Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifetime Maximization for UAV-Enabled Cognitive-NOMA IoT Networks: Joint Location, Power, and Decoding Order Optimization

This paper investigates a cognitive unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) network, where secondary/cognitive IoT devices upload their data to the UAV hub following a non-orthogonal multiple access (NOMA) protocol in the spectrum of the primary network. We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, transmit power, and decoding order subject to interference-power constraints in presence of the imperfect channel state information (CSI). To solve the formulated non-convex mixed-integer programming problem, we first jointly optimize the UAV location and transmit power for a given decoding order and obtain the globally optimal solution with the assistance of Lagrange duality and then obtain the best decoding order by exhaustive search, which is applicable to relatively small-scale scenarios. For large-scale scenarios, we propose a low-complexity sub-optimal algorithm by transforming the original problem into a more tractable equivalent form and applying the successive convex approximation (SCA) technique and penalty function method. Numerical results demonstrate that the proposed design significantly outperforms the benchmark schemes.
Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ Trajectories

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. The government highly relies on active human efforts to detect illegal parking events. However, such an approach is extremely ineffective to cover a large city since the police have to patrol over the entire city roads. The massive and high-quality sharing bike trajectories from Mobike offer us a unique opportunity to design a ubiquitous illegal parking detection approach, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. The detection result can guide the patrol schedule, i.e., send the patrol policemen to the region with higher illegal parking risks, and further improve the patrol efficiency. Inspired by this idea, three main components are employed in the proposed framework: 1) trajectory pre-processing, which filters outlier GPS points, performs map-matching, and builds trajectory indexes; 2) illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories, and utilizes a distribution test-based method to discover the illegal parking events; and 3) patrol scheduling, which leverages the detection result as reference context, and models the scheduling task as a multi-agent reinforcement learning problem to guide the patrol police. Finally, extensive experiments are presented to validate the effectiveness of illegal parking detection, as well as the improvement of patrol efficiency.
Statistical review of animal trials—A guideline

Any experiment involving living organisms requires justification of the need and moral defensibleness of the study. Statistical planning, design, and sample size calculation of the experiment are no less important review criteria than general medical and ethical points to consider. Errors made in the statistical planning and data evaluation phase can have severe consequences on both results and conclusions. They might proliferate and thus impact future trials—an unintended outcome of fundamental research with profound ethical consequences. Unified statistical standards are currently missing for animal review boards in Germany. In order to accompany, we developed a biometric form to be filled and handed in with the proposal at the concerned local authority on animal welfare. It addresses relevant points to consider for biostatistical planning of animal experiments and can help both the applicants and the reviewers in overseeing the entire experiment(s) planned. Furthermore, the form might also aid in meeting the current standards set by the 3+3R's principle of animal experimentation: Replacement, Reduction, Refinement as well as Robustness, Registration, and Reporting. The form has already been in use by the concerned local authority of animal welfare in Berlin, Germany. In addition, we provide reference to our user guide giving more detailed explanation and examples for each section of the biometric form. Unifying the set of biostatistical aspects will help both the applicants and the reviewers to equal standards and increase quality of preclinical research projects, also for translational, multicenter, or international studies.
Energetic pickup proton population downstream of the termination shock as revealed by the IBEX-Hi data

The pickup protons originate as a result of the ionization of hydrogen atoms in the supersonic solar wind, forming the suprathermal component of protons in the heliosphere. While picked by the heliospheric magnetic field and convected into the heliosheath, the pickup protons may suffer the stochastic acceleration by the solar wind turbulence in the region from the Sun up to the heliospheric termination shock, where they can also experience the shock-drift acceleration or the reflection from the cross-shock potential. These processes create a high-energy «tail» in the pickup ion energy distribution. The properties of this energetic pickup proton population are still not well-defined, despite they are vital for themodels to simulate energetic neutral atom fluxes. We have considered two scenarios for pickup proton velocity distribution downstream of the heliospheric termination shock (filled shell with energetic power-law «tail» and biMaxwellian). Based on the numerical kinetic model and observations of the energetic neutral atom fluxes from the inner heliosheath by the IBEX-Hi instrument, we have characterized the pickup proton distribution and provided estimations on the properties of the energetic pickup proton population downstream of the termination shock.
Metal Mixing in Minihalos: The Descendants of Pair-instability Supernovae

The lack of observations of abundance patterns originating in pair-instability supernovae has been a long-standing problem in relation to the first stars. This class of supernovae is expected to have an abundance pattern with a strong odd–even effect, making it substantially different from present-day supernovae. In this study, we use a cosmological radiation hydrodynamics simulation to model such supernovae and the subsequent formation of the second generation of stars. We incorporate streaming velocities for the first time. There are 14 star-forming minihalos in our 1 cMpc h −1 box, leading to 14 supernovae occurring before redshift z = 19.5, where we start reducing the complexity of the simulation. Following the explosions, extremely metal-poor stars form in 10 halos via internal and external enrichment, which makes it the most common outcome. Only one halo does not recollapse during the simulations. This result is at variance with the current (lack of) observations of metal-poor stars with pair-instability supernova abundance patterns, suggesting that these very massive stars might be rare even in the early universe. The results from this simulation also give us insights into what drives different modes of recollapse and what determines the mixing behavior of metals after very energetic supernovae.
SDP: Scalable Real-Time Dynamic Graph Partitioner

The time-evolving large graph has received attention due to it’s participation in real-world applications such as social networks and PageRank calculation. It is necessary to partition a large-scale dynamic graph in a streaming manner in order to overcome the memory bottleneck while partitioning the computational load. Reducing network communication and balancing the load between the partitions are the criteria for achieving effective run-time performance in graph partitioning. Moreover, an optimal resource allocation is needed to utilise the resources while storing the graph streams into the partitions. A number of existing partitioning algorithms have been proposed to address the above problem. However, these partitioning methods are incapable of scaling the resources and handling the stream of data in real-time. In this study, we propose a dynamic graph partitioning method called Scalable Dynamic Graph Partitioner(SDP) using the streaming partitioning technique. The SDP contributes a novel vertex assigning method, communication-aware balancing method, and a scaling technique in order to produce an efficient dynamic graph partitioner. Experiment results show that the proposed method achieves up to 90% reduction of communication cost and 60%-70% balancing the load dynamically, compared with previous algorithms. Moreover, the proposed algorithm significantly reduces the execution time during partitioning.
Estimating and Maximizing Mutual Information for Knowledge Distillation

In this work, we propose Mutual Information Maximization Knowledge Distillation (MIMKD). Our method uses a contrastive objective to simultaneously estimate and maximize a lower bound on the mutual information of local and global feature representations between a teacher and a student network. We demonstrate through extensive experiments that this can be used to improve the performance of low capacity models by transferring knowledge from more performant but computationally expensive models. This can be used to produce better models that can be run on devices with low computational resources. Our method is flexible, we can distill knowledge from teachers with arbitrary network architectures to arbitrary student networks. Our empirical results show that MIMKD outperforms competing approaches across a wide range of student-teacher pairs with different capacities, with different architectures, and when student networks are with extremely low capacity. We are able to obtain 74.55% accuracy on CIFAR100 with a ShufflenetV2 from a baseline accuracy of 69.8% by distilling knowledge from ResNet-50. On Imagenet we improve a ResNet-18 network from 68.88% to 70.32% accuracy (1.44%+) using a ResNet-34 teacher network.
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

The advancement of generative radiance fields has pushed the boundary of 3D-aware image synthesis. Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint as regularization to learn valid 3D radiance fields from 2D images. Despite the progress, they often fall short of capturing accurate 3D shapes due to the shape-color ambiguity, limiting their applicability in downstream tasks. In this work, we address this ambiguity by proposing a novel shading-guided generative implicit model that is able to learn a starkly improved shape representation. Our key insight is that an accurate 3D shape should also yield a realistic rendering under different lighting conditions. This multi-lighting constraint is realized by modeling illumination explicitly and performing shading with various lighting conditions. Gradients are derived by feeding the synthesized images to a discriminator. To compensate for the additional computational burden of calculating surface normals, we further devise an efficient volume rendering strategy via surface tracking, reducing the training and inference time by 24% and 48%, respectively. Our experiments on multiple datasets show that the proposed approach achieves photorealistic 3D-aware image synthesis while capturing accurate underlying 3D shapes. We demonstrate improved performance of our approach on 3D shape reconstruction against existing methods, and show its applicability on image relighting. Our code will be released at https://github.com/XingangPan/ShadeGAN.
Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text

Natural Language Processing (NLP) is a branch of machine learning that gives the machines the ability to decode human languages. Part-of-speech tagging (POS tagging) is a preprocessing task that requires an annotated corpora. Rule-based and stochastic methods showed great results for POS tag prediction. On this work, I performed a mathematical model based on Hidden Markov structures and I obtained a high level accuracy of ingredients extracted from text recipe which is a performance greater than what traditional methods could make without unknown words consideration.
Novel View Synthesis from a Single Image via Unsupervised learning

View synthesis aims to generate novel views from one or more given source views. Although existing methods have achieved promising performance, they usually require paired views of different poses to learn a pixel transformation. This paper proposes an unsupervised network to learn such a pixel transformation from a single source viewpoint. In particular, the network consists of a token transformation module (TTM) that facilities the transformation of the features extracted from a source viewpoint image into an intrinsic representation with respect to a pre-defined reference pose and a view generation module (VGM) that synthesizes an arbitrary view from the representation. The learned transformation allows us to synthesize a novel view from any single source viewpoint image of unknown pose. Experiments on the widely used view synthesis datasets have demonstrated that the proposed network is able to produce comparable results to the state-of-the-art methods despite the fact that learning is unsupervised and only a single source viewpoint image is required for generating a novel view. The code will be available soon.
Assessing present and future risk of water damage using building attributes, meteorology and topography

Weather-related risk makes the insurance industry inevitably concerned with climate and climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise to compensations for the induced physical damages and business interruptions. In this work, we establish a nationwide, building-specific risk score for water damage associated with pluvial flooding in Norway. We fit a generalized additive model that relates the number of water damages to a wide range of explanatory variables that can be categorized into building attributes, climatological variables and topographical characteristics. The model assigns a risk score to every location in Norway, based on local topography and climate, which is not only useful for insurance companies, but also for city planning. Combining our model with an ensemble of climate projections allows us to project the (spatially varying) impacts of climate change on the risk of pluvial flooding towards the middle and end of the 21st century.
Spinons and damped phonons in spin-1/2 quantum-liquid Ba$_{4}$Ir${}_3$O${}_{10}$ observed by Raman scattering

In spin-1/2 Mott insulators, non-magnetic quantum liquid phases are often argued to arise when the system shows no magnetic ordering, but identifying positive signatures of these phases or related spinon quasiparticles can be elusive. Here we use Raman scattering to provide three signatures for spinons in a possible spin-orbit quantum liquid material Ba 4 Ir 3 O 10 : (1) A broad hump, which we show can arise from Luttinger Liquid spinons in Raman with parallel photon polarizations normal to 1D chains; (2) Strong phonon damping from phonon-spin coupling via the spin-orbit interaction; and (3) the absence of (1) and (2) in the magnetically ordered phase that is produced when 2% of Ba is substituted by Sr ((Ba 0 . 98 Sr 0 . 02 ) 4 Ir 3 O 10 ). The phonon damping via itinerant spinons seen in this quantum-liquid insulator suggests a new mechanism for enhancing thermoelectricity in strongly correlated conductors, through a neutral quantum liquid that need not aﬀect electronic transport.
Visual Keyword Spotting with Attention

In this paper, we consider the task of spotting spoken keywords in silent video sequences -- also known as visual keyword spotting. To this end, we investigate Transformer-based models that ingest two streams, a visual encoding of the video and a phonetic encoding of the keyword, and output the temporal location of the keyword if present. Our contributions are as follows: (1) We propose a novel architecture, the Transpotter, that uses full cross-modal attention between the visual and phonetic streams; (2) We show through extensive evaluations that our model outperforms the prior state-of-the-art visual keyword spotting and lip reading methods on the challenging LRW, LRS2, LRS3 datasets by a large margin; (3) We demonstrate the ability of our model to spot words under the extreme conditions of isolated mouthings in sign language videos.
How to leverage the multimodal EHR data for better medical prediction?

Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of service and reduce costs. However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. The results on two prediction tasks show that our fused model with different data outperforms the state-of-the-art method without clinical notes, which illustrates the importance of our fusion method and the clinical note features.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings

The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer. Recent approaches attempt to introduce the knowledge graph embedding (KGE) technique to handle the KG incompleteness but only consider the triple facts and neglect the significant semantic correlation between paths and multi-relational questions. In this paper, we propose a Path and Knowledge Embedding-Enhanced multi-relational Question Answering model (PKEEQA), which leverages multi-hop paths between entities in the KG to evaluate the ambipolar correlation between a path embedding and a multi-relational question embedding via a customizable path representation mechanism, benefiting for achieving more accurate answers from the perspective of both the triple facts and the extra paths. Experimental results illustrate that PKEEQA improves KBQA models' performance for multi-relational question answering with explainability to some extent derived from paths.
Unsupervised Person Re-Identification With Wireless Positioning Under Weak Scene Labeling

Existing unsupervised person re-identification methods only rely on visual clues to match pedestrians under different cameras. Since visual data is essentially susceptible to occlusion, blur, clothing changes, etc., a promising solution is to introduce heterogeneous data to make up for the defect of visual data. Some works based on full-scene labeling introduce wireless positioning to assist cross-domain person re-identification, but their GPS labeling of entire monitoring scenes is laborious. To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras. Specifically, we propose a novel unsupervised multimodal training framework (UMTF), which models the complementarity of visual data and wireless information. Our UMTF contains a multimodal data association strategy (MMDA) and a multimodal graph neural network (MMGN). MMDA explores potential data associations in unlabeled multimodal data, while MMGN propagates multimodal messages in the video graph based on the adjacency matrix learned from histogram statistics of wireless data. Thanks to the robustness of the wireless data to visual noise and the collaboration of various modules, UMTF is capable of learning a model free of the human label on data. Extensive experimental results conducted on two challenging datasets, i.e., WP-ReID and Campus4K demonstrate the effectiveness of the proposed method.
The Structure-Behavior Coalescence Method -Toward a Unified View of the Software System in Model-Driven Engineering

—In Model-Driven Engineering (MDE), the Unified Modeling Language (UML) 2.0 specification includes a metamodel that defines the language concepts and a user model that defines how the language concepts are represented. In UML 2.0, an important use of metamodel is to provide an integrated semantic framework that every diagram in the user model can be projected as a view of the metamodel. However, most existing UML 2.0 metamodels lack the ability to serve as the basis for unifying different views of a software system. To overcome the shortcomings of the current UML 2.0 metamodel approaches, we developed Operation-Based Multi-Queue Structure-Behavior Coalescence Process Algebra (O-M-SBC-PA), which provides an integrated semantic framework that is able to integrate structural and behavioral constructs. Using O-M-SBC-PA as the metamodel of UML 2.0, each diagram in the user model can be projected as a view of the MDE metamodel.
Scale invariance and dilaton mass

We consider a generic scale invariant scalar quantum ﬁeld theory and its symmetry breakdown. Based on the dimension counting identity, we give a concise proof that dilaton is exactly massless at the classical level if scale invariance is broken spontaneously. On the other hand, on the basis of the generalized dimension counting identity, we prove that the dilaton becomes massive at the quantum level if scale invariance is explicitly broken by quantum anomaly. It is pointed out that a subtlety occurs when scale invariance is spontaneously broken through a scale invariant regularization method where the renormalization scale is replaced with the dilaton ﬁeld. In this case, the dilaton remains massless even at the quantum level after spontaneous symmetry breakdown of scale symmetry, but when the massless dilaton couples non-minimally to the Einstein-Hilbert term and is applied for cosmology, it is phenomenologically ruled out by solar system tests unless its coupling to matters is much suppressed compared to the gravitational interaction.