Miracle-dz/UniFAR-FTUs huggingface dataset:
From Hodl to Heist: Analysis of Cyber Security Threats to Bitcoin Exchanges

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin’s market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.
Bellowing,Rank-class and Mating Success in Père David's Deer Stags

We carried out behavioral observations on the Pere David's deer ( Elaphurus davidianus ) stags in the Beijing Milu Park to investigate the vocal display and mating success in different social rank classes during rut.The stags were classified into three rank classes according to their rutting behavior:"harem master","challenger" and "bachelor". Bellowing,herding and mating were recorded in 12 stags:4 harem masters,5 challengers and 3 bachelors.The effectiveness value ( E ) were defined,that is E = A / T ,to assess the effectiveness of herding or mating attempts made by stags ( T represents the frequency of herding or mating attempts made by stag and A represents the frequency of herding or mating accepted by hinds). Results exhibited that:①Frequency of bellowing,herding or mating showed significantly differences among "harem masters","challengers" and "bachelors"( P 0 05). ②Effectiveness value of herding also differed significantly among "harem masters","challengers" and "bachelors" ( P 0 05). ③Effectiveness value of mating was significantly different between harem masters and challengers ( P 0 05). We conclude that bellowing of Pere David's deer stags strongly associates with its rank class,and rank class affects the mating success of stag directly.In addition,we recorded another new vocal display pattern in stags-barking,which was only recorded in hinds previously (Wemmer et al ,1983).
Old mountain birches at high altitudes are prone to outbreaks of Epirrita autumnata (Lepidoptera: Geometridae)

We studied which site characteristics were associated with susceptibility to outbreaks of Epirrita autumnata Borkhausen in a mixed coniferous forest area in Finnish Lapland during a high density phase of the population cycle from 1992 to 1995. This information is important in understanding population dynamics of the species and as a background in studies of biotic interactions between E. autumnata and its host plants and natural enemies. We determined relative densities of larvae living on birch, Betula pubescens Ehrhart, at 45 sites covering an area of approximately 3,900 km2. Association of larval density with 5 site characteristics was evaluated: altitude-related winter minimum temperature, birch age, birch density, overall tree density, and forest type. Of these variables, local winter minimum temperature and birch age together accounted for nearly half of the variance in larval density; the rest of the variables were not important. There is a clear mechanism for the effect of winter minimum temperature: E. autumnata eggs are killed at temperatures below -36 degrees C. Because of inversion, cold air accumulates in topographically low sites, killing eggs there. We have, however, no simple explanation for why sites with old birches had high larval densities.
Toward Universal Neural Interfaces for Daily Use: Decoding the Neural Drive to Muscles Generalises Highly Accurate Finger Task Identification Across Humans

Peripheral neural signals can be used to estimate movement-specific muscle activation patterns for the purpose of human-machine interfacing (HMI). The available HMI solutions, however, provide limited movement decoding accuracy that often results in inadequate device control, especially in the dynamic tasks context, and require extensive algorithm training that is highly subject-specific. Here, we show that dexterous movements can be identified with high accuracy using a physiology-derived and information-theoretically optimised feature space that targets the spatio-temporal properties of the spiking activity of spinal motor neurons (neural features), decomposed from the interference myoelectric signal. Moreover, we show that the movement decoding accuracy based on these neural features is not influenced by the muscle activation level, reaching overall >98% in the full range of forces investigated and from processing intervals as short as 30-ms. Finally, we show that the high accuracy in individual finger movement recognition can be achieved without user-specific models. These results are the first to show a highly accurate discrimination of dexterous movement tasks in a wide range of muscle activation levels from near-real time processing intervals, with minimal subject-specific training, and thus are promising for the translation of HMI to daily use.
An Encrypted Speech Retrieval Method Based on Deep Perceptual Hashing and CNN-BiLSTM

Since convolutional neural network (CNN) can only extract local features, and long short-term memory (LSTM) neural network model has a large number of learning calculations, a long processing time and an obvious degree of information loss as the length of speech increases. Utilizing the characteristics of autonomous feature extraction in deep learning, CNN and bidirectional long short-term memory (BiLSTM) network are combined to present an encrypted speech retrieval method based on deep perceptual hashing and CNN-BiLSTM. Firstly, the proposed method extracts the Log-Mel Spectrogram/MFCC features of the original speech and enters the CNN and BiLSTM networks in turn for model training. Secondly, we use the trained fusion network model to learn the deep perceptual feature and generate deep perceptual hashing sequences. Finally, the normalized Hamming distance algorithm is used for matching retrieval. In order to protect the speech security in the cloud, a speech encryption algorithm based on a 4D hyperchaotic system is proposed. The experimental results show that the proposed method has good discrimination, robustness, recall and precision compared with the existing methods, and it has good retrieval efficiency and retrieval accuracy for longer speech. Meanwhile, the proposed speech encryption algorithm has a high key space to resist exhaustive attacks.
Impact of Varied Buffer Layer Designs on Single-Event Response of 1.2-kV SiC Power MOSFETs

In this article, the single-event response of the 1.2-kV silicon-carbide (SiC) power MOSFETs with varied buffer layer designs is investigated by the 2-D numerical simulations. The structural parameters of the buffer layers are compared and analyzed to understand the transient response after the heavy ion strike and the related physical mechanisms comprehensively. Simulation results reveal that an optimized single buffer structure can be acquired by using a relatively thicker buffer layer (T $\mu \text{m}$ ) and a moderate doping concentration (D cm−3). It demonstrates that the single-event-burnout (SEB) performance can be improved significantly under the worst case bias conditions [a drain bias voltage of 1.2 kV and a linear energy transfer (LET) of 1 pC/ $\mu \text{m}$ ]. In addition, the optimized structural combinations adopted in the dual or triple buffer layers can strengthen the SEB performance further. The simulation results show that a step electric field distribution is established inside the optimized dual or triple buffers, where the electric field peak is mitigated from 3 to 2.2 MV/cm. Moreover, the excess carrier generation is suppressed and the local temperature rise is weakened during the transient electrothermal process. Consequently, the structure with the optimal buffer layer designs can enlarge the SEB safe operating area (SOA) from 30%–50% to 100% rated breakdown voltage at high LET bias, which makes the SiC power MOSFETs used in aerospace and aviation power electronic systems become possible.
Link-Level Throughput Maximization Using Deep Reinforcement Learning

A multi-agent deep reinforcement learning framework is proposed to address link level throughput maximization by power allocation and modulation and coding scheme (MCS) selection. Given the complex problem space, reward shaping is utilized instead of classical training procedures. The time-frame utilities are decomposed into subframe rewards, and a stepwise training procedure is proposed, starting from a simplified power allocation setup without MCS selection, incorporating MCS selection gradually, as the agents learn optimal power allocation. The proposed method outperforms both weighted minimum mean squared error (WMMSE) and Fractional Programming (FP) with idealized MCS selections.
Optimizing Directional Power Deposition of Antenna in Radio Frequency Excited Helicon Plasma Thruster

Plasma thruster operating with high-density helicon source is expected to mitigate the problems of the finite lifetime of electric thrusters and shows a possibility to be incorporated in spacecraft for long-duration interplanetary missions. Helicon wave is a low-frequency whistler wave, where the frequency $\omega$ lies in between lower hybrid frequency $\omega_{LH}$ and electron cyclotron frequency $\omega_{c}$. The generated electromagnetic wave from helicon antenna penetrates to the plasma and transfer energy to the particles. In helicon thruster with lower rf power 1 – 2 kW, relatively fully ionized plasma $\eta\geq 10^{14}cm^{3}$ can be achieved [1], [2]. The high plasma resistance and power deposition are the key factors for the optimal design of helicon antenna [3]. This research work investigates the directional power deposition of nagoya, half helix, and a single loop antenna at a driven frequency, $f=13.56$ MHz. A wide range of plasma densities, $n_{0}$ from 1011cm−3 to 101cm−3 and magnetic field strength, $B_{0}=0.3T$ was taken into account. HELIC code [4]–[7] is used for designing rf plasma sources. Plasma density profile has significant effects on antenna plasma coupling. The optimal combination of the plasma density, magnetic field for which the rf power deposition maximize presented.
Social Structure of the Japanese Wagtail Motacilla grandis

The vocalization of the Japanese wagtail was classified into location call, warning call, threatening sound and song, each being composed of basic sounds and phrases. The epigamic behavioural sequence to copulation was related to the following behaviours: Female-appeasement display, flying courtship display and repelling; Male-courtship display, bill opening and back showing. Aggressive behaviours (chasing, threatening and physical combat) were most frequently observed between the same sexes. The most intensive exclusiveness was also seen between the same sexes showing the above aggressive behaviours. Spatial relationships between the song and two aggressive behaviours (chasing and threatening) were interpreted in relation to 1) the area which an owner tried to occupy, 2) the dominance relationship between the owner and others which tried to intrude, and 3) the aggressive area where the owner's aggressiveness existed. The border line of territory as "any defended area" was defined here as the outer limit of aggressive area where the neighbour was absent and as the positions of mutual threatening where the neighbour existed.
The effect of coal surface mine reclamation on diurnal lepidopteran conservation

Diurnal lepidopteran and vegetational communities were surveyed on 18 reclaimed coal surface-mined sites and five reference, hardwood sites in south-western Virginia. Comparison of lepidopteran communities using detrended correspondence analysis suggested that lepidopteran community composition on later-successional reclaimed sites was approaching that of the surrounding hardwoods. Diurnal moth species richness and abundance increased with time since reclamation, whereas butterfly species richness and abundance decreased. Diurnal moth communities were better indicators of vegetational community composition and species richness than were butterfly communities. While most lepidopteran species commonly found in the hardwoods were recorded in later-successional reclaimed sites, a number of less common species were not observed in them. Therefore, the success of mine reclamation in restoring the full complement of lepidopteran species native to hardwood forest in this region is debatable and further consideration must be given to the long-term effects of current mine reclamation practices.
Origins and Consequences of AID Expression in Lymphoid Neoplasms

The enzyme Activation-induced cytidine deaminase (AID) initiates somatic hypermutation (SHM) and class switch recombination (CSR) of the immunoglobulin (Ig) genes, which are critically important for an effective immune response. In addition, AID seems to contribute to B cell tolerance in mice and humans by, in some still undefined way, eliminating developing autoreactive B cells. As a trade-off for the benefits brought about by its physiological roles, AID can also contribute to cellular transformation and tumor progression through its mutagenic activity. AID deaminates deoxycytidines at the Ig genes thereby generating deoxyuridine, which as part of the normal mechanism of SHM and CSR is processed by DNA repair enzymes into a larger spectrum of point mutations and also DNA double- strand breaks. Multiple mechanisms regulate AID function to minimize deleterious or pathogenic DNA damage during antibody gene diversification. Despite this, off-target AID activity still makes point mutations and initiates chromosomal translocations that affect tumor suppressor and proto-oncogenes associated with B-cell lymphoid neoplasms. Through this collateral damage, AID is etiological for the development of lymphoma in several mouse models and is expressed in many human malignancies of mature B-cell origin where it may contribute to tumor clonal evolution. Mounting evidences indicate a role for AID also in disease progression and worsening of the prognosis of Chronic Lymphocytic Leukemia (CLL) and Chronic Myelogenous Leukemia (CML). Since these leukemia are not immediately derived from germinal center B cells, normal AID regulation might not be fully functional in those cases. This review discusses recent findings on the role of AID in lymphomagenesis. We describe the multilevel regulation of AID expression and function in normal compared to tumor B cells, specially focusing on the emerging role of AID in CLL and CML.
Predicting User Retweeting Behavior in Social Networks With a Novel Ensemble Learning Approach

Information sharing through online social networks has become a main mechanism by which people share information with their friends through retweeting behaviors, which may result in a variety of information diffusion cascades on social media such as Facebook, Twitter, and Weibo. Predicting user retweeting behavior in those social networks is extremely challenging. To complete the prediction task, identifying factors that affect retweeting behavior and constructing efficient model are necessary. In this paper, we study heterogeneous relation networks by considering various social interactions, which reflect how a particular retweeting action is affected by the social behaviors performed by the sender and the receiver of the retweet. We then generate various features from our identified factors belonging to three dimensions – content semantics, user diffusion behavior, and network structure. Moreover, we cast our prediction problem as an ensemble learning problem and propose a novel ensemble learning approach to solve the problem. Combing the generated features and the novel ensemble learning approach, we then propose a model named Retweeting Behavior on Multiple Heterogeneous Diffusion Relation Networks (RBMHDRN) to predict user retweeting behavior in social networks. Experiments on a real dataset extracted from a social network site Weibo demonstrate the effectiveness of our proposed model, indicating that our generated features and proposed approach can significantly improve the performance of predicting user retweeting behavior occurring in the process of information diffusion in social networks.
Map-Based Millimeter-Wave Channel Models: An Overview, Data for B5G Evaluation and Machine Learning

Within the mm-Wave range of the B5G communication systems, there will appear many types of applications with different link types. In discussions on how to cover the various channel modeling requirements of such applications, some researchers have suggested map-based channel models that are based on a ray-tracing algorithm. This article thus begins with an overview of mapbased mm-Wave channel models. The overview includes available modeling requirements with map-based channel models and the categorization of map-based channel parameters. We then explain why map-based channel models are necessary for researchers trying to evaluate novel technologies in the mm-Wave range. They are particularly necessary when the technologies operate with a new link type or when channel behaviors of a user exhibit user-specific characteristics. Next, we share the measurement data and the map-based channel parameters, which can model new link types and have user-specific characteristics. Finally, as a use case of the proposed channel model, we evaluate a machine-learning-based beam-selection algorithm that exploits power delay profiles through the shared database and a geometry-based stochastic channel model (GSCM). The delay profiles of the shared database are user-specific so they are highly correlated with angular parameters. Numerical results show that the algorithm can be more accurately evaluated through the shared database than through a GSCM. We expect that our overview and sharing the database will enable researchers to readily design a map-based channel model and evaluate B5G and machine-learning technologies.
Point Clouds Learning Using Directed Connected Graph

With the development of various 3D sensors, it has become easier for humans to obtain information in the 3D world, more and more people turn their attention to the problem of point clouds understanding. At present, most of methods focus on directly extracting features from point clouds, where feature extraction is performed by Multi-Layer Perception (MLP) and fusion is by local pooling. However, they do not consider the spatial relationship within the local point sets. We propose a directed connected graph network (DCGN), which can effectively capture the spatial relationship of local point sets by constructing the directed connected graph (DCG). Specifically, in the feature learning stage, the connection directions from neighbor points to the center point are constructed for each local point set to learn the feature transferring weights from neighbor points to center point. In order to further model the spatial distribution of local point sets, we use a distance-weighted method to perform local feature fusion. Extensive experimental results demonstrate that our method can achieve competitive performance on some standard data sets.
Neuroprotective effect of Hericium erinaceum against oxidative stress on PC12 cells

The free radical scavenging activity of various enzymatic extracts prepared from Hericium erinaceum was evaluated by using an ESR spectrometer. For this study, H. erinaceum was enzymatically hydrolyzed by eight carbohydrases and eight proteases. All of the hydrolyzed extracts scavenged DPPH, hydroxyl, superoxide, and alkyl radicals. Especially, the the pepsintreated extracts (PTE) exhibited the highest scavenging activity on hydroxyl radical, which is deemed of the strongest radical. Also, the neuroprotective effects of PTE against hydrogen peroxide-induced oxidative stress in PC12 cells were investigated. PTE stimulated the expression of Bcl-2, on the contrary, the extracts suppressed the expression of Bax. These results indicate that PTE possess antioxidative activity on PC12 cells.
Plasmin Cascade Mediates Thrombotic Events in SARS-CoV-2 Infection via Complement and Platelet-Activating Systems

Objective: Recently emerged beta-coronavirus SARS-CoV-2, has resulted in the current pandemic designated COVID-19. COVID-19 manifests as severe illness exhibiting systemic inflammatory response syndrome, acute respiratory distress syndrome (ARDS), thrombotic events, and shock, exacerbated further by co-morbidities and age. Recent clinical evidence suggests that the development of ARDS and subsequent pulmonary failure result from a complex interplay between cell types (endothelial, epithelial and immune) within the lung promoting inflammatory infiltration and a pro-coagulative state. How the complex molecular events mediated by SARS-CoV-2 in infected lung epithelial cells lead to thrombosis and pulmonary failure, is yet to be fully understood. Methods: We address these questions here, using publicly available transcriptomic data in the context of lung epithelia affected by SARS-CoV-2 and other respiratory infections, in vitro. We then extend our results to the understanding of in vivo lung, using a publicly available COVID-19 lung transcriptomic study. Results and Conclusions: Our analysis indicates that there exists a complex interplay between the fibrinolytic system particularly plasmin, and the complement and platelet-activating systems upon SARS-CoV-2 infection, with a potential for therapeutic intervention.
Influence of the debris trajectory on output inductive signal in large-diameter particle detector

An effectively way to detect potential failures of mechanical equipment is to detect its lubricating oil. For ships and steam turbines, large-diameter wear debris detection sensors are recommended to meet the actual demand of high throughput. However, when wear debris cuts the magnetic line through the flow path, due to the coupling of gravity, lubricating oil and magnetic field, the wear debris move in a curve, which will have an important effect on the induced voltage of the sensor. Therefore, it is necessary to research the influence of the debris trajectory on the output signal of inductive sensor. A series of experimental was conducted in this paper to research the change of the corresponding induced voltage when the wear debris flows the magnetic field at different angles and the experimental results show that the radial angle affects not only the amplitude of the output signal but also the waveform. Conversely, the axial angle takes barley effect on the output signal.
RED SQUIRREL NESTS IN WITCHES' BROOMS IN DOUGLAS-FIR TREES

We examined a random sample of 105 felled Douglas-fir trees (Pseudotsuga men- ziesii) for nests. Some trees were infected by Douglas-fir dwarf mistletoe (Arceuthobium douglasii) and some were not. Nineteen nests of similar form and construction, judged to be those of red squirrels (Tamiasciurus hudsonicus), were found in 17 infected trees. Most of the trees with nests were moderately to heavily infected with dwarf mistletoe. All nests were found in witches' brooms caused by dwarf mistletoe. Our finding supports the concept that witches' brooms are an ecologically important addition to the structural and/or functional diversity of forest can- opies-