Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Inferential Control of Neural State-Space Models for Autonomous Vehicle Motion Planning

Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the vehicle dynamics. As the neural state-space model will lead to highly complex, nonlinear and nonconvex optimization landscapes, mainstream gradient-based MPC methods will be computationally too heavy to be a viable solution. In a departure, we propose the idea of model predictive inferential control (MPIC), which seeks to infer the best control decisions from the control objectives and constraints. Following the idea, we convert the MPC problem for motion planning into a Bayesian state estimation problem. Then, we develop a new particle filtering/smoothing approach to perform the estimation. This approach is implemented as banks of unscented Kalman filters/smoothers and offers high sampling efficiency, fast computation, and estimation accuracy. We evaluate the MPIC approach through a simulation study of autonomous driving in different scenarios, along with an exhaustive comparison with gradient-based MPC. The results show that the MPIC approach has considerable computational efficiency, regardless of complex neural network architectures, and shows the capability to solve large-scale MPC problems for neural state-space models.
RoboCLIP: One Demonstration is Enough to Learn Robot Policies

Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL) methods attempt to circumvent these problems by utilizing expert demonstrations but typically require a large number of in-domain expert demonstrations. Inspired by advances in the field of Video-and-Language Models (VLMs), we present RoboCLIP, an online imitation learning method that uses a single demonstration (overcoming the large data requirement) in the form of a video demonstration or a textual description of the task to generate rewards without manual reward function design. Additionally, RoboCLIP can also utilize out-of-domain demonstrations, like videos of humans solving the task for reward generation, circumventing the need to have the same demonstration and deployment domains. RoboCLIP utilizes pretrained VLMs without any finetuning for reward generation. Reinforcement learning agents trained with RoboCLIP rewards demonstrate 2-3 times higher zero-shot performance than competing imitation learning methods on downstream robot manipulation tasks, doing so using only one video/text demonstration.
Irreducibility of Markov Chains on simplicial complexes, the Spectrum of the Discrete Hodge Laplacian and Homology

Random walks on graphs are a fundamental concept in graph theory and play a crucial role in solving a wide range of theoretical and applied problems in discrete math, probability, theoretical computer science, network science, and machine learning. The connection between Markov chains on graphs and their geometric and topological structures is the main reason why such a wide range of theoretical and practical applications exist. Graph connectedness ensures irreducibility of a Markov chain. The convergence rate to the stationary distribution is determined by the spectrum of the graph Laplacian which is associated with lower bounds on graph curvature. Furthermore, walks on graphs are used to infer structural properties of underlying manifolds in data analysis and manifold learning. However, an important question remains: can similar connections be established between Markov chains on simplicial complexes and the topology, geometry, and spectral properties of complexes? Additionally, can we gain topological, geometric, or analytic information about a manifold by defining appropriate Markov chains on its triangulations? These questions are not only theoretically important but answers to them provide powerful tools for the analysis of complex networks that go beyond the analysis of pairwise interactions. In this paper, we provide an integrated overview of the existing results on random walks on simplicial complexes, using the novel perspective of signed graphs. This perspective sheds light on previously unknown aspects such as irreducibility conditions. We show that while up-walks on higher dimensional simplexes can never be irreducible, the down walks become irreducible if and only if the complex is orientable. We believe that this new integrated perspective can be extended beyond discrete structures and enables exploration of classical problems for triangulable manifolds.
Full intrinsic quadrics of dimension two

A full intrinsic quadric is a normal complete variety with a finitely generated Cox ring defined by a single quadratic relation of full rank. We describe all surfaces of this type explicitly via local Gorenstein indices. As applications, we present upper and lower bounds in terms of the Gorenstein index for the degree, the log canonicity and the Picard index. Moreover, we determine all full intrinsic quadric surfaces admitting a K\"ahler-Einstein metric.
LiveVV: Human-Centered Live Volumetric Video Streaming System

Volumetric video has emerged as a prominent medium within the realm of eXtended Reality (XR) with the advancements in computer graphics and depth capture hardware. Users can fully immersive themselves in volumetric video with the ability to switch their viewport in six degree-of-freedom (DOF), including three rotational dimensions (yaw, pitch, roll) and three translational dimensions (X, Y, Z). Different from traditional 2D videos that are composed of pixel matrices, volumetric videos employ point clouds, meshes, or voxels to represent a volumetric scene, resulting in significantly larger data sizes. While previous works have successfully achieved volumetric video streaming in video-on-demand scenarios, the live streaming of volumetric video remains an unresolved challenge due to the limited network bandwidth and stringent latency constraints. In this paper, we for the first time propose a holistic live volumetric video streaming system, LiveVV, which achieves multi-view capture, scene segmentation \&reuse, adaptive transmission, and rendering. LiveVV contains multiple lightweight volumetric video capture modules that are capable of being deployed without prior preparation. To reduce bandwidth consumption, LiveVV processes static and dynamic volumetric content separately by reusing static data with low disparity and decimating data with low visual saliency. Besides, to deal with network fluctuation, LiveVV integrates a volumetric video adaptive bitrate streaming algorithm (VABR) to enable fluent playback with the maximum quality of experience. Extensive real-world experiment shows that LiveVV can achieve live volumetric video streaming at a frame rate of 24 fps with a latency of less than 350ms.
Radio Galaxy Zoo: Tagging radio subjects using text

Abstract RadioTalk is a communication platform that enabled members of the Radio Galaxy Zoo (RGZ) citizen science project to engage in discussion threads and provide further descriptions of the radio subjects they were observing in the form of tags and comments. It contains a wealth of auxiliary information which is useful for the morphology identification of complex and extended radio sources. In this paper, we present this new dataset, and for the first time in radio astronomy, we combine text and images to automatically classify radio galaxies using a multi-modal learning approach. We found incorporating text features improved classification performance which demonstrates that text annotations are rare but valuable sources of information for classifying astronomical sources, and suggests the importance of exploiting multi-modal information in future citizen science projects. We also discovered over 10000 new radio sources beyond the RGZ-DR1 catalogue in this dataset.
A pr 2 01 7 Partial resampling to approximate covering integer programs ∗

We consider column-sparse positive covering integer programs, which generalize set cover and which have attracted a long line of research developing (randomized) approximation algorithms. We develop a new rounding scheme based on the Partial Resampling variant of the Lovász Local Lemma developed by Harris & Srinivasan (2013). This achieves an approximation ratio of 1 + ln(∆1+1) amin +O( √ log(∆1+1) amin ), where amin is the minimum covering constraint and ∆1 is the maximum l1-norm of any column of the covering matrix (whose entries are scaled to lie in [0, 1]). When there are additional constraints on the sizes of the variables, we show an approximation ratio of 1 + O( log(∆1+1) aminǫ + √ log(∆1+1) amin ) to satisfy these size constraints up to multiplicative factor 1 + ǫ, or an approximation of ratio of ln∆0 +O( √ log∆0) to satisfy the size constraints exactly (where ∆0 is the maximum number of non-zero entries in any column of the covering matrix). We also show nearly-matching inapproximability and integrality-gap lower bounds. These results improve asymptotically, in several different ways, over results shown by Srinivasan (2006) and Kolliopoulos & Young (2005). We show also that our algorithm automatically handles multi-criteria programs, efficiently achieving approximation ratios which are essentially equivalent to the single-criterion case and which apply even when the number of criteria is large.
Microwave Limb Sounder measurement of stratospheric SO2 from the Mt. Pinatubo volcano

This paper presents measurements of sulfur dioxide densities in the stratosphere made by the microwave limb sounder (MLS) on the upper atmosphere research satellite. The SO[sub 2] came from the eruption of the Mt Pinatubo volcano which injected a massive quantity of gas into the stratosphere. The MLS is able to measure the decay rate of the gas densities based on its extended time and spatial coverage, and from this decay rate infer the OH densities in the stratosphere, since OH is the major reactive species which converts the SO[sub 2] into sulfuric acid.
Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and statistical paradigms of cognition, has been an active research area of Artiﬁcial Intelligence (AI) for many years. As NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks, it may serve as a catalyst for the next generation of AI. In the present paper, we provide a systematic overview of the important and recent developments of research on NeSy AI. Firstly, we introduce study history and background concepts of this area. After-ward, we categorize recent approaches along several main characteristics that underline this research paradigm, including neural-symbolic interrelation, neural architecture, knowledge representation, and functionality. Then, we brieﬂy discuss the successful application of modern NeSy approaches in several domains. Finally, we identify the open problems together with potential future research directions.
A constructive algorithm for the LLL on permutations

While there has been significant progress on algorithmic aspects of the Lov\'{a}sz Local Lemma (LLL) in recent years, a noteworthy exception is when the LLL is used in the context of random permutations. The breakthrough algorithm of Moser & Tardos only works in the setting of independent variables, and does not apply in this context. We resolve this by developing a randomized polynomial-time algorithm for such applications. A noteworthy application is for Latin transversals: the best-known general result here (Bissacot et al., improving on Erd\H{o}s and Spencer), states that any $n \times n$ matrix in which each entry appears at most $(27/256)n$ times, has a Latin transversal. We present the first polynomial-time algorithm to construct such a transversal. We also develop RNC algorithms for Latin transversals, rainbow Hamiltonian cycles, strong chromatic number, and hypergraph packing. In addition to efficiently finding a configuration which avoids bad-events, the algorithm of Moser & Tardos has many powerful extensions and properties. These include a well-characterized distribution on the output distribution, parallel algorithms, and a partial resampling variant. We show that our algorithm has nearly all of the same useful properties as the Moser-Tardos algorithm, and present a comparison of this aspect with recent works on the LLL in general probability spaces.
Ein kritischer DRG-Blick auf Fälle mit chronisch entzündlichen Darmerkrankungen

Zusammenfassung Einleitung Ob stationäre Behandlungsfälle mit chronisch-entzündlichen Darmerkrankungen (CED) in deutschen Krankenhäusern kostendeckend vergütet werden, wurde bislang nicht untersucht. Vor diesem Hintergrund befasst sich die vorliegende Arbeit mit der Vergütungssituation (Kosten-Erlös-Vergleich) von CED in deutschen Krankenhäusern in Abhängigkeit von der Erkrankungskomplexität sowie den Versorgungstypen. Methodik Für diese retrospektive Studie standen anonymisierte Falldaten inkl. Kostendaten aus der InEK-Kalkulation (§ 21–4 KHEntgG) des DRG-Projektes der Deutschen Gesellschaft für Gastroenterologie, Verdauungs- und Stoffwechselerkrankungen (DGVS) aus dem Jahr 2019 zur Verfügung. Analysiert wurden 3385 Fälle mit CED als Hauptdiagnose aus 49 Krankenhäusern. Um die Auswirkungen der Krankheitskomplexität auf die Vergütungssituation zu untersuchen, wurden verschiedene Variablen analysiert, darunter gastroenterologische Komplikationen, Infektionen, Aufnahmeanlass und Zusatzentgelte. Zur Untersuchung möglicher Zentrumseffekte wurden die Krankenhäuser nach Versorgungstyp, im Wesentlichen definiert durch Bettenanzahl, gruppiert. Ergebnisse Die vorliegende Arbeit zeigt, dass sämtliche Versorgungstypen im Durchschnitt als nicht kostendeckend einzustufen sind. Die Unterdeckung liegt durchschnittlich bei 10 % (296 € absolute Unterdeckung) und variiert zwischen den Versorgungstypen. Fälle mit höherer Komplexität weisen eine höhere Kostenunterdeckung auf als Fälle mit geringerer Komplexität. Auf DRG-Ebene sind die analysierten Fallkosten der 3 häufigsten CED-DRGs für Normallieger höher als die InEK-Kosten; jedoch ist das Delta nicht signifikant. Jedoch tragen Fälle mit dem Aufnahmegrund Verlegung spezifischer DRGs signifikant höhere Kosten. Diskussion Unsere Ergebnisse zeigen, dass CED nicht kostendeckend vergütet werden. Dies ist auf die unzureichende Vergütung von gastroenterologischen Komplikationen, Infektionen, spezifischen Prozeduren und Notfall- und Verlegungsfällen zurückzuführen. Verlegungsfälle tragen signifikant höhere Kosten.
Symptoms and Management of Aseptic Liver Abscesses

ABSTRACT Aseptic liver abscesses occur very rarely. Clinical guidelines on the management of the disease do not exist, and the diagnosis is challenging. We screen MEDLINE and PUBMED databases for relevant case reports from inception to November 2022. Information on patient age, sex, initial symptoms, the extent of abscess formation, further diagnoses, treatment, and course of the disease is analyzed. Thirty cases with sterile hepatic abscess formation are identified. In most patients (n=18), the spleen is affected as well. Patients typically present with fever, abdominal pain, and increased inflammatory values. Comorbidity with inflammatory bowel disease is very common (n=18) and is associated with a significantly younger age at the time of hepatic abscess development. In addition, many patients show autoimmune-mediated cutaneous, ocular, or arthritic rheumatoid manifestations. Histological examination of abscess material reveals neutrophilic infiltration. The majority of patients initially receive corticosteroid therapy. Furthermore, response to azathioprine, anti-TNF-α antibodies, and other immunomodulatory drugs is reported. Ten out of fourteen patients with a long-term follow-up (≥ 36 months) have at least one relapse of hepatic abscess formation. Aseptic hepatic abscesses should be considered in the case of sterile punctures and non-response to antibiotics. Patients with aseptic liver abscesses have a high risk of recurrence warranting immunomodulatory maintenance therapy.
Thrip Damage on Off-Season and In-Season Mangosteen Production in Nakhon Si Thammarat, Thailand

The damage of thrips (Thripidae: Thysanoptera) on mangosteen production were studied in local orchards in Nakhon Si Thammarat from September 2008 to July 2010. It was found that the damage was related directly to thrip population and the cycles of mangosteen growth such as the young leaves, flowering and young fruit stages. For the first and second year-rounds, off-season production, thrip abundance in October was averaged at 0.57 and 0.26 thrips/shoot in September. For the in-season production in April; the amount of thrips was averaged at 0.12 with 0.15 thrips/shoot, respectively. The lowest amount of thrips was noticed in the harvesting season of the two-year-round production. The damage at the flowering and young fruits stages due to rough scarring on the fruit surfaces and calyxes at the harvesting period were caused by thrips. The average rough scars on the fruit surface in the first- and second year- round, off-season production were 22.03 and 28.87 %; in-season production were 23.12 and 21.60 %, respectively. However, the rough scars on fruit calyxes at the harvesting period were found to be 100 %. The fruit gamboges in the first- and second year- round, off-season production were averaged at 45.40 and 23.92 %; and in-season production at 46.44 and 13.19 %, respectively. Over 20 % of rough scars appearing on fruit surfaces of the total mangosteen production lowered the quality for export. Good management in the local orchards is essential for increasing the marketing quality of mangosteen production.
Research and design of fresh food traceability data collection and processing technology based on edge computing

The immutability of blockchain can ensure the security and veracity of fresh food information, but in the process of data collection and processing by edge nodes and servers, a large quantity of data that must be uploaded to the blockchain network for storage will be generated quickly. This paper designs and proposes the edge cloud data collaboration technology of message queue, which uses RabbitMQ message middleware to provide cache space for the data waiting to be uploaded to the storage and achieves batch uploading by matching the throughput of the data accepted by the blockchain network, in order to address the problem that the data uploading speed does not match the data generation speed and thus causes data backlog. On the basis of an information tracing architecture that combines peripheral cloud collaboration and message queue technology, smart contracts were developed to submit authenticated data to the blockchain and IPFS. The prototype verifies the viability of the system and ensures the safety and dependability of the storage of fresh food data traceability information.
DDoS attack detection based on LSTM-GRU in SDN environment

The implementation of Software-Defined Networking (SDN) as a new network architecture brings about a variety of novel characteristics and application scenarios, decreasing network development redundancy, but it also raises serious security concerns. This study describes and develops the recurrent neural network-based LSTM-GRU detection algorithm for Distributed Denial-of-Service attacks (DDoS) assaults in SDN, which uses LSTM and GRU as the base model and stacks them to achieve high accuracy classification of traffic. According to tests, the detection algorithm outperforms conventional detection techniques, achieving accuracy levels of 99.53% and 98.78% on the CIC-IDS 2017 and CIC-DDoS 2019 datasets, respectively.
Gen Z image and precision analysis of online games big data based on the web crawler technology

In this paper, big data of Gen Z new media by relevant technology is collected and analyzed, and the structure of Gen Z employee image is structured and described, making group characteristics of enterprise employees and the precision information push possible. This study efficiently collects, integrates, and analyzes data, and more accurately reflects users’ preferences to meet the needs of today's growing Gen Z market with Beautiful Soup, as well as Python and other algorithms. We calculate the optimal number of clusters with the K-prototype clustering algorithm through the silhouette coefficient based on big data. The results provide a solution for tracking the user's dynamic data and accurate preferences and interests.
Observed changes in seasons: an overview

Within the last decade the study of phenology has taken on a new legitimacy in the area of climate change research. A growing literature reveals that a change in the timing of natural events is occurring in a wide range of locations and affecting a wide range of species. Changes in spring have been those most commonly reported, with the emphasis on an advance in spring linked to an increase in temperature. Detection of change in autumn is hampered by a smaller pool of available data, events that are harder to define (such as leaf coloration), and various influencing environmental factors triggering autumnal phases. Despite this, the general pattern may be towards a delay in autumn. Plant, animal and abiotic responses, especially in spring, are quite similar. Thus, it would appear that winter is being squeezed at both ends, and this effect, of increasing the growing season, should become more pronounced in the face of predicted global warming. Copyright © 2002 Royal Meteorological Society.
BSGD: a behavior and social based group discovery method

With the rise of social networks and online communities, group activities become increasingly common, which promotes the development of group recommendations. As the first step of group recommendation, group discovery aims to identify potential groups from users. Current group discovery methods often fail to consider behavior preferences and social connections simultaneously and may not be trained in a group-discovery-oriented manner. To address the above problems, we propose a novel group discovery method called BSGD based on users' behaviors and socialization, which introduces three tasks and implements group-discovery-oriented end-to-end training through multi-task joint training. The experiment results show that the proposed method has an excellent performance in both group discovery and gains in group recommendation on two real-world datasets.