Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyphoderma moniliforme and H. nemorale (Basidiomycota) newly recorded from China

Hyphoderma moniliforme and H. nemorale, saprobically growing on wood, are recorded as new for mycobiota of China. Both species were collected in mountains at the altitudes of 1850‒3000 m, from Yunnan Province (southwestern China). Hyphoderma moniliforme is also a new record for Eurasia, and previously known only from South Africa. The collections of H. nemorale in this study represent the most eastern and the most southern localities for this species known for Eurasia. Both species bear moniliform cystidia. Bayesian inference of phylogeny based on ITS and partial 28S nuclear ribosomal DNA sequences indicated that H. moniliforme is united in one clade with H. litschaueri from North America. 28S-based phylogram demonstrated that Chinese H. nemorale belong to the same clade with the holotype of this species collected from Europe. Morphology descriptions and illustrations for these two species are provided.
The effect of behavioural interventions for sleep problems on secondary outcomes in children with ASD and their parents.

Sleep problems are highly prevalent in children with Autism Spectrum Disorder (ASD) and are one of the most common sources of concern for parents. A small number of studies have investigated the negative secondary effects associated with sleep problems. However, there has been limited research looking at the outcomes of successful behavioural sleep interventions on children’s daytime behaviour and parental well-being. The purpose of this study was to investigate whether improved sleep resulting from behaviourally based sleep interventions had any effect on children’s daytime behaviour and parental well-being. Twenty-four children participated, aged between 3 and 14 years including their parents. The design of this study was a single-case multiple baseline across participants with preand postintervention measures. In response to treatment, the study did not find consistent improvements in children’s daytime functioning or parental wellbeing across all participants, as measured by the CSHQ, GARS-3, CBCL, DASS-21, PSQI and RQI. However, the majority improved on these measures and some to a clinically significant degree. Behaviourally-based sleep interventions were found to be effective in improving marital satisfaction, parent sleep quality as well as levels of depression, anxiety and stress among parents and decreasing ASD severity and problem behaviours (e.g., externalising behaviour) in children. However, deteriorations were also evident, with some parent’s deteriorating to a clinically significant degree on these outcomes. The present findings add to the limited literature investigating preand postmeasures of secondary outcomes associated with sleep difficulties in the ASD population. Future research may address the use of objective and subjective measures as well as participant specific variables in response to treatment.
Sequential Segment-based Level Generation and Blending using Variational Autoencoders

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros. , Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.
Backdoor Learning: A Survey

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be maliciously changed if the hidden backdoor is activated by attacker-specified triggers. This threat could happen when the training process is not fully controlled, such as training on third-party datasets or adopting third-party models, which poses a new and realistic threat. Although backdoor learning is an emerging and rapidly growing research area, there is still no comprehensive and timely review of it. In this article, we present the first comprehensive survey of this realm. We summarize and categorize existing backdoor attacks and defenses based on their characteristics, and provide a unified framework for analyzing poisoning-based backdoor attacks. Besides, we also analyze the relation between backdoor attacks and relevant fields (i.e., adversarial attacks and data poisoning), and summarize widely adopted benchmark datasets. Finally, we briefly outline certain future research directions relying upon reviewed works. A curated list of backdoor-related resources is also available at https://github.com/THUYimingLi/backdoor-learning-resources.
In search of the weirdest galaxies in the Universe

Weird galaxies are outliers that have either unknown or very uncommon features making them different from the normal sample. These galaxies are very interesting as they may provide new insights into current theories, or can be used to form new theories about processes in the Universe. Interesting outliers are often found by accident, but this will become increasingly more difficult with future big surveys generating an enormous amount of data. This gives the need for machine learning detection techniques to find the interesting weird objects. In this work, we inspect the galaxy spectra of the third data release of the Galaxy And Mass Assembly survey and look for the weird outlying galaxies using two different outlier detection techniques. First, we apply distance-based Unsupervised Random Forest on the galaxy spectra using the flux values as input features. Spectra with a high outlier score are inspected and divided into different categories such as blends, quasi-stellar objects, and BPT outliers. We also experiment with a reconstruction-based outlier detection method using a variational autoencoder and compare the results of the two different methods. At last, we apply dimensionality reduction techniques on the output of the methods to inspect the clustering of similar spectra. We find that both unsupervised methods extract important features from the data and can be used to find many different types of outliers.
Mixing Real and Synthetic Data to Enhance Neural Network Training - A Review of Current Approaches

Deep neural networks have gained tremendous importance in many computer vision tasks. However, their power comes at the cost of large amounts of annotated data required for supervised training. In this work we review and compare different techniques available in the literature to improve training results without acquiring additional annotated real-world data. This goal is mostly achieved by applying annotation-preserving transformations to existing data or by synthetically creating more data.
Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification

Data uncertainty in practical person reID is ubiquitous, hence it requires not only learning the discriminative features, but also modeling the uncertainty based on the input. This paper proposes to learn the sample posterior and the class prior distribution in the latent space, so that not only representative features but also the uncertainty can be built by the model. The prior reflects the distribution of all data in the same class, and it is the trainable model parameters. While the posterior is the probability density of a single sample, so it is actually the feature defined on the input. We assume that both of them are in Gaussian form. To simultaneously model them, we put forward a distribution loss, which measures the KL divergence from the posterior to the priors in the manner of supervised learning. In addition, we assume that the posterior variance, which is essentially the uncertainty, is supposed to have the second-order characteristic. Therefore, a $\Sigma-$net is proposed to compute it by the high order representation from its input. Extensive experiments have been carried out on Market1501, DukeMTMC, MARS and noisy dataset as well.
Joint Multiuser DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence

Mobile-edge computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge, thus acting as an enabler for edge intelligence services empowering massive mobile and Internet-of-Things (IoT) devices with artificial intelligence (AI) capability. With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN)-based AI applications, which are generally resource hungry and computation intensive such that an individual UE can hardly afford by itself in real time. However, the resources in each individual edge server are typically limited. Therefore, any resource optimization involving edge servers is by nature a resource-constrained optimization problem and needs to be tackled in such a realistic context. Motivated by this observation, we investigate the optimization problem of DNN partitioning (an emerging DNN offloading scheme) in a realistic multiuser resource-constrained condition that rarely considered in previous works. Despite the extremely large solution space, we reveal several properties of this specific optimization problem of joint multi-UE DNN partitioning and computational resource allocation. We propose an algorithm called iterative alternating optimization (IAO) that can achieve the optimal solution in polynomial time. In addition, we present a rigorous theoretic analysis of our algorithm in terms of time complexity and performance under realistic estimation error. Moreover, we build a prototype that implements our framework and conducts extensive experiments using realistic DNN models, whose results demonstrate its effectiveness and efficiency.
Add force and/or change underlying projection method to improve accuracy of Explicit Robin-Neumann and fully decoupled schemes for the coupling of incompressible fluid with thin-walled structure

Enlightened by added-mass effect and viscosity of fluid, in Explicit Robin-Neumann and fully decoupled schemes for the coupling of incompressible fluid with thin-walled structure, the force between fluid and structure corresponding to viscosity is increased. Numerical experiments demonstrate improvement of accuracy under such modification. To further improve accuracy of fully decoupled schemes, the underlying projection method is replaced.
Collision Avoidance Robotics Via Meta-Learning (CARML)

This paper presents an approach to exploring a multi-objective reinforcement learning problem with Model-Agnostic Meta-Learning. The environment we used consists of a 2D vehicle equipped with a LIDAR sensor. The goal of the environment is to reach some pre-determined target location but also effectively avoid any obstacles it may find along its path. We also compare this approach against a baseline TD3 solution that attempts to solve the same problem.
GENERATIVE ADVERSARIAL NETWORKS FOR SINGLE PHOTO 3D RECONSTRUCTION

Abstract. Fast but precise 3D reconstructions of cultural heritage scenes are becoming very requested in the archaeology and architecture. While modern multi-image 3D reconstruction approaches provide impressive results in terms of textured surface models, it is often the need to create a 3D model for which only a single photo (or few sparse) is available. This paper focuses on the single photo 3D reconstruction problem for lost cultural objects for which only a few images are remaining. We use image-to-voxel translation network (Z-GAN) as a starting point. Z-GAN network utilizes the skip connections in the generator network to transfer 2D features to a 3D voxel model effectively (Figure 1). Therefore, the network can generate voxel models of previously unseen objects using object silhouettes present on the input image and the knowledge obtained during a training stage. In order to train our Z-GAN network, we created a large dataset that includes aligned sets of images and corresponding voxel models of an ancient Greek temple. We evaluated the Z-GAN network for single photo reconstruction on complex structures like temples as well as on lost heritage still available in crowdsourced images. Comparison of the reconstruction results with state-of-the-art methods are also presented and commented.
Denial-of-Service Vulnerability of Hash-Based Transaction Sharding: Attack and Countermeasure

Since 2016, sharding has become an auspicious solution to tackle the scalability issue in legacy blockchain systems. Despite its potential to strongly boost the blockchain throughput, sharding comes with its own security issues. To ease the process of deciding which shard to place transactions, existing sharding protocols use a hash-based transaction sharding in which the hash value of a transaction determines its output shard. Unfortunately, we show that this mechanism opens up a loophole that could be exploited to conduct a single-shard flooding attack, a type of Denial-of-Service (DoS) attack, to overwhelm a single shard that ends up reducing the performance of the system as a whole. To counter the single-shard flooding attack, we propose a countermeasure that essentially eliminates the loophole by rejecting the use of hash-based transaction sharding. The countermeasure leverages the Trusted Execution Environment (TEE) to let blockchain's validators securely execute a transaction sharding algorithm with a negligible overhead. We provide a formal specification for the countermeasure and analyze its security properties in the Universal Composability (UC) framework. Finally, a proof-of-concept is developed to demonstrate the feasibility and practicality of our solution.
Intrusion Detection in Binary Process Data: Introducing the Hamming-distance to Matrix Profiles

The digitisation of industry provides a plethora of novel applications that increase flexibility and reduce setup and maintenance time as well as cost. Furthermore, novel use cases are created by the digitisation of industry, commonly known as Industry 4.0 or the Industrial Internet of Things, applications make use of communication and computation technology that is becoming available. This enables novel business use cases, such as the digital twin, customer individual production, and data market places. However, the inter-connectivity such use cases rely on also significantly increases the attack surface of industrial enterprises. Sabotage and espionage are aimed at data, which is becoming the most crucial asset of an enterprise. Since the requirements on security solutions in industrial networks are inherently different from office networks, novel approaches for intrusion detection need to be developed. In this work, process data of a real water treatment process that contains attacks is analysed. Analysis is performed by an extension of Matrix Profiles, a motif discovery algorithm for time series. By extending Matrix Profiles with a Hamming-distance metric, binary and tertiary actuators can be integrated into the analysis in a meaningful fashion. This algorithm requires low training effort while providing accurate results. Furthermore, it can be employed in a real-time fashion. Selected actuators in the data set are analysed to highlight the applicability of the extended Matrix Profiles.
Diyabet bakım kalitesinin değerlendirilmesinde laboratuvar test sonuçları nasıl kullanılabilir

AMAC: Diyabet bakim kalitesinin degerlendirilebilmesi icin kalite indikatorleri belirlenmektedir. Bazi kalite indikatorleri hasta muayene verilerinden oldugu kadar HbA1c, LDL-Kolesterol, idrarda albumin, kreatinin klirens veya hesaplanmis glomeruler filtrasyon hizi (eGFR) gibi laboratuvar test sonuclarindan elde edilen sonuc olculerinden hesaplanir. Calismamizda bu testlerin klinik yararlanim ve diyabet bakim kalitesinin degerlendirilmesinde kullanilma durumunun degerlendirilmesi amaclandi.YONTEMLER: Diyabet ve komplikasyonlariyla iliskili klinik birimlerden ve laboratuvardan olusturulan ekip calisma protokolunu hazirladi. Davet edilen 199 kurulustan ucu calismayi yuruttu. Baslangicta hasta sayisi 243 idi. Klinik muayeneler calisma basinda yapildi. Hastalar protokole gore alti ay izlendi. Toplanan veriler diyabet bakim kalitesi acisindan degerlendirildi. BULGULAR: Baslangicta VKI, kan basinclari, aclik ve tokluk glukoz, HbA1c, HDL-K (kadinlarda), LDL-K duzeylerine gore hedef duzeyi karsilamayan hasta yuzdeleri %40 ile %81 arasindaydi. Altinci ayda HbA1c icin %25’e dustu. Ortalama degerler incelendiginde, hasta grubunda 6 aydaki HbA1c ortalamalarinda A, B ve C hastanelerindeki dususler ve %95 guven araliklari, sirasiyla, -0,46 (-0,64 ve 0,88); -1,71 (-2,17 ve -1,25) ve -1,20 (-1,64 ve -0,77) olarak saptandi. Risk grubundaki hasta yuzdesinde baslangica gore dusus gozlenemese de T.Kol, HDL-Kol, LDL-Kol, trigliserid ve idrarda Alb. ve kreatinin klirens ortalama duzeylerinde de olumlu yonde degisiklikler saptandi. SONUC: Calismamiz diyabet bakim kalitesinin degerlendirilmesinde laboratuvar test sonuclarindan yararlanilmasina ornek uygulamalar ve sonuclar icermektedir. Ancak ulkemizde henuz uygulanmayan bu tur calismalar icin coklu disiplinli ve sistematik bir yapilanma gerekmektedir.
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node

Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate that both the logic and SRAM voltage can be dropped to 0.5Vwithout any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving energy efficiency by 2.2X w.r. t. nominal conditions. Furthermore, we show that the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more than99% of the nominal accuracy (with a bit error rate ~1/1000). In this operating point, our prototype performs 4Gop/s (15.4Inference/s on the CIFAR-10 dataset) by computing up to 13binary ops per pJ, achieving 22.8 Inference/s/mW while keeping within a peak power envelope of 674uW - low enough to enable always-on operation in ultra-low power smart cameras, long-lifetime environmental sensors, and insect-sized pico-drones.
The influence of bulk composition on the long-term interior-atmosphere evolution of terrestrial exoplanets

Aims. The secondary atmospheres of terrestrial planets form and evolve as a consequence of interaction with the interior over geological time. We aim to quantify the influence of planetary bulk composition on the interior–atmosphere evolution for Earth-sized terrestrial planets to aid in the interpretation of future observations of terrestrial exoplanet atmospheres. Methods. We used a geochemical model to determine the major-element composition of planetary interiors (MgO, FeO, and SiO2) following the crystallization of a magma ocean after planet formation, predicting a compositional profile of the interior as an initial condition for our long-term thermal evolution model. Our 1D evolution model predicts the pressure–temperature structure of the interior, which we used to evaluate near-surface melt production and subsequent volatile outgassing. Volatiles are exchanged between the interior and atmosphere according to mass conservation. Results. Based on stellar compositions reported in the Hypatia catalog, we predict that about half of rocky exoplanets have a mantle that convects as a single layer (whole-mantle convection), and the other half exhibit double-layered convection due to the presence of a mid-mantle compositional boundary. Double-layered convection is more likely for planets with high bulk planetary Fe-content and low Mg/Si-ratio. We find that planets with low Mg/Si-ratio tend to cool slowly because their mantle viscosity is high. Accordingly, low-Mg/Si planets also tend to lose volatiles swiftly through extensive melting. Moreover, the dynamic regime of the lithosphere (plate tectonics vs. stagnant lid) has a first-order influence on the thermal evolution and volatile cycling. These results suggest that the composition of terrestrial exoplanetary atmospheres can provide information on the dynamic regime of the lithosphere and the thermo-chemical evolution of the interior.
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge

In this report, we descibe our approach to the ECCV 2020 VIPriors Object Detection Challenge which took place from March to July in 2020. We show that by using state-of-the-art data augmentation strategies, model designs, and post-processing ensemble methods, it is possible to overcome the difficulty of data shortage and obtain competitive results. Notably, our overall detection system achieves 36.6$\%$ AP on the COCO 2017 validation set using only 10K training images without any pre-training or transfer learning weights ranking us 2nd place in the challenge.
Correlated disorder in a model binary glass through a local SU(2) bonding topology

A quantitative understanding of the microscopic constraints which underlie a well relaxed glassy structure is the key to developing a microscopic theory of structural evolution and plasticity for the amorphous solid. Here we demonstrate the applicability of one such theory of local bonding constraints developed by D. R. Nelson [Phys. Rev. B 28, 5515 (1983)], for a model binary Lennard-Jones glass structure that has undergone an isothermal annealing simulation spanning over 10 micro-seconds of physical simulation time. By introducing a modified radical Voronoi tessellation which removes some ambiguity in how nearest neighbour bonds are enumerated, it is found, that a large proportion ($>95\%$) of local atomic environments follow the connectivity rules of the SU(2) topology of Nelson's work resulting in a dense network of disclination lines characterizing the defect bonds. Furthermore, it is numerically shown that a low energy glass structure corresponds to a reduced level of bond-length frustration and thus a minimally defected bond-defect network. It is then demonstrated that such a defect network provides a framework in which to analyse thermally-activated structural excitations, revealing those high-energy/low-density regions not following the connectivity constraints are more likely to undergo structural rearrangement that often results in a local relaxation that ends with the creation of new SU(2) local topology content.