allows us to simplify the proof of the restricted version of strong multiplicity one for [Formula: see text] originally given in [J. Chai and Q. Zhang, A strong multiplicity one theorem for [Formula: see text], Pacific J. Math. 285(2) (2016) 345–374].
Hierarchical Attention-Based Sensor Fusion Strategy for Depth Estimation in Diverse Weather
Authors: Xiong Mengchen , Xu Xiao , Yang Dong , Seguel Fabian , Steinbach Eckehard
Published: June 2023

In this paper, we propose a hierarchical attention-based sensor fusion strategy for depth estimation under various weather conditions. Multiple-sensor fusion is proven as a promising solution for predicting accurate depth maps in diverse weather conditions, especially for extreme weather conditions. However, most of the current studies simply fuse the information from different sensors without jointly considering the difference in their performance at the sensor level and feature level. To fill this gap, our hierarchical attention-based fusion strategy uses two attention mask-generation modules that weigh sensor data from branches (i.e. different sensors) and features. With the cooperation of these two masks, our system is able to determine the adaptive contribution of each sensor as well as the individual contribution of each feature in the sensor regarding their performance in different weather. We compare the proposed methods with the baseline, i.e. the late fusion Sparse-to-Dense model, and two extended models individually with the branch-wise-only and feature-wise-only masks. The results show a robust and superior performance of our methods even in clear environments where the baseline already performs well enough. Moreover, we investigate the performance of RGB camera, radar, and LiDAR in foggy environments comprehensively by visualizing the generated mask. Our results show a significantly increased importance of radar sensors in extreme weather conditions, e.g. dense fog.
Adaptive Semantic Matching in a Multilingual Context
Authors: Liu Zhan , Glassey Balet Nicole
Published: June 2023

In an increasingly multilingual digital world, information management tools must support the simultaneous use and matching of multiple natural languages. A prerequisite for this is that the underlying database engine seamlessly processes multilingual data across languages. However, most natural language processing-based techniques have focused on developing monolingual matching algorithms, often ignoring context knowledge and external domain-based sources, which lead to incomplete and inaccurate matching results in a multilingual environment. The purpose of this study is to propose an adaptive semantic matching method with context knowledge and user involvement as two new dimensions for matching the semantically related entities ontologies. We present a comprehensive evaluation of our solution by applying it in a multilingual e-commerce platform case study, which performed well on matching accuracy.
PrimitivePose: Generic Model and Representation for 3D Bounding Box Prediction of Unseen Objects
Authors: Kriegler Andreas , Beleznai Csaba , Gelautz Margrit , Murschitz Markus , Göbel Kai
Published: June 2023

A considerable amount of research is concerned with the challenging task of estimating three-dimensional (3D) pose and size for multi-object indoor scene configurations. Many existing models rely on a priori known object models, such as 3D CAD models and are therefore limited to a predefined set of object categories. This closed-set constraint limits the range of applications for robots interacting in dynamic environments where previously unseen objects may appear. This paper addresses this problem with a highly generic 3D bounding box detection method that relies entirely on geometric cues obtained from depth data percepts. While the generation of synthetic data, e.g. synthetic depth maps, is commonly used for this task, the well-known synth-to-real gap often emerges, which prohibits transition of models trained solely on synthetic data to the real world. To ameliorate this problem, we use stereo depth computation on synthetic data to obtain pseudo-realistic disparity maps. We then propose an intermediate representation, namely disparity-scaled surface normal (SN) images, which encodes geometry and at the same time preserves depth/scale information unlike the commonly used standard SNs. In a series of experiments, we demonstrate the usefulness of our approach, detecting everyday objects on a captured data set of tabletop scenes, and compare it to the popular PoseCNN model. We quantitatively show that standard SNs are less adequate for challenging 3D detection tasks by comparing predictions from the model trained on disparity alone, SNs and disparity-scaled SNs. Additionally, in an ablation study we investigate the minimal number of training samples required for such a learning task. Lastly, we make the tool used for 3D object annotation publicly available at: https://preview.tinyurl.com/3ycn8v5k . A video showcasing our results can be found at: https://preview.tinyurl.com/dzdzabek .
Cost-Effective Solution for Fallen Tree Recognition Using YOLOX Object Detection
Authors: Moon Hearim , Lee Juyeong , Kim Doyoon , Park Eunsik , Moon Junghyun , Lee Minsun , Lee Minji , Matson Eric T.
Published: June 2023

Tropical cyclones are the world’s deadliest natural disasters, especially causing tree death by pulling out or breaking the roots of trees, which has a great impact on the forest ecosystem and forest owners. To minimize additional damage, an efficient approach is needed to quickly grasp information on the location and distribution of fallen trees. There are several studies that try to detect fallen trees in the past, but most of the research requires huge costs and is difficult to utilize. This research focuses on resolving those problems. Unmanned aerial vehicle (UAV) is widely used for ground detection for those who need a cost-effective way while pursuing high-resolution images. To take this advantage, this research collects data mainly using a UAV with an auxiliary high-resolution camera. The collected data is used for training the YOLOX model, an object detection algorithm, which can perform an accurate detection within a remarkably short time period. Also, by using YOLOX as a detection model, a wide-range versatility is obtained, which means, the solution driven by this research can be utilized for every scenario where inexpensive, but highly reliable object detection result is needed. This research implements a visualization application that displays detection results, calculated by a trained model, in a client-friendly way. Fallen trees are recognized in images or videos, and the analyzed results are provided as web-based visualizations.
Transparency in Medical Artificial Intelligence Systems
Authors: Quakulinski Lars , Koumpis Adamantios , Beyan Oya Deniz
Published: June 2023

Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.
Class-Aware Pseudo-Labeling for Non-Random Missing Labels in Semi-Supervised Learning
Authors: Gui Qian , Wu Xinting , Niu Baoning
Published: June 2023

Semi-supervised learning (SSL) is a classic missing label problem. Existing SSL algorithms always rely on the basic assumption, label missing completely at random (MCAR), where both labeled and unlabeled data share the same class distribution. Compared to MCAR, the label missing not at random (MNAR) problem is more realistic. In MNAR, the labeled and unlabeled data have different class distributions resulting in biased label imputation, which leads to the performance degradation of SSL models. Existing SSL algorithms can hardly perform well on tail classes (the classes with few training examples) in MNAR setting, since the pseudo-labels learned from unlabeled data tend to be biased toward head classes (the classes with a large number of training examples). To alleviate this issue, we propose a class-aware pseudo-labeling (CAPL) for non-random missing labels in SSL, which utilizes the unlabeled data by dynamically adjusting the threshold for selecting pseudo-labels. Under various MNAR settings, our method achieves up to 15.0% overall accuracy gain upon FixMatch in CIFAR-10 compared with existing baselines.
Hierarchical Time-Aware Summarization with an Adaptive Transformer for Video Captioning
Authors: Cardoso Leonardo Vilela , Guimarães Silvio Jamil Ferzoli , do Patrocínio Júnior Zenilton Kleber Gonçalves
Published: June 2023

A coherent description is an ultimate goal regarding video captioning via a couple of sentences because it might also affect the consistency and intelligibility of the generated results. In this context, a paragraph describing a video is affected by the activities used to both produce its specific narrative and provide some clues that can also assist in decreasing textual repetition. This work proposes a model, named Hierarchical time-aware Summarization with an Adaptive Transformer (HSAT), that uses a strategy to enhance the frame selection reducing the amount of information that needed to be processed along with attention mechanisms to enhance a memory-augmented transformer. This new approach increases the coherence among the generated sentences, assessing data importance (about the video segments) contained in the self-attention results and uses that to improve readability using only a small fraction of time spent by the other methods. The test results show the potential of this new approach as it provides higher coherence among the various video segments, decreasing the repetition in the generated sentences and improving the description diversity in the ActivityNet Captions dataset.
From Communist Allies to Pragmatics Partners: A Historical View of Albanian–Chinese Relations
Authors: Kreka Alba , Bezeraj Selim
Published: June 2023

The Albanian–Chinese alliance existed between 1960 and 1978, an unusual relationship due to the roles and influences of both in and out of the Communist Bloc. Although ideological affinity is generally regarded as essential to the performance of an alliance, contingency and circumstance also matter. The main factors that influenced the character of the Sino-Albanian alliance will be revealed through the official correspondence of that time between the two countries, founded mainly in Central State Archive. Albania and China collaborated for 18 years and after their ideological conflicts Albania followed a self-isolation policy. After the fall of Communism in Albania, bilateral ties developed under the new circumstances for several years. It was China’s Belt and Road Initiative (BRI) that helped create new conditions for cooperation. Through a descriptive analysis, this paper presumes/hypothesizes whether being old allies in the past can be considered as a prerequisite for cooperation in the present. In the beginning, Albania assessed the potential of BRI and proposed some important projects with China, but lately it has shown more ambivalence and reluctance toward the “[Formula: see text]” mechanism. We also examine the main factors on the Albanian side that may bolster or undercut future bilateral cooperation under the “16+1” framework.
Exporting the American Dream? Ideology and US China Policy Since 9/11
Authors: Wen Ma
Published: June 2023

Ideology is an important though not dominant factor driving the US foreign policy. Amid intensifying great-power rivalry, Washington in recent years has increasingly stepped up its ideological offensive against Beijing. Latest US efforts in this regard include exporting core values associated with the American Dream, e.g., individual freedoms, liberal ideals, and democratic institutions. The three consecutive administrations of George W. Bush, Barack Obama, and Donald Trump had all incorporated ideological components into their policy toward Beijing despite their differences of policy priority vis-à-vis China. Long vigilant about the West’s ideological offensive, Beijing had been pushing back against Washington’s efforts by promoting a rival narrative, the Chinese Dream. Even though Beijing has roundly rejected Washington’s framing of US–China relations in years to come as democracy versus autocracy rivalry and tried hard to insulate China’s relations with the rest of the world from rising geopolitical tensions with the United States, the great and growing ideological rift between the two major powers and their divergent perspectives on and approaches to the big issues of the day may herald an era in which an increasing number of global challenges as well as bilateral disputes and crises defy easy and quick solutions.
Analysis and Interpretation of Primary and Derived Data Sets in Cardiology
Authors: Kerkhof Peter L. M. , Li John K.-J. , Diaz-Navarro Rienzi A. , Heyndrickx Guy R. , Faes Theo J. C. , Tona Francesco , Handly Neal
Published: June 2023

Investigators collect data and present them in a way that offers the best insight regarding the questions at hand. To facilitate understanding of certain aspects, it may occasionally be useful to rearrange primary data and formulate them as derived variables. For example, the travel distance divided by the invested time yields average velocity (as m/s). Problems may arise when interpreting ratios that fail to have a physical dimension. For example, current TV-sets have a fixed ratio for height and width, implying that we need an additional detail to define its size. Size then is determined by the diagonal, which can be calculated from the two sides using the Pythagorean theorem. Similarly, paired hemodynamic variables may be expressed as ratios. Again, a fixed ratio may refer to a variety of underlying primary data which require consideration if the ratio is unitless. In this survey, we evaluate several derived metrics commonly used in cardiovascular studies, and offer comprehensive analysis strategies.
P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning
Authors: Timilsina Ashutosh , Silvestri Simone
Published: June 2023

Peer-to-peer (P2P) energy tradingis a decentralized energy market where local energyprosumersact as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling and assume users’ sustained active participation and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this article, we propose anautomatedP2P energy-trading framework that specifically considers the users’ perception by exploitingprospect theory. We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy (DEbATE) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), is based on Q-learning. Additionally, given the scalability issues ofPQR, we propose a Deep Q-Network-based algorithm calledProDQNthat exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve 26% higher perceived value for buyers and generate 7% more reward for sellers, compared to recent state-of-the-art approaches.
The Method of Moving Frames for Surface Global Parametrization
Authors: Coiffier Guillaume , Corman Etienne
Published: June 2023

This article introduces a new representation of surface global parametrization based on
Cartan’s method of moving frames
. We show that a system of
structure equations
, characterizing the local coordinates changes with respect to a local frame system, completely characterizes the set of possible cone parametrizations. The discretization of this system provably provides necessary and sufficient conditions for the existence of a valid mapping. We are able to derive a versatile algorithm for surface parametrization, allowing feature constraints and singularities. As the first structure equation is independent of the global coordinate system, we do not require prior knowledge of cuts or cone positions. So, a single non-linear least-square problem is enough to place quantized cones while minimizing a given distortion energy. We are therefore able to take full advantage of the link between the parametrization geometry and the topology of its cone metric to solve challenging constrained parametrization problems.
Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction
Authors: Wang Ruihang , Xia Deneng , Cao Zhiwei , Wen Yonggang , Tan Rui , Zhou Xin
Published: June 2023

Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for the online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration approaches apply heuristics to search the model configurations. However, each search step requires a long-lasting process of repeatedly solving the CFD model, rendering them impractical, especially for complex CFD models. This article presents
Kalibre
, a knowledge-based neural surrogate approach that calibrates a CFD model by iterating four steps of (i) training a neural surrogate model, (ii) finding the optimal parameters through neural surrogate retraining, (iii) configuring the found parameters back to the CFD model, and (iv) validating the CFD model using sensor-measured data. Thus, the parameter search is offloaded to the lightweight neural surrogate. To speed up Kalibre’s convergence, we incorporate prior knowledge in training data initialization and surrogate architecture design. With about ten hours of computation on a 64-core processor, Kalibre achieves mean absolute errors (MAEs) of 0.57°C and 0.88°C in calibrating the CFD models of two production data halls hosting thousands of servers. To accelerate CFD-based simulation, we further propose
Kalibreduce
that incorporates the energy balance principle to reduce the order of the calibrated CFD model. Evaluation shows the model reduction only introduces 0.1°C to 0.27°C extra errors while accelerating the CFD-based simulations by thousand times.
Multi-Label Feature Selection Via Adaptive Label Correlation Estimation
Authors: Zhang Zan , Zhang Zhe , Yao Jialu , Liu Lin , Li Jiuyong , Wu Gongqing , Wu Xindong
Published: June 2023

In multi-label learning, each instance is associated with multiple labels simultaneously. Multi-label data often have noisy, irrelevant, and redundant features of high dimensionality. Multi-label feature selection has received considerable attention as an effective means for dealing with high-dimensional multi-label data. Many multi-label feature selection methods exploit label correlations to help select features. However, finding label correlations and selecting features in existing multi-label feature selection methods are often two separate processes, the existence of noises and outliers in training data makes the label correlations exploited from label space less reliable. Therefore, the learned label correlations may mislead the feature selection process and result in the selection of less informative features. This article proposes a novel algorithm named ROAD, i.e., multi-label featuRe selectiOn via ADaptive label correlation estimation. ROAD jointly performs adaptive label correlation exploration and feature selection with alternating optimization to obtain reliable estimation of label correlations, which can more effectively reveal the intrinsic manifold structure among labels and lead to the selection of a more proper feature subset. Comprehensive experiments on several frequently used datasets validate the superiority of ROAD against the state-of-the-art multi-label feature selection algorithms.
Unveiling the Role of Inflammatory Mediators and Gut Microbiome in Appendicitis: Types and Applications in Clinical Scoring
Authors: Elwan Taher H. , Abdennour Aimen Elsheikh Khalil , Akl Usama , Elshennawy A. T. M. , Shatla Ibrahim M.
Published: June 2023

Appendicitis is a common medical condition that affects millions of people worldwide. It is characterized by inflammation of the appendix. The exact mechanisms that trigger the inflammatory response in appendicitis are not well understood, but it is known that inflammatory mediators play a crucial role in the development and progression of the disease. In this review, we provide a comprehensive overview of the current understanding of the role of inflammatory mediators in the pathogenesis of appendicitis. This review article examines the various types of inflammatory mediators involved, including cytokines, chemokines, and prostaglandins, and discusses their interactions with other cells and molecules in the inflammatory cascade. Our review highlights the complex interplay between inflammatory mediators in the development of appendicitis and their potential implications for diagnosis and treatment of the disease. We discuss the potential for targeted therapies aimed at reducing the production or activity of specific inflammatory mediators, as well as the potential for new diagnostic approaches based on the detection of specific mediators in the blood or other bodily fluids. At the end, the role of inflammatory mediators in appendicitis is an active area of research, and continued investigation is necessary to fully elucidate the mechanisms involved. However, the growing understanding of the complex interactions between these molecules offers new opportunities for the development of targeted therapies and diagnostic tools for this common and potentially serious condition