Miracle-dz/UniFAR-FTUs huggingface dataset:
NDT_A_259800 1899..1905

Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China Background: Traditionally, cerebral arteriovenous malformations (cAVMs) are surgically treated with microscopy, and no cases receive full neuroendoscopy. We operated on two small cAVMs with hematoma under a neuroendoscope, and the two patients obtained excellent results. Objective: To explore the feasibility of treating cAVMs with a full transcranial neuroendoscopic approach. Methods: The clinical data and operative techniques were collected and described for two patients who were diagnosed with cAVMs with hematoma and treated via a full transcranial neuroendoscopic approach. Results: In the two patients, the hematomas were successfully evacuated, and the AVMs were removed simultaneously; both patients had achieved excellent recoveries at discharge. Conclusion: To our knowledge, this is the first report in the literature to successfully treat cAVMs with hematoma with a full transcranial neuroendoscopic approach, showing that this approach is feasible and may be an alternative for small ruptured cAVMs. However, while the feasibility of attempting this approach was demonstrated, it was difficult to certify its obvious advantage. Nevertheless, we believe that this approach might be difficult for middle and large AVMs due to the shortcomings of neuroendoscopy. More cases and practice are needed to confirm the merits and limitations of this new approach for AVMs.
Thinking About Knowledge

This thesis is about logics that are concerned with reasoning about knowledge. The acquisition of knowledge can be modelled. There are several systems to formalize reasoning about knowledge. Two systems that are used for epistemic logic are intuitionistic logic and the modal logic S4. Both logics will be discussed. The two differ a lot from each other, especially in the way that statements are considered to be true. But classical logic can be reduced to intuitionistic logic and intuitionistic logic can be reduced to the modal logic S4. This is done by the tranlations that were introduced by Godel. One of these translations will be examined explicitly. The thesis assumes some familiarity with classical logic.
An Approach to Insulation Condition Monitoring and Life Assessment in Emerging Electrical Environments

Distributed generation, dc transmission and distribution, and power electronics for ac/dc/ac conversion bring the advantages of increased control and flexibility in electric power management. These advantages also introduce challenges that must be managed. An electrical environment where voltage is not anymore strictly sinusoidal implies that a new approach for the design and maintenance of electrical insulation systems has to be devised. Non-sinusoidal voltage supply often causes increased failure probability and, thus, reduced reliability and life of an electrical asset. This research addresses the implications of the transient and steady waveform distortion introduced by power electronic systems and dc supply, for which the electrical insulation was designed and tested through consolidated criteria based on sinusoidal voltage supply. Based on condition monitoring, a health index definition is proposed, which allows the condition of the insulation of an electrical apparatus to be assessed as a function of operation time, and, based on the aging and life models, the evaluation of maintenance actions and of the feasibility and extent of life extension plans to be carried out.
Fault tolerance of beta-networks in interconnected multicomputer systems

Several proposals have been made recently for using a class of connecting networks called (beta)-networks in multicomputer systems. A (beta)-network is a connecting network composed of 2 x 2 crossbar switches called (beta)-elements. This dissertation presents a study of the fault tolerance of (beta)-networks intended for multicomputer applications. A fault model is specified which allows (beta)-elements to be stuck in either of their two normal states. A new connectivity property called dynamic full access (DFA) is introduced which serves as the criterion for fault tolerance in (beta)-networks. A fault is called critical if it destroys the DFA property. A minimal critical fault (MCF) is a critical fault none of whose proper subsets constitutes a critical fault. Two graph-theoretical characterizations of the minimal critical and noncritical faults of a (beta)-network are presented. A (beta)-network is defined to be k-fault tolerant or k-FT if the failure of any k or fewer (beta)-elements does not destroy DFA. The largest k for which a (beta)-network is k-FT is called the fault-tolerance (FT) parameter of the (beta)-network. In the synthesis of practical fault-tolerant (beta)-networks, network performance must also be considered. A performance criterion called the communication-delay (CD) parameter is introduced, which is defined as the worst possible transmission delay through the (beta)-network, measured in terms of the number of intervening (beta)-elements between any pair of communicating devices. It is proven that the FT parameter k and CD parameter of any (beta)-network with n (beta)-elements must satisfy the following bounds:^ 0 (LESSTHEQ) k (LESSTHEQ) n-1 (,(R-PERP))log(,2)n(,(L-PERP)) + 1 (LESSTHEQ) d (LESSTHEQ) n. ^ These bounds are shown to be tight. The modified inverse shuffle-exchange (MISE) network is shown to have FT parameter k=1 and CD parameter d= (,(R-PERP))log(,2)n(,(L-PERP)) + 1. Another network called the double parallel ring (DPR) network is shown to have FT parameter k=n-1 and CD parameter d=n. The DPR-network is unique in achieving the maximum value n-1 of the FT parameter. The preceding theoretical results are applied to the analysis of various (beta)-network structures. Some general properties of cascaded (beta)-networks are derived. FT and CD parameters are obtained for several well-known (beta)-networks.
First gravitational-wave search for intermediate-mass black hole mergers with higher-order harmonics

Current matched-filter searches for gravitational waves from binary black-hole mergers compare the calibrated detector data to waveform templates that omit the higher-order mode content of the signals predicted by General Relativity. However, higher-order emission modes become important for highly inclined asymmetric sources with masses above $\simeq 100 M_\odot$, causing current searches to be ill-suited at detecting them. We present a new gravitational-wave search that implements templates including higher-order modes, adapted signal-glitch discriminators, and trigger-ranking statistics to specifically target signals displaying strong higher modes, corresponding to nearly edge-on sources with total redshifted masses in the intermediate-mass black-hole range $M_T(1+z) \in (100,500) M_\odot$ and mass-ratios $q\in(1,10)$. Our search shows a volumetric sensitivity gain of up to $450\%$ to these signals compared to existing searches omitting higher-order modes. We deploy our search on public data from the third observing run of Advanced LIGO. While we find no statistically significant candidates beyond those already reported elsewhere, our search sets the stage to search for higher-mode rich signals in future observing runs. The efficient detection of such signals is crucial to performing detailed tests of General Relativity, observing strong-field phenomena, and maximizing the chances of observing the yet uncharted realm of intermediate-mass black hole binaries.
AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture

To address the ill-posed problem caused by partial observations in monocular human volumetric capture, we present AvatarCap, a novel framework that introduces animatable avatars into the capture pipeline for high-fidelity reconstruction in both visible and invisible regions. Our method firstly creates an animatable avatar for the subject from a small number (~20) of 3D scans as a prior. Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly recon-structs high-fidelity 3D textured models with dynamic details regardless of the visibility. To learn an effective avatar for volumetric capture from only few samples, we propose GeoTexAvatar, which leverages both geometry and texture supervisions to constrain the pose-dependent dynamics in a decomposed implicit manner. An avatar-conditioned volumetric capture method that involves a canonical normal fusion and a reconstruction network is further proposed to integrate both image observations and avatar dynamics for high-fidelity reconstruction in both observed and invisible regions. Overall, our method enables monocular human volumetric capture with detailed and pose-dependent dynamics, and the experiments show that our method outperforms state of the art. Code is available at https://github.com/lizhe00/AvatarCap.
Many-fields Packet Classification Using R-Tree and Field Concatenation Technique

Software-defined Networking is an approach that decouples the software-based control plane from the hardware-based data plane proposed for enterprise networks; OpenFlow is the most famous flexible protocol that can manage network traffic between the control and the data plane. Software-Defined Networking (SDN) requires up to 18 fields of the packets header to be checked against a big many-fields ruleset to categorize packets into flows, the process of categorizing packets into flows is called packet classification. Network switches process all packets belonging to the same flow in a similar manner by applying the same actions defined in the corresponding rule. Packet classification facilitates supporting new services such as filtering, blocking unsafe sites traffic, routing packets based on the packet's header information, and giving priority to specific flows. High-performance algorithms for many-field packet classification had been gained much interest in the research communities. This paper presents a new method to implement the many-fields packet classification of SDN flow table using Rectangle Tree (R-Tree). In this method, source and destination IP addresses from each flow table entry have been converted to a two-dimensional point. The remainders of the rule's fields have been concatenated into a single field by taking the most important bits with rules' ID in order to be inserted into the R-tree, for each rule an effective small binary flag is used to indicate the field's size, type, and ranges. Subsequently, searching is performed on the rectangle tree to find the matched rules according to the highest priority. In the simulation using the class-bench databases, the results show that this method achieves very good performance, classification speed and reduces the number of memory accesses significantly.
Weighted-norm preconditioners for a multi-layer tide model

We derive a linearized rotating shallow water system modeling tides, which can be discretized by mixed finite elements. Unlike previous models, this model allows for multiple layers stratified by density. Like the single-layer case~\cite{kirby2021preconditioning} a weighted-norm preconditioner gives a (nearly) parameter-robust method for solving the resulting linear system at each time step, but the all-to-all coupling between the layers in the model poses a significant challenge to efficiency. Neglecting the inter-layer coupling gives a preconditioner that degrades rapidly as the number of layers increases. By a careful analysis of the matrix that couples the layers, we derive a robust method that requires solving a reformulated system that only involves coupling between adjacent layers. Numerical results obtained using Firedrake confirm the theory.
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient

Knowledge distillation(KD) is a widely-used technique to train compact models in object detection. However, there is still a lack of study on how to distill between heterogeneous detectors. In this paper, we empirically find that better FPN features from a heterogeneous teacher detector can help the student although their detection heads and label assignments are different. However, directly aligning the feature maps to distill detectors suffers from two problems. First, the difference in feature magnitude between the teacher and the student could enforce overly strict constraints on the student. Second, the FPN stages and channels with large feature magnitude from the teacher model could dominate the gradient of distillation loss, which will overwhelm the effects of other features in KD and introduce much noise. To address the above issues, we propose to imitate features with Pearson Correlation Coefficient to focus on the relational information from the teacher and relax constraints on the magnitude of the features. Our method consistently outperforms the existing detection KD methods and works for both homogeneous and heterogeneous student-teacher pairs. Furthermore, it converges faster. With a powerful MaskRCNN-Swin detector as the teacher, ResNet-50 based RetinaNet and FCOS achieve 41.5% and 43.9% mAP on COCO2017, which are 4.1\% and 4.8\% higher than the baseline, respectively.
ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks

We aim at improving spoken language modeling (LM) using very large amount of automatically transcribed speech. We leverage the INA (French National Audiovisual Institute) collection and obtain 19GB of text after applying ASR on 350,000 hours of diverse TV shows. From this, spoken language models are trained either by fine-tuning an existing LM (FlauBERT) or through training a LM from scratch. New models (FlauBERT-Oral) are shared with the community and evaluated for 3 downstream tasks: spoken language understanding, classification of TV shows and speech syntactic parsing. Results show that FlauBERT-Oral can be beneficial compared to its initial FlauBERT version demonstrating that, despite its inherent noisy nature, ASR-generated text can be used to build spoken language models.
Segmenting Moving Objects via an Object-Centric Layered Representation

The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video. We make four contributions: First, we introduce an object-centric segmentation model with a depth-ordered layer representation. This is implemented using a variant of the transformer architecture that ingests optical flow, where each query vector specifies an object and its layer for the entire video. The model can effectively discover multiple moving objects and handle mutual occlusions; Second, we introduce a scalable pipeline for generating multi-object synthetic training data via layer compositions, that is used to train the proposed model, significantly reducing the requirements for labour-intensive annotations, and supporting Sim2Real generalisation; Third, we conduct thorough ablation studies, showing that the model is able to learn object permanence and temporal shape consistency, and is able to predict amodal segmentation masks; Fourth, we evaluate our model, trained only on synthetic data, on standard video segmentation benchmarks, DAVIS, MoCA, SegTrack, FBMS-59, and achieve state-of-the-art performance among existing methods that do not rely on any manual annotations. With test-time adaptation, we observe further performance boosts.
Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.
Wider: Scale Out Blockchain With Sharding by Account

The development of blockchain applications increased the demand for blockchain performance. Among the attempts of many blockchain scale-out solutions, sharding can improve performance and reduce the storage requirements of each node. Sharding enhances the throughput of the entire blockchain. Most sharding solutions have a fixed number of shards. We propose Wider chain with sharding by account. The number of shards is increased to unlimited. Meanwhile, Wider combines the idea of rollup and retains the main chain to confirm the status of the subchains. This design can avoid the Proof of Work computing power being scattered to the subchains and ensure the security of the blockchain.
A Model-Based Approach for the Estimation of Bearing Forces and Moments Using Outer Ring Deformation

Bearing load estimation would form a valuable addition to the fields of condition monitoring and system control. Despite effort spend on its development by all major bearing manufacturers, no product solution has come to market yet. This can be attributed to both the complexity in conditioning of the strain measurement as well as its nonlinearity with respect to the bearing loading. To address these issues, this paper proposes a novel approach based on modeling of the physical behavior of the bearing. An extended Kalman filter including a novel strain model is applied for signal conditioning, whereas an unscented Kalman filter including a semianalytical bearing model is proposed for reconstruction of the bearing load. An experimental study in both laboratory and field conditions shows that the proposed cascaded Kalman filtering approach leads to accurate estimates for all four considered bearings loads in various loading conditions. Besides an improvement on accuracy, the novel approach leads to a reduction in calibration effort.
iLibScope: Reliable Third-Party Library Detection for iOS Mobile Apps

Vetting the security impacts introduced by third-party libraries in iOS mobile apps requires a reliable library detection technique. Especially when a new vulnerability (or a privacy-invasive behavior) was discovered in a third-party library, there is a practical need to precisely identify the existence of third-party libraries and their versions for iOS apps. However, few studies have been proposed to tackle this problem, and they all suffer from the code duplication problem in different libraries. In this paper, we focus on third-party library detection in iOS mobile apps. Given an app, we aim to identify the integrated libraries and pinpoint their versions (or the version range). To this end, we first conduct an in-depth study on iOS third-party libraries to reveal the relationship between them to demystify code duplication. By doing so, we have the following two key observations: 1) even though two libraries can share some classes, however, the shared classes cannot be integrated into an app at the same time without causing a class name conflict; and 2) code duplication between multiple versions of two libraries can vary. Based on these observations, we propose a novel profile-based similarity comparison approach to perform the detection. Specifically, we build a library database consists of original library binaries with distinct versions. After extracting profiles for each library version and the target app, we conduct a similarity comparison to find the best matches. We implemented this approach in iLibScope. We also built a benchmark consists of 5,807 apps with 10,495 library integrations and applied our tool to it. Our evaluation shows that iLibScope achieves a recall exceeds 99% and a precision exceeds 97%. We also applied iLibScope to detect the presence of well-known vulnerable third-party libraries in real-world iOS mobile apps to show the promising usage of our tool. It successfully identified 405 vulnerable library usage from 4 , 249 apps. and (developed by have occupied over
Static Deadlock Detection in Low-Level C Code

We present a novel scalable deadlock analyser L2D2 capable of handling C code with low-level unstructured lock manipulation. L2D2 runs along the call tree of a program, starting from its leaves, and analyses each function just once, without any knowledge of the call context. L2D2 builds function summaries recording information about locks that are assumed or known to be locked or unlocked at the entry, inside, and at the exit of functions, together with lock dependencies, and reports warnings about possible deadlocks when cycles in the lock dependencies are detected. We implemented L2D2 as a plugin of the Facebook/Meta Infer framework and report results of experiments on a large body of C as well as C++ code illustrating the effectiveness and efficiency of L2D2.
Hamiltonians of Bipartite Walks

In this paper, we introduce a discrete quantum walk model called bipartite walks. Bipartite walks include many known discrete quantum walk models, like arc-reversal walks, vertex-face walks. For the transition matrix of a quantum walk, there is a Hamiltonian associated with it. We will study the Hamiltonians of the bipartite walks. Let S be a skew-symmetric matrix. We are mainly interested in the Hamiltonians of the form iS . We show that the Hamiltonian can be written as iS if and only if the adjacency matrix of the bipartite graph is invertible. We show that arc-reversal walks and vertex-face walks are special cases of bipartite walks. Via the Hamiltonians, phenomena of bipartite walks lead to phenomena of continuous walks. We show in detail how we use bipartite walks on paths to construct universal perfect state transfer in continuous walks.
Clustered Saliency Prediction

We present a new method for image salience prediction, Clustered Saliency Prediction. This method divides individuals into clusters based on their personal features and their known saliency maps, and generates a separate image salience model for each cluster. We test our approach on a public dataset of personalized saliency maps, with varying importance weights for personal feature factors and observe the effects on the clusters. For each cluster, we use an image-to-image translation method, mainly Pix2Pix model, to convert universal saliency maps to saliency maps of that cluster. We try three state-of-the-art universal saliency prediction methods, DeepGaze II, ML-Net and SalGAN, and see their impact on the results. We show that our Clustered Saliency Prediction technique outperforms the state-of-the-art universal saliency prediction models. Also we demonstrate the effectiveness of our clustering method by comparing the results of Clustered Saliency Prediction using clusters obtained by Subject Similarity Clustering algorithm with two baseline methods. We propose an approach to assign new people to the most appropriate cluster, based on their personal features and any known saliency maps. In our experiments we see that this method of assigning new people to a cluster on average chooses the cluster that gives higher saliency scores.