Miracle-dz/UniFAR-FTUs huggingface dataset:
Collision-free Formation Control of Multiple Nano-quadrotors

The utilisation of unmanned aerial vehicles has witnessed significant growth in real-world applications including surveillance tasks, military missions, and transportation deliveries. This letter investigates practical problems of formation control for multiple nano-quadrotor systems. To be more specific, the first aim of this work is to develop a theoretical framework for the time-varying formation flight of the multi-quadrotor system regarding anti-collisions. In order to achieve this goal, the finite cut-off potential function is devoted to avoiding collisions among vehicles in the group as well as between vehicles and an obstacle. The control algorithm navigates the group of nano-quadrotors to asymptotically reach an anticipated time-varying formation. The second aim is to implement the proposed algorithm on Crazyflies nanoquadrotors, one of the most ubiquitous indoor experimentation platforms. Several practical scenarios are conducted to tendentiously expose anti-collision abilities among group members as well as between vehicles and an obstacle. The experimental outcomes validate the effectiveness of the proposed method in the formation tracking and the collision avoidance of multiple nano-quadrotors.
Universal scaling of shear thickening transitions

Nearly, all dense suspensions undergo dramatic and abrupt thickening transitions in their flow behavior when sheared at high stresses. Such transitions occur when the dominant interactions between the suspended particles shift from hydrodynamic to frictional. Here, we interpret abrupt shear thickening as a precursor to a rigidity transition and give a complete theory of the viscosity in terms of a universal crossover scaling function from the frictionless jamming point to a rigidity transition associated with friction, anisotropy, and shear. Strikingly, we find experimentally that for two different systems—cornstarch in glycerol and silica spheres in glycerol—the viscosity can be collapsed onto a single universal curve over a wide range of stresses and volume fractions. The collapse reveals two separate scaling regimes due to a crossover between frictionless isotropic jamming and frictional shear jamming, with different critical exponents. The material-specific behavior due to the microscale particle interactions is incorporated into a scaling variable governing the proximity to shear jamming, that depends on both stress and volume fraction. This reformulation opens the door to importing the vast theoretical machinery developed to understand equilibrium critical phenomena to elucidate fundamental physical aspects of the shear thickening transition.
SPOTS: An Accelerator for Sparse CNNs Leveraging General Matrix-Matrix Multiplication

—This paper proposes a new hardware accelerator for sparse convolutional neural networks (CNNs) by building a hardware unit to perform the Image to Column (I M 2C OL ) transformation of the input feature map coupled with a systolic array-based general matrix-matrix multiplication (GEMM) unit. Our design carefully overlaps the I M 2C OL transformation with the GEMM computation to maximize parallelism. We propose a novel design for the I M 2C OL unit that uses a set of distributed local memories connected by a ring network, which improves energy efﬁciency and latency by streaming the input feature map only once. We propose a tall systolic array for the GEMM unit while also providing the ability to organize it as multiple small GEMM units, which enables our design to handle a wide range of CNNs and their parameters. Further, our design improves performance by effectively mapping the sparse data to the hardware units by utilizing sparsity in both input feature maps and weights. Our prototype, SPOTS, is on average 1.74 × faster than Eyeriss. It is also 78 × , and 12 × more energy-efﬁcient when compared to CPU and GPU implementations, respectively.
A low-energy explosion yields the underluminous Type IIP SN 2020cxd

Context. We present our observations and analysis of SN 2020cxd, a low-luminosity (LL), long-lived Type IIP supernova (SN). This object is LC suggests that the progenitor of SN 2020cxd had a radius of R 0 = 1 . 3 × 10 13 cm, kinetic energy of E kin = 4 . 3 × 10 50 erg, and ejecta mass of M ej = 9 . 5 M (cid:12) . From the bolometric LC, we estimated the total radiated energy E rad = 1 . 52 × 10 48 erg. Using our late-time nebular spectra, we compared these results against SN II spectral synthesis models to constrain the progenitor zero-age main sequence mass and found that it is likely to be (cid:46) 15 M (cid:12) . Conclusions. SN 2020cxd is a LL Type IIP SN. The inferred progenitor parameters and the features observed in the nebular spectrum favour a low-energy, Ni-poor, iron CC SN from a low-mass ( ∼ 12 M (cid:12) ) red supergiant.
MWP-BERT: A Strong Baseline for Math Word Problems

Math word problem (MWP) solving is the task of transforming a sequence of natural language problem descriptions to executable math equations. An MWP solver not only needs to understand complex scenarios described in the problem texts, but also identify the key mathematical variables and associate text descriptions with math equation logic. Although recent sequence modeling MWP solvers have gained credits on the math-text contextual understanding, pre-trained language models (PLM) have not been explored for solving MWP, considering that PLM trained over free-form texts is limited in representing text references to mathematical logic. In this work, we introduce MWP-BERT to obtain pre-trained to-ken representations that capture the alignment between text description and mathematical logic. Additionally, we introduce a keyword-based prompt matching method to address the MWPs requiring common-sense knowledge. On a benchmark Math23K dataset and a new Ape210k dataset, we show that MWP-BERT outperforms the strongest baseline model by 5-10% improvement on accuracy.
Site percolation on pseudo‐random graphs

We consider vertex percolation on pseudo‐random d$$ d $$ ‐regular graphs. The previous study by the second author established the existence of phase transition from small components to a linear (in nd$$ \frac{n}{d} $$ ) sized component, at p=1d$$ p=\frac{1}{d} $$ . In the supercritical regime, our main result recovers the sharp asymptotic of the size of the largest component, and shows that all other components are typically much smaller. Furthermore, we consider other typical properties of the largest component such as the number of edges, existence of a long cycle and expansion. In the subcritical regime, we strengthen the upper bound on the likely component size.
Interacting Stellar EMRIs as Sources of Quasi-periodic Eruptions in Galactic Nuclei

A star that approaches a supermassive black hole (SMBH) on a circular extreme mass ratio inspiral (EMRI) can undergo Roche lobe overflow (RLOF), resulting in a phase of long-lived mass transfer onto the SMBH. If the interval separating consecutive EMRIs is less than the mass-transfer timescale driven by gravitational wave emission (typically ∼1–10 Myr), the semimajor axes of the two stars will approach each another on scales of ≲ hundreds to thousands of gravitational radii. Close flybys tidally strip gas from one or both RLOFing stars, briefly enhancing the mass-transfer rate onto the SMBH and giving rise to a flare of transient X-ray emission. If both stars reside in a common orbital plane, these close interactions will repeat on a timescale as short as hours, generating a periodic series of flares with properties (amplitudes, timescales, sources lifetimes) remarkably similar to the “quasi-periodic eruptions” (QPEs) recently observed from galactic nuclei hosting low-mass SMBHs. A cessation of QPE activity is predicted on a timescale of months to years, due to nodal precession of the EMRI orbits out of alignment by the SMBH spin. Channels for generating the requisite coplanar EMRIs include the tidal separation of binaries (Hills mechanism) or Type I inward migration through a gaseous AGN disk. Alternative stellar dynamical scenarios for QPEs, that invoke single stellar EMRIs on an eccentric orbit undergoing a runaway sequence of RLOF events, are strongly disfavored by formation rate constraints.
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning

In Federated Learning (FL), the clients learn a single global model (FedAvg) through a central aggregator. In this setting, the non-IID distribution of the data across clients restricts the global FL model from delivering good performance on the local data of each client. Personalized FL aims to address this problem by finding a personalized model for each client. Recent works widely report the average personalized model accuracy on a particular data split of a dataset to evaluate the effectiveness of their methods. However, considering the multitude of personalization approaches proposed, it is critical to study the per-user personalized accuracy and the accuracy improvements among users with an equitable notion of fairness. To address these issues, we present a set of performance and fairness metrics intending to assess the quality of personalized FL methods. We apply these metrics to four recently proposed personalized FL methods, PersFL, FedPer, pFedMe, and Per-FedAvg, on three different data splits of the CIFAR-10 dataset. Our evaluations show that the personalized model with the highest average accuracy across users may not necessarily be the fairest. Our code is available at https://tinyurl.com/1hp9ywfa for public use.
Rank-Based Verification for Long-Term Face Tracking in Crowded Scenes

Most current multi-object trackers focus on short-term tracking, and are based on deep and complex systems that often cannot operate in real-time, making them impractical for video-surveillance. In this paper we present a long-term, multi-face tracking architecture conceived for working in crowded contexts where faces are often the only visible part of a person. Our system benefits from advances in the fields of face detection and face recognition to achieve long-term tracking, and is particularly unconstrained to the motion and occlusions of people. It follows a tracking-by-detection approach, combining a fast short-term visual tracker with a novel online tracklet reconnection strategy grounded on rank-based face verification. The proposed rank-based constraint favours higher inter-class distance among tracklets, and reduces the propagation of errors due to wrong reconnections. Additionally, a correction module is included to correct past assignments with no extra computational cost. We present a series of experiments introducing novel specialized metrics for the evaluation of long-term tracking capabilities, and publicly release a video dataset with 10 manually annotated videos and a total length of 8’ 54”. Our findings validate the robustness of each of the proposed modules, and demonstrate that, in these challenging contexts, our approach yields up to 50% longer tracks than state-of-the-art deep learning trackers.
Third-order scale-independent WENO-Z scheme achieving optimal order at critical points

As we found previously, when critical points occur within grid intervals, the accuracy relations of smoothness indicators of WENO-JS would differ from that assuming critical points occurring on grid nodes, and accordingly the global smoothness indicator in WENO-Z scheme will differs from the original one. Based on above understandings, we first discuss several issues regarding current third-order WENO-Z improvements (e.g. WENO-NP3, -F3, -NN3, -PZ3 and – P+3), i.e. the numerical results with scale dependency, the validity of analysis assuming critical points occurring on nodes, and the sensitivity regarding computational time step and initial condition in order convergence studies. By analyses and numerical validations, the defections of present improvements are demonstrated, either scale-dependency of results or failure to recover optimal order when critical points occurring at half nodes, and then a generic analysis is provided which considers the first-order critical point occurring within grid intervals. Based on achieved theoretical outcomes, two scale-independent, third-order WENO-Z schemes are proposed which could truly recover the optimal order at critical points: the first one is acquired by limited expansion of grid stencil, deriving new global smoothness indicator and incorporating with the mapping function; the second one is achieved by further expanding grid stencil and employing a different global smoothness indicator. For validating, typical 1-D scalar advection problems, 1-D and 2-D problems by Euler equations are chosen and tested. The consequences verify the optimal order recovery at critical points by proposed schemes and show that: the first scheme outperforms aforementioned third-order WENO-Z improvements in terms of numerical resolution, while the second scheme indicates weak numerical robustness in spite of improved resolution and is mainly of theoretical significance.
Quasi-Newton methods for machine learning: forget the past, just sample

We present two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems that arise in machine learning. Contrary to the classical variants of these methods that sequentially build Hessian or inverse Hessian approximations as the optimization progresses, our proposed methods sample points randomly around the current iterate at every iteration to produce these approximations. As a result, the approximations constructed make use of more reliable (recent and local) information and do not depend on past iterate information that could be significantly stale. Our proposed algorithms are efficient in terms of accessed data points (epochs) and have enough concurrency to take advantage of parallel/distributed computing environments. We provide convergence guarantees for our proposed methods. Numerical tests on a toy classification problem as well as on popular benchmarking binary classification and neural network training tasks reveal that the methods outperform their classical variants.
On automatic extraction of on-street parking spaces using park-out events data

This article proposes two different approaches to automatically create a map for valid on-street car parking spaces. For this, we use car sharing park-out events data. The first one uses spatial aggregation and the second a machine learning algorithm. For the former, we chose rasterization and road sectioning; for the latter we chose decision trees. We compare the results of these approaches and discuss their advantages and disadvantages. Furthermore, we show our results for a neighborhood in the city of Berlin and report a classification accuracy of 91.6% on the original imbalanced data. Finally, we discuss further work; from gathering more data over a longer period of time to fitting spatial Gaussian densities to the data and the usage of apps for manual validation and annotation of parking spaces to improve ground truth data.
APE1/Ref-1 as a Novel Target for Retinal Diseases

APE1/Ref-1 (also called Ref-1) has been extensively studied for its role in DNA repair and reduction-oxidation (redox) signaling. The review titled: “The multifunctional APE1 DNA repair-redox signaling protein as a drug target in human disease” by Caston et. al. summarizes the molecular functions of Ref-1 and the role it plays in a number of diseases, with a specific focus on various types of cancer [1]. Previous studies have demonstrated that Ref-1 plays a critical role in regulating specific transcription factors (TFs) involved in a number of pathways, not only in cancer, but other disease indications as well. Disease indications of particular therapeutic interest include retinal vascular diseases such as diabetic retinopathy (DR), diabetic macular edema (DME), and neovascular age-related macular degeneration (nvAMD). While Ref-1 controls a number of TFs that are under redox regulation, three have been found to directly link cancer studies to retinal diseases; HIF-1α, NF-κB and STAT3. HIF-1α controls the expression of VEGF for angiogenesis while NF-κB and STAT3 regulate a number of known cytokines and factors involved in inflammation. These pathways are highly implicated and validated as major players in DR, DME and AMD. Therefore, findings in cancer studies for Ref-1 and its inhibition may be translated to these ocular diseases. This report discusses the path from cancer to the potential treatment of retinal disease, the Ref-1 redox signaling function as a possible target, and the current small molecules which have been identified to block this activity. One molecule, APX3330, is in clinical trials, while the others are in preclinical development. Inhibition of Ref-1 and its effects on inflammation and angiogenesis makes it a potential new therapeutic target for the treatment of retinal vascular diseases. This commentary summarizes the retinal-relevant research that built on the results summarized in the review by Caston et. al. [1].
Comparison of dexmedetomidine in two different doses on emergence agitation in children under sevoflurane anaesthesia: A double-blind randomised controlled trial

Background and Aims: Emergence agitation (EA) in children is one of the most common postoperative problems encountered in the recovery room. Sevoflurane has been strongly associated with EA owing to its lower solubility and rapid recovery. Dexmedetomidine has been found to reduce the incidence of EA. This study was designed to evaluate the effectiveness of dexmedetomidine in two doses in reducing EA in children. Methods: This was a prospective double-blinded randomised study done on eighty children aged 5–14 years undergoing adenotonsillectomy/tonsillectomy under sevoflurane anaesthesia. Patients in Group A (n = 40) received 0.3 μg/kg/h and patients in group B (n = 40) received 0.5 μg/kg/h infusion after a bolus dose of 0.5 μg/kg of dexmedetomidine. The primary objective was to compare two different doses of dexmedetomidine on EA in the postoperative period. The secondary objectives were to assess the pain and perioperative haemodynamics in the recovery room. The anaesthesiologist blinded to the study charted the paediatric anaesthesia emergence delirium score (PAED), perioperative haemodynamic parameters, objective pain score and rescue medications if given. The data were analysed using Student's unpaired t-test, Chi-square test, repeated measures Analysis of Variance (ANOVA) and Mann-Whitney U test wherever appropriate. Results: The incidence of EA was comparable between both groups (P = 0.960). The haemodynamic parameters (P > 0.05) and the objective pain score (P = 0.810) also did not show a statistically significant difference. Conclusion: A lower dose of dexmedetomidine (0.3 μg/kg/h) is equally effective as a higher dose (0.5 μg/kg/h) after a bolus dose of 0.5 μg/kg in decreasing EA.
Bioinformatics Analysis of KIF1A Expression and Gene Regulation Network in Ovarian Carcinoma

Background The study aims to analyze the expression levels of kinesin family member 1A (KIF1A) in ovarian cancer (OC) and explore its clinical significance in the development of OC and its potential regulatory network. Methods The Cancer Genome Atlas (TCGA) OC data was used to examine the expression differences between OC and normal tissue and explore the correlation with tumor stage. The relationship between KIF1A expression and prognosis was analyzed using Oncomine and Kaplan–Meier plotter tools. The co-expression network of KIF1A in TCGA OC was analyzed based on the application of cBioPortal, GO cluster, and KEGG analyses were performed based on the co-expression network. Immune-infiltration analysis were used to analyze the significant involvement of KIF1A in function. Results KIF1A was highly elevated in OC tissues and KIF1A expression was significantly correlated with the FIGO stage (P=0.015) and age (P=0.020). High KIF1A expression of OC predicted the poor prognosis including overall survival (OS) (HR: 1.27; 95% CI: 1.11–1.45; P=0.00046) and post-progression survival (PFS) (HR: 1.18; 95% CI: 1.03–1.35; P=0.015). GO and KEGG analysis showed KIF1A had a potential role in the biological process of ATP-dependent chromatin remodeling, transcription, DNA-templated cytolysis, positive regulation of T cell proliferation, positive regulation of transcription, DNA-templated via cell adhesion molecules (CAMs), primary immunodeficiency, oxidative phosphorylation, NF-kappa B signaling pathway, pathways in cancer and Wnt signaling pathway, and immune infiltrating cells. Conclusion KIF1A was highly expressed and correlated with poor survival and immune infiltration in OC, and it may be a prognostic biomarker in OC.
Louis Pasteur and Dom Pedro II engaged in rabies vaccine development

Summary Louis Pasteur is the renowned chemist and microbiologist of the 19th century involved in the development of the rabies vaccine. He worked with a researchers team in the laboratory, mainly Pierre Paul Emile Roux, and also physicians in the clinical practice approach and the defense of Pasteur’s anti-rabies technique in the Académie Nationale de Médecine, Alfred Vulpian being the most notable. Pasteur’s first studies on rabies are noted in his 1881 publication. But in 1885, he revealed that he had already immunized 50 dogs against rabies. Meanwhile, he was looking for human subjects. The most polemic of this search involves the second and last Emperor of Brazil, Dom Pedro II, who was a patron of the arts and sciences and followed and supported the work of the great scientist. During the reign of Dom Pedro II, the first Pasteur’s Institute was founded in Rio de Janeiro, nine months before the Parisian, which had the financial support of Dom Pedro. This article deals with the interaction between the two outstanding characters, especially in the development of prophylactic treatment against rabies, and with the utilitarian aspects of this vaccine researches development against individual autonomy.
Dynamic sampling of images from various categories for classification based incremental deep learning in fog computing

Incremental learning evolves deep neural network knowledge over time by learning continuously from new data instead of training a model just once with all data present before the training starts. However, in incremental learning, new samples are always streaming in whereby the model to be trained needs to continuously adapt to new samples. Images are considered to be high dimensional data and thus training deep neural networks on such data is very time-consuming. Fog computing is a paradigm that uses fog devices to carry out computation near data sources to reduce the computational load on the server. Fog computing allows democracy in deep learning by enabling intelligence at the fog devices, however, one of the main challenges is the high communication costs between fog devices and the centralized servers especially in incremental learning where data samples are continuously arriving and need to be transmitted to the server for training. While working with Convolutional Neural Networks (CNN), we demonstrate a novel data sampling algorithm that discards certain training images per class before training even starts which reduces the transmission cost from the fog device to the server and the model training time while maintaining model learning performance both for static and incremental learning. Results show that our proposed method can effectively perform data sampling regardless of the model architecture, dataset, and learning settings.