Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA

The (open-high-low-close) OHLC data is the most common data form in the field of finance and the investigate object of various technical analysis. With increasing features of OHLC data being collected, the issue of extracting their useful information in a comprehensible way for visualization and easy interpretation must be resolved. The inherent constraints of OHLC data also pose a challenge for this issue. This paper proposes a novel approach to characterize the features of OHLC data in a dataset and then performs dimension reduction, which integrates the feature information extraction method and principal component analysis. We refer to it as the pseudo-PCA method. Specifically, we first propose a new way to represent the OHLC data, which will free the inherent constraints and provide convenience for further analysis. Moreover, there is a one-to-one match between the original OHLC data and its feature-based representations, which means that the analysis of the feature-based data can be reversed to the original OHLC data. Next, we develop the pseudo-PCA procedure for OHLC data, which can effectively identify important information and perform dimension reduction. Finally, the effectiveness and interpretability of the proposed method are investigated through finite simulations and the spot data of China's agricultural product market.
Learning with Memory-based Virtual Classes for Deep Metric Learning

The core of deep metric learning (DML) involves learning visual similarities in high-dimensional embedding space. One of the main challenges is to generalize from seen classes of training data to unseen classes of test data. Recent works have focused on exploiting past embeddings to increase the number of instances for the seen classes. Such methods achieve performance improvement via augmentation, while the strong focus on seen classes still remains. This can be undesirable for DML, where training and test data exhibit entirely different classes. In this work, we present a novel training strategy for DML called MemVir. Unlike previous works, MemVir memorizes both embedding features and class weights to utilize them as additional virtual classes. The exploitation of virtual classes not only utilizes augmented information for training but also alleviates a strong focus on seen classes for better generalization. Moreover, we embed the idea of curriculum learning by slowly adding virtual classes for a gradual increase in learning difficulty, which improves the learning stability as well as the final performance. MemVir can be easily applied to many existing loss functions without any modification. Extensive experimental results on famous benchmarks demonstrate the superiority of MemVir over state-of-the-art competitors. Code of MemVir is publicly available 1.
ESTIMATION OF TOTAL PHENOLIC AND FLAVONOID CONTENT OF POLYHERBAL

Background: Indian system of medicine mainly based on polyherbal compound to treat various types of diseases. Dashang lepa is one of the prestigious formulation of ten indigenous drugs which are having predominantly anti-inflammatory properties and clinically used in inflammatory swelling like boil, furunculosis, cellulitis, erysipelas etc. As several reports are on various individual phytochemical, phenolic and flavonoid content but none is having study of polyherbal compound Dashang lepa. Objectives: Total phenolic and total flavonoid content were studied of alcoholic extract of Dashang lepa. Method: Methanolic extraction was done by hot percolation method through soxhlet apparatus. Phenolic content was estimated using Follin ciocalteau reagent, flavonoid using aluminum chloride (2%) reagent as quercetin equivalent. Results: Estimation of total phenolic and total flavonoid content showed that Dashang lepa extract was having phenolic content (15.8 ± 1.14) μg Gallic acid equivalents (GAE) and flavonoid content (22.4 ± 2.54) in μg of quercetin equivalents (QE).
Divide and Rule: Training Context-Aware Multi-Encoder Translation Models with Little Resources

Multi-encoder models are a broad family of context-aware Neural Machine Translation (NMT) systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence. The context encoding is undertaken by contextual parameters , trained on document-level data. In this work, we show that training these parameters takes large amount of data, since the contextual training signal is sparse. We propose an efﬁcient alternative, based on splitting sentence pairs, that allows to enrich the training signal of a set of parallel sentences by breaking intra-sentential syntactic links, and thus frequently pushing the model to search the context for disambiguating clues. We evaluate our approach with BLEU and contrastive test sets, showing that it allows multi-encoder models to achieve comparable performances to a setting where they are trained with × 10 document-level data. We also show that our approach is a viable option to context-aware NMT for language pairs with zero document-level parallel data.
Training robust deep learning models for medical imaging tasks with spectral decoupling

Deep neural networks show impressive performance in medical imaging tasks. However, many current networks generalise poorly to data unseen during training, for example data generated by di ﬀ erent centres. Such behaviour can be caused by networks overﬁtting easy-to-learn, or statistically dominant, features while disregarding other potentially informative features. Moreover, dominant features can lead to learning spurious correlations. For instance, indistinguishable di ﬀ erences in the sharpness of the images from two di ﬀ erent scanners can degrade the performance of the network signiﬁcantly. To address these challenges, we evaluate the utility of spectral decoupling in the context of medical image analysis. Spectral decoupling encourages the neural network to learn more features by simply regularising the networks’ unnormalized prediction scores with an L2 penalty. Simulation experiments show that spectral decoupling allows training neural networks on datasets with strong spurious correlations. Networks trained without spectral decoupling do not learn the original task and appear to make false predictions based on the spurious correlations. Spectral decoupling also signiﬁcantly increases networks’ robustness for data distribution shifts. To validate our ﬁndings, we train networks with and without spectral decoupling to detect prostate cancer on haematoxylin and eosin stained whole slide images. The networks are then evaluated with data scanned in the same centre with two di ﬀ erent scanners, and data from a di ﬀ erent centre. Networks trained with spectral decoupling increase the accuracy by 10 percentage points over weight decay on the dataset from a di ﬀ erent centre. Our results show that spectral decoupling allows training generalisable
Green IoT using UAVs in B5G Networks: A Review of Applications and Strategies

Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.
Tossing quantum coins and dice

The procedure of tossing quantum coins and dice is described. This case is an important example of a quantum procedure because it presents a typical framework employed in quantum information processing and quantum computing. The emphasis is on the clarification of the difference between quantum and classical conditional probabilities. These probabilities are designed for characterizing different systems, either quantum or classical, and they, generally, cannot be reduced to each other. Thus the Lüders probability cannot be treated as a generalization of the classical conditional probability. The analogies between quantum theory of measurements and quantum decision theory are elucidated.
On deeply critical oriented cliques

In this work we consider arc criticality in colourings of oriented graphs. We study deeply critical oriented graphs, those graphs for which the removal of any arc results in a decrease of the oriented chromatic number by 2. We prove the existence of deeply critical oriented cliques of every odd order n ≥ 9 , closing an open question posed by Borodin et al. Additionally, we prove the nonexistence of deeply critical oriented cliques among the family of circulant oriented cliques of even order.
Digital Forensics vs. Anti-Digital Forensics: Techniques, Limitations and Recommendations

The number of cyber attacks has increased tremendously in the last few years. This resulted into both human and financial losses at the individual and organization levels. Recently, cyber-criminals are leveraging new skills and capabilities by employing anti-forensics activities, techniques and tools to cover their tracks and evade any possible detection. Consequently, cyber-attacks are becoming more efficient and more sophisticated. Therefore, traditional cryptographic and non-cryptographic solutions and access control systems are no longer enough to prevent such cyber attacks, especially in terms of acquiring evidence for attack investigation. Hence, the need for well-defined, sophisticated, and advanced forensics investigation tools are highly required to track down cyber criminals and to reduce the number of cyber crimes. This paper reviews the different forensics and anti-forensics methods, tools, techniques, types, and challenges, while also discussing the rise of the anti-anti-forensics as a new forensics protection mechanism against anti-forensics activities. This would help forensics investigators to better understand the different anti-forensics tools, methods and techniques that cyber criminals employ while launching their attacks. Moreover, the limitations of the current forensics techniques are discussed, especially in terms of issues and challenges. Finally, this paper presents a holistic view from a literature point of view over the forensics domain and also helps other fellow colleagues in their quest to further understand the digital forensics domain.
On the absence of the complete Bose-Einstein condensation for hamiltonians with two-body interactions

The Reduced Density Matrix Functional Theory (RDMFT) is a remarkable tool for analytically studying properties of ground states of strongly interacting quantum many body systems. As it gives access to the one-particle reduced density matrix of the ground state, it provides a perfectly tailored approach to studying the quantum depletion problem, i.e. the absence of the complete BoseEinstein condensation in nature. Despite relentless efforts over the past few decades, a comprehensive explanation of the quantum depletion in its full generality was not found (progress has been made only for specific regimes such as the weak interaction limit, dilute or homogeneous gases). Only recently, a tremendous progress in the formulation of RDMFT for bosons made explicit calculations possible. In this article, we utilize these latest theoretical breakthroughs and introduce a powerful ansatz that solves this long-standing problem. By proposing a strikingly simple, one-fits-all family of variational trial states we prove that quantum depletion is present in any quantum system of spinless bosons with completely arbitrary pair interactions, kinetic energy operators as well as local potentials with the only requirement for the total number of bosons to be conserved. In particular, our proposed trial state is sufficient to show that the relevant density functional universally exhibits a repulsive gradient force which diverges when the number of non-condensed bosons tends to zero.
Robust Beamforming Design for Covert Communications

In this paper, we consider a common unicast beamforming network where Alice utilizes the communication to Carol as a cover and covertly transmits a message to Bob without being recognized by Willie. We investigate the beamformer design of Alice to maximize the covert rate to Bob when Alice has either perfect or imperfect knowledge about Willie’s channel state information (WCSI). For the perfect WCSI case, the problem is formulated under the perfect covert constraint, and we develop a covert beamformer by applying semidefinite relaxation and the bisection method. Then, to reduce the computational complexity, we further propose a zero-forcing beamformer design with a single iteration processing. For the case of the imperfect WCSI, the robust beamformer is developed based on a relaxation and restriction approach by utilizing the property of Kullback-Leibler divergence. Furthermore, we derive the optimal decision threshold of Willie, and analyze the false alarm and the missed detection probabilities in this case. Finally, the performance of the proposed beamformer designs is evaluated through numerical experiments.
Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos

We are motivated by the goal of generalist robots that can complete a wide range of tasks across many environments. Critical to this is the robot's ability to acquire some metric of task success or reward, which is necessary for reinforcement learning, planning, or knowing when to ask for help. For a general-purpose robot operating in the real world, this reward function must also be able to generalize broadly across environments, tasks, and objects, while depending only on on-board sensor observations (e.g. RGB images). While deep learning on large and diverse datasets has shown promise as a path towards such generalization in computer vision and natural language, collecting high quality datasets of robotic interaction at scale remains an open challenge. In contrast,"in-the-wild"videos of humans (e.g. YouTube) contain an extensive collection of people doing interesting tasks across a diverse range of settings. In this work, we propose a simple approach, Domain-agnostic Video Discriminator (DVD), that learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task, and can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos. We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.
Descending Predictive Feedback: From Optimal Control to the Sensorimotor System

Descending predictive feedback (DPF) is an ubiquitous yet unexplained phenomenon in the central nervous system. Motivated by recent observations on motor-related signals in the visual system, we approach this problem from a sensorimotor standpoint and make use of optimal controllers to explain DPF. We define and analyze DPF in the optimal control context, revisiting several control problems (state feedback, full control, and output feedback) to explore conditions that necessitate DPF. We find that even small deviations from the unconstrained state feedback problem (e.g. incomplete sensing, communication delay) necessitate DPF in the optimal controller. We also discuss parallels between controller structure and observations from neuroscience. In particular, the system level (SLS) controller displays DPF patterns compatible with predictive coding theory and easily accommodates signaling restrictions (e.g. delay) typical to neurons, making it a candidate for use in sensorimotor modeling.
A genuinely natural information measure

The theoretical measuring of information was famously initiated by Shannon in his mathematical theory of communication, in which he proposed a now widely used quantity, the entropy, measured in bits. Yet, in the same paper, Shannon also chose to measure the information in continuous systems in nats, which differ from bits by the use of the natural rather than the binary logarithm. We point out that there is nothing natural about the choice of logarithm basis, rather it is arbitrary. We remedy this problematic state of affairs by proposing a genuinely natural measure of information, which we dub gnats. We show that gnats have many advantages in information theory, and propose to adopt the underlying methodology throughout science, arts and everyday life.
Deep Image Harmonization by Bridging the Reality Gap

Image harmonization has been significantly advanced with large-scale harmonization dataset. However, the current way to build dataset is still labor-intensive, which adversely affects the extendability of dataset. To address this problem, we propose to construct rendered harmonization dataset with fewer human efforts to augment the existing real-world dataset. To leverage both real-world images and rendered images, we propose a cross-domain harmonization network to bridge the domain gap between two domains. Moreover, we also employ well-designed style classifiers and losses to facilitate cross-domain knowledge transfer. Extensive experiments demonstrate the potential of using rendered images for image harmonization and the effectiveness of our proposed network.
A turbulence model based on deep neural network considering the near-wall effect

There exists continuous demand of improved turbulence models for the closure of Reynolds Averaged Navier-Stokes (RANS) simulations. Machine Learning (ML) offers effective tools for establishing advanced empirical Reynolds stress closures on the basis of high fidelity simulation data. This paper presents a turbulence model based on the Deep Neural Network(DNN) which takes into account the non-linear relationship between the Reynolds stress anisotropy tensor and the local mean velocity gradient as well as the near-wall effect. The construction and the tuning of the DNN-turbulence model are detailed. We show that the DNN-turbulence model trained on data from direct numerical simulations yields an accurate prediction of the Reynolds stresses for plane channel flow. In particular, we propose including the local turbulence Reynolds number in the model input.
Fluid mechanics of mosaic ciliated tissues

In tissues as diverse as amphibian skin and the human airway, the cilia that propel fluid are grouped in sparsely distributed multiciliated cells (MCCs). We investigate fluid transport in this “mosaic” architecture, with emphasis on the trade-offs that may have been responsible for its evolutionary selection. Live imaging of MCCs in embryos of the frog Xenopus laevis shows that cilia bundles behave as active vortices that produce a flow field accurately represented by a local force applied to the fluid. A coarse-grained model that self-consistently couples bundles to the ambient flow reveals that hydrodynamic interactions between MCCs limit their rate of work so that when the system size is large compared to a single MCC, they best shear the tissue at low area coverage, a result that mirrors findings for other sparse distributions such as cell receptors and leaf stomata.
Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

Cluster assignment of large and complex datasets is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the resampled data from estimated bottleneck space have consistent clustering properties to improve the deep clustering model progressively. Experiments on various datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.
Continuous latent position models for instantaneous interactions

We create a framework to analyze the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays and easily available. Examples of instantaneous interactions include email networks, phone call networks, and some common types of technological and transportation networks. Our framework relies on a novel extension of the latent position network model: we assume that the entities are embedded in a latent Euclidean space and that they move along individual trajectories which are continuous over time. These trajectories are used to characterize the timing and frequency of the pairwise interactions. We discuss an inferential framework where we estimate the individual trajectories from the observed interaction data and propose applications on artificial and real data.
Sorption behavior of an oriented surface-grown MOF-film studied by in situ X-ray diffraction

The structural changes of a flexible metal–organic framework (MOF) during adsorption and desorption of guest molecules were investigated by means of in situ X-ray diffraction. The flexible MOF Fe-MIL-88B was investigated in the form of surface-grown, oriented crystal films and in bulk form, and its structural changes upon ad- and desorption of water were recorded. Due to the orientation of the grown crystals on the gold substrates, structural changes in [001] direction could be observed. For the randomly oriented bulk crystals the structural changes in all crystallographic directions were observable and the changes of the lattice constants a and c and the cell volume could be determined quantitatively by indexing the complete diffraction patterns. The MOF crystals show two distinct steps of structural changes during water sorption. During adsorption of water the lattice parameter c is slightly decreased, whereas the lattice parameter a shows a prominent two-step increase resulting in an increase of the cell volume by about 40%. The conventional volumetric water sorption measurement also shows two distinct steps in the uptake of water vapor that can be correlated with the structural changes observed in X-ray diffraction.