Miracle-dz/UniFAR-FTUs huggingface dataset:
FaSS-MVS - Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery

With FaSS-MVS, we present an approach for fast multi-view stereo with surface-aware Semi-Global Matching that allows for rapid depth and normal map estimation from monocular aerial video data captured by UAVs. The data estimated by FaSS-MVS, in turn, facilitates online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated while the image data is acquired or being received. FaSS-MVS is comprised of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing to efficiently process large scene depths which are inherent to oblique imagery captured by low-flying UAVs. The actual depth estimation employs a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of SGM. Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and calculating the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study we show that the accuracies of the 3D information calculated by FaSS-MVS is close to that of state-of-the∗Corresponding author Email addresses: boitumelo.ruf@kit.edu, boitumelo.ruf@iosb.fraunhofer. de (Boitumelo Ruf), martin.weinmann@kit.edu (Martin Weinmann), stefan.hinz@kit.edu (Stefan Hinz) Preprint submitted to Elsevier December 3, 2021 ar X iv :2 11 2. 00 82 1v 1 [ cs . C V ] 1 D ec 2 02 1 art approaches for offline multi-view stereo, with the error not even being one magnitude higher than that of COLMAP. At the same time, however, the average run-time of FaSS-MVS to estimate a single depth and normal map is less than 14 % of that of COLMAP, allowing to perform an online and incremental processing of Full-HD imagery at 1-2 Hz.
NeuroHammer: Inducing Bit-Flips in Memristive Crossbar Memories

Emerging non-volatile memory (NVM) technologies offer unique advantages in energy efficiency, latency, and features such as computing-in-memory. Consequently, emerging NVM technologies are considered an ideal substrate for computation and storage in future-generation neuromorphic platforms. These technologies need to be evaluated for fundamental reliability and security issues. In this paper, we present NeuroHammer, a security threat in ReRAM crossbars caused by thermal crosstalk between memory cells. We demonstrate that bit-flips can be deliberately induced in ReRAM devices in a crossbar by systematically writing adjacent memory cells. A simulation flow is developed to evaluate NeuroHammer and the impact of physical parameters on the effectiveness of the attack. Finally, we discuss the security implications in the context of possible attack scenarios.
Modelling Approaches of Power Systems Considering Grid-Connected Converters and Renewable Generation Dynamics

This paper presents a comparative analysis of several modelling approaches of key elements used in simulations of power systems with renewable energy sources. Different models of synchronous generators, transmission lines, converters, wind generators and PV power plants are compared to assess the most suitable models for grid-connection studies. It also analyses how the dynamics of PV power plants and the mechanical dynamics of wind generators affect the electrical variables on the grid side. The models were compared in terms of precision, computational time and ease of use through simulations of load connection, short-circuits, disconnection of generators and lines in a benchmark system modelled in Simulink.
Evaluation of mathematical questioning strategies using data collected through weak supervision

A large body of research demonstrates how teachers' questioning strategies can improve student learning outcomes. However, developing new scenarios is challenging because of the lack of training data for a specific scenario and the costs associated with labeling. This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills. Using a human-in-the-loop approach, we collected a high-quality training dataset for a mathematical questioning scenario. Using recent advances in uncertainty quantification, we evaluated our conversational agent for usability and analyzed the practicality of incorporating a human-in-the-loop approach for data collection and system evaluation for a mathematical questioning scenario.
A New Approach to Detect Important Members that Create the Communities in Bipartite Networks

The world around us consists of objects that have different relationships with each other. The result of these communications is various networks, part of which are bipartite networks. While many studies have investigated essential network members, less attention has been paid to the bipartite graphs. On the other hand, one of the most critical aspects of network analysis is the detection and extraction of communities that arise in the structure of networks. For these reasons, we have introduced a measure called H.H to identify influential nodes in community formation in the one-mode projection of a bipartite graph. The three main parameters that influence this measure are the size of the formed community, the effect of each node in the formation of that community, and the number of communities in which the node had an impact. The results of this paper show the differences of this measure with other centralities (eigenvector centrality, closeness centrality, betweenness centrality, and degree centrality) and how this measure takes into account aspects that other centralities do not. Through H.H score, we can find essential nodes that have been effective in forming a community, and by removing these nodes, communities can be eliminated. Any of the existing centralities has not addressed this issue, and this measure has sufficient independence to represent the important nodes in the formation of the communities. Experimental validation of the proposed measure is carried out on two real-world datasets: Southern Women Network and Person-Crime Network. The results of the implementation of the H.H score on the Person-Crime dataset show that by eliminating the nodes with the highest H.H score (top-10%), 29% of the communities have changed; this is while the centralities change the average of 18% of the communities and this shows the importance of the H.H score.
Design of compact patch antenna based on metamaterial for WiMAX applications with circular polarization

In this article, compact antenna with high gain and circular polarization (CP) for WiMAX applications is noticed. Using metamaterials with negative permittivity and permeability improves the antenna parameters; thus, the prototype antenna is designed based on metamaterial composite right‐/left‐handed to achieve CP and compact size. The final antenna has directional pattern and high gain with CP at 3450 MHz. The antenna gain is 8.28 dBi. The size of prototype patch antenna is 30 × 30 mm2. It is designed and fabricated on Taconic TLY‐5, Laminate substrate with εr = 2.2 and thickness of 1.6 mm. It is simulated by CST microwave studio and HFSS. Also, equivalent circuit and experimental results are presented and compared here. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:357–360, 2015
Substructure detection reanalyzed: dark perturber shown to be a line-of-sight halo

Observations of structure at sub-galactic scales are crucial for probing the properties of dark matter, which is the dominant source of gravity in the universe. It will become increasingly important for future surveys to distinguish between line-of-sight haloes and subhalos to avoid wrong inferences on the nature of dark matter. We reanalyze a sub-galactic structure (in lens JVAS B1938+666) that has been previously found using the gravitational imaging technique in galaxy-galaxy lensing systems. This structure has been assumed to be a satellite in the halo of the main lens galaxy. We fit the redshift of the perturber of the system as a free parameter, using the multi-plane thin-lens approximation, and find that the redshift of the perturber is $z_\mathrm{int} = 1.42^{+0.10}_{-0.15}$ (with a main lens redshift of z = 0.881). Our analysis indicates that this structure is more massive than the previous result by an order of magnitude. This constitutes the first dark perturber shown to be a line-of-sight halo with a gravitational lensing method.
Maximum Entropy Model-based Reinforcement Learning

Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical and real-world tasks is currently limited due to most RL state-of-art algorithms' sample inefficiency, i.e., the need for a vast number of training episodes. For example, OpenAI Five algorithm that has beaten human players in Dota 2 has trained for thousands of years of game time. Several approaches exist that tackle the issue of sample inefficiency, that either offers a more efficient usage of already gathered experience or aim to gain a more relevant and diverse experience via a better exploration of an environment. However, to our knowledge, no such approach exists for model-based algorithms, that showed their high sample efficiency in solving hard control tasks with high-dimensional state space. This work connects exploration techniques and model-based reinforcement learning. We have designed a novel exploration method that takes into account features of the model-based approach. We also demonstrate through experiments that our method significantly improves the performance of the model-based algorithm Dreamer.
Association of painful human immunodeficiency virus distal sensory polyneuropathy with aberrant expectation of pain relief: functional magnetic resonance imaging evidence

Abstract Mechanisms underlying chronic neuropathic pain associated with HIV-associated distal sensory polyneuropathy are poorly understood, yet 40% of those with distal neuropathy (or 20% of all people with HIV) suffer from this debilitating condition. Central pain processing mechanisms are thought to contribute to the development of HIV neuropathic pain, yet studies investigating central mechanisms for HIV neuropathic pain are few. Considering the motivational nature of pain, we aimed to examine the degree to which expectation of pain onset and expectation of pain offset are altered in sixty-one male patients with HIV-related distal sensory polyneuropathy with (N = 30) and without (N = 31) chronic neuropathic pain. By contrasting painful (foot) and non-painful (hand) sites between those with and without neuropathic pain, we could identify unique neural structures that showed altered activation during expectation of pain offset or relief. Our results showed no evidence for peripheral mechanisms evidenced by lack of significant between group differences in thermo-sensation, subjective pain response or epidermal nerve fibre density. Likewise, we found no significant differences between groups in subjective or brain mechanisms underlying the expectation of pain onset. Conversely, we found significant interaction within right anterior insula during expectation of pain offset in our study in that individuals in the pain group compared to the no-pain group exhibited increased anterior insula activation on the painful compared to the non-painful site. Our findings are consistent with abnormal processing of expectation of pain offset or abnormal pain relief-related mechanisms potentially due to increased emotional distress regarding the experience of chronic endogenous pain.
Amplifier development for multiplexed cryogenic detectors

We make some considerations on the question of driving the cable from the cryogenic stage of refrigerators to the room temperature, in the case of multiplexed detector array systems where a high total Shannon information capacity is required. We have constructed large SQUID arrays for the purpose, some of which exhibit lower than 5 × 10−8 Φ0 Hz−1/2 flux noise at 4.2 K and do not require magnetic shielding in a typical laboratory environment. The option of using class-D amplifiers to reduce the cryogenic heat load is briefly reviewed.
Evaluation and analysis of transformerless photovoltaic inverter topology for efficiency improvement and reduction of leakage current

In transfomerless photovoltaic (PV) grid-connected inverter application, to reduce leakage current and to increase efficiency, many inverter topologies have been proposed. The method for increasing efficiency and reducing leakage current is evaluated and analysed in the present study. The operation of transfomerless PV inverter topologies with high-performance such as full-bridge, H5, H6, HERIC and paralleled-buck topology is analysed to calculate switching losses, conduction losses and free-wheeling losses. Device total losses for inverter topology are calculated according to the switching frequency and the output power. Also, a novel high-frequency model of inverter topology is proposed using the leg voltage and high-frequency equivalent circuit. The relation between the parasitic capacitor voltage and its leakage current is explained using the proposed high-frequency model. Also, the magnitude of the high-frequency voltage in the parasitic capacitor is derived mathematically. Efficiency of the several inverter topology according to the output power is compared and the leakage current is analysed using a novel high-frequency model. Finally, the efficiency and leakage current analysis are verified by simulation tool and 3 kW prototype.
DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels

In edge-cloud collaborative intelligence (CI), an unreliable transmission channel exists in the information path of the AI model performing the inference. It is important to be able to simulate the performance of the CI system across an imperfect channel in order to understand system behavior and develop appropriate error control strategies. In this paper we present a simulation framework called DFTS2, which enables researchers to define the components of the CI system in TensorFlow~2, select a packet-based channel model with various parameters, and simulate system behavior under various channel conditions and error/loss control strategies. Using DFTS2, we also present the most comprehensive study to date of the packet loss concealment methods for collaborative image classification models.
Coproduction in the management of individuals with cleft lip and palate in South Africa: the Ekhaya Lethu model

Abstract Objective Cleft lip and palate (CLP), one of the most common congenital anomalies of the craniofacial complex, has a worldwide prevalence rate of 1 in 700 live births. In South Africa, a middle-income country, the CLP prevalence rate is 0.3 per 1000 live births in the public health sector. The complexity of the condition requires that individuals with CLP be treated by a multi-disciplinary team of health professionals, with the integral involvement of caregivers and families. Methods Between 2015 and 2018, we conducted a cross-sectional study entitled: The epidemiology and care of individuals with cleft lip and palate in South Africa, in fulfilment of a Doctor of Philosophy degree. The study setting consisted of 11 specialized academic centres (nine central hospitals and two specialized dental hospitals) that are situated in six of South Africa’s nine provinces. The study used a combination of quantitative and qualitative methods and consisted of four distinct but inter-linked components. The first component consisted of a record review of CLP data over a 2-year period to determine the prevalence of CLP in the public sector of South Africa. The second component consisted of a survey of the leaders or heads of the health care teams in the 11 specialized centres to determine the current approach to CLP care provision. The third component consisted of a survey among CLP team members to measure inter-professional collaboration. The fourth component consisted of interviews with parents or caregivers on their perceptions of health service provision and support for children with CLP. We draw on the findings of this large empirical study on CLP in South Africa’s public health sector and the theory and principles of health care service coproduction to present the Ekhaya Lethu model for the management of CLP. Results The conceptual design of Ekhaya Lethu derives from the findings of each of the study components. We describe the possible application of the model in the coproduction of health care to examine the roles, relationships and aims of the multidisciplinary team in CLP management. We highlight both the implications and challenges of coproduction in the care and management of CLP for multidisciplinary health teams, the caregivers and families of individuals with CLP, and for health managers and policy makers. Conclusion The proposed Ekhaya Lethu model introduces a discourse on coproduction in the design and implementation of quality health care to individuals with CLP in South Africa and other low-and middle-income countries.
Active Learning for Domain Adaptation: An Energy-based Approach

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit free energy biases when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https://github.com/BIT-DA/EADA.
EngineKGI: Closed-Loop Knowledge Graph Inference

Knowledge Graph (KG) inference is the vital technique to address the natural incompleteness of KGs. The existing KG inference approaches can be classified into rule learningbased and KG embedding-based models. However, these approaches cannot well balance accuracy, generalization, interpretability and efficiency, simultaneously. Besides, these models always rely on pure triples and neglect additional information. Therefore, both KG embedding (KGE) and rule learning KG inference approaches face challenges due to the sparse entities and the limited semantics. We propose a novel and effective closed-loop KG inference framework EngineKGI operating similarly as an engine based on these observations. EngineKGI combines KGE and rule learning to complement each other in a closed-loop pattern while taking advantage of semantics in paths and concepts. KGE module exploits paths to enhance the semantic association between entities and introduces rules for interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms other baselines on link prediction tasks, demonstrating the effectiveness and superiority of our model on KG inference in a joint logic and data-driven fashion with a closed-loop mechanism.
A higher order Minkowski loss for improved prediction ability of acoustic model in ASR

Conventional automatic speech recognition (ASR) system uses second-order minkowski loss during inference time which is suboptimal as it incorporates only first order statistics in posterior estimation [2]. In this paper we have proposed higher order minkowski loss (4th Order and 6th Order) during inference time, without any changes during training time. The main contribution of the paper is to show that higher order loss uses higher order statistics in posterior estimation, which improves the prediction ability of acoustic model in ASR system. We have shown mathematically that posterior probability obtained due to higher order loss is function of second order posterior and thus the method can be incorporated in standard ASR system in an easy manner. It is to be noted that all changes are proposed during test(inference) time, we do not make any change in any training pipeline. Multiple baseline systems namely, TDNN1, TDNN2, DNN and LSTM are developed to verify the improvement incurred due to higher order minkowski loss. All experiments are conducted on LibriSpeech dataset and performance metrics are word error rate (WER) on"dev-clean","test-clean","dev-other"and"test-other"datasets.
Learning Large-scale Network Embedding from Representative Subgraph

We study the problem of large-scale network embedding, which aims to learn low-dimensional latent representations for network mining applications. Recent research in the field of network embedding has led to significant progress such as DeepWalk, LINE, NetMF, NetSMF. However, the huge size of many real-world networks makes it computationally expensive to learn network embedding from the entire network. In this work, we present a novel network embedding method called"NES", which learns network embedding from a small representative subgraph. NES leverages theories from graph sampling to efficiently construct representative subgraph with smaller size which can be used to make inferences about the full network, enabling significantly improved efficiency in embedding learning. Then, NES computes the network embedding from this representative subgraph, efficiently. Compared with well-known methods, extensive experiments on networks of various scales and types demonstrate that NES achieves comparable performance and significant efficiency superiority.