Miracle-dz/UniFAR-FTUs huggingface dataset:
Airmed: Efficient Self-Healing Network of Low-End Devices

The proliferation of application specific cyber-physical systems coupled with the emergence of a variety of attacks on such systems (malware such as Mirai and Hajime) underlines the need to secure such networks. Most existing security efforts have focused on only detection of the presence of malware. However given the ability of most attacks to spread through the network once they infect a few devices, it is important to contain the spread of a virus and at the same time systematically cleanse the impacted nodes using the communication capabilities of the network. Toward this end, we present Airmed - a method and system to not just detect corruption of the application software on a IoT node, but to self correct itself using its neighbors. Airmed's decentralized mechanisms prevent the spread of self-propagating malware and can also be used as a technique for updating application code on such IoT devices. Among the novelties of Airmed are a novel bloom-filter technique along with hardware support to identify position of the malware program from the benign application code, an adaptive self-check for computational efficiency, and a uniform random-backoff and stream signatures for secure and bandwidth efficient code exchange to correct corrupted devices. We assess the performance of Airmed, using the embedded systems security architecture of TrustLite in the OMNeT++ simulator. The results show that Airmed scales up to thousands of devices, ensures guaranteed update of the entire network, and can recover 95% of the nodes in 10 minutes in both internal and external propagation models. Moreover, we evaluate memory and communication costs and show that Airmed is efficient and incurs very low overhead.
Construction, expression and characterization of human interferon α2b-（G4S）n-thymosin α1 fusion proteins in Pichia pastoris

AIM: Interferon α2b (IFNα2b) and thymosin α1 (Tα1) exhibit synergic effects in the treatment of hepatitis B and hepatitis C when used together. For developing a fusion protein drug, fusion proteins of IFNα2b and Tα1 linked by different lengths of (G4S)n (n = 1-3) were constructed and expressed in Pichia pastoris.METHODS: Using PCR and molecular clone techniques, the fusion genes of IFNα2b-(G4S)n-Tα1 (n = 1-3) were constructed and subcloned into the eukaryotic expression vector pPIC9. After transformation of these plasmids into P. pastoris, the expressed fusion proteins IFNα2b-(G4S)n-Tα1 (n = 1-3) were obtained. These proteins were purified through diethylaminoethyl (DEAE) affinity chromatography and SuperdexTM 75 gel filtration and analyzed by SDSPAGE and Western blot. Antiviral and E-rosette assays were used to investigate the bioactivities of these fusion proteins.RESULTS: DNA sequencing confirmed that the fusion genes of IFNα2b-(G4 S)n-Tα1 (n = 1-3) were correctly cloned to the pPIC9 vector. The recombinant IFNα2b(G4 S)n-Tα1 (n = 1-3) fusion proteins expressed in P. pastoris were purified with DEAE and SuperdexTM 75 gel filtration chromatography. The fusion proteins could be observed on sodium dodecylsulfate-polyacrylamide gel electrophoresis with molecular weight (MW) of 23.2, 22.9, and 22.6 ku, respectively, and reacted to the IFNα2b monoclonal antibody and Tα1 polyclonal antibody. The purified fusion proteins exhibit antiviral activity and can enhance the percentage of E-rosette-forming-cell in E-rosette assay.CONCLUSION: The recombinant IFNα2b-(G4S)n-Tα1 (n = 1-3) fusion proteins were successfully expressed in P. pastoris. Purified fusion proteins exhibit both antiviral activity of IFNα2b and immunomodulatory activity of Tα1 in vitro. These results will be the basis for further evaluation of the fusion proteins' function in vivo.
MCQA: Multimodal Co-attention Based Network for Question Answering

We present MCQA, a learning-based algorithm for multimodal question answering. MCQA explicitly fuses and aligns the multimodal input (i.e. text, audio, and video), which forms the context for the query (question and answer). Our approach fuses and aligns the question and the answer within this context. Moreover, we use the notion of co-attention to perform cross-modal alignment and multimodal context-query alignment. Our context-query alignment module matches the relevant parts of the multimodal context and the query with each other and aligns them to improve the overall performance. We evaluate the performance of MCQA on Social-IQ, a benchmark dataset for multimodal question answering. We compare the performance of our algorithm with prior methods and observe an accuracy improvement of 4-7%.
"Unsex me here": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples

To effectively tackle sexism online, research has focused on automated methods for detecting sexism. In this paper, we use items from psychological scales and adversarial sample generation to 1) provide a codebook for different types of sexism in theory-driven scales and in social media text; 2) test the performance of different sexism detection methods across multiple data sets; 3) provide an overview of strategies employed by humans to remove sexism through minimal changes. Results highlight that current methods seem inadequate in detecting all but the most blatant forms of sexism and do not generalize well to out-of-domain examples. By providing a scale-based codebook for sexism and insights into what makes a statement sexist, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.
Kodaira dimensions of almost complex manifolds II

This is the second of a series of papers where we study the plurigenera, the Kodaira dimension and the Iitaka dimension on compact almost complex manifolds. By using the pseudoholomorphic pluricanonical map, we define the second version of Kodaira dimension as well as Iitaka dimension on compact almost complex manifolds. We show the almost complex structures with the top Kodaira dimension are integrable. For compact almost complex 4-manifolds with Kodaira dimension one, we obtain elliptic fibration like structural description. Some vanishing theorems in complex geometry are generalized to the almost complex setting. For tamed symplectic $4$-manifolds, we show that the almost Kodaira dimension is bounded above by the symplectic Kodaira dimension, by using a probabilistic combinatorics style argument. The appendix also contains a few results including answering a question of the second author that there is a unique subvariety in exceptional curve classes for irrational symplectic $4$-manifolds, as well as extending Evans-Smith's constraint on symplectic embeddings of certain rational homology balls by removing the assumption on the intersection form.
Relational Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.
The “hidden” companion in LB-1 unveiled by spectral disentangling

Context. The intriguing binary LS V +22 25 (LB-1) has drawn much attention following claims of it being a single-lined spectroscopic binary with a 79-day orbit comprising a B-type star and a ≈70 M⊙ black hole – the most massive stellar black hole reported to date. Subsequent studies demonstrated a lack of evidence for a companion of such great mass. Recent analyses have implied that the primary star is a stripped He-rich star with peculiar sub-solar abundances of heavy elements, such as Mg and Fe. However, the nature of the secondary, which was proposed to be a black hole, a neutron star, or a main sequence star, remains unknown. Aims. Based on 26 newly acquired spectroscopic observations secured with the HERMES and FEROS spectrographs covering the orbit of the system, we perform an orbital analysis and spectral disentangling of LB-1 to elucidate the nature of the system. Methods. To derive the radial velocity semi-amplitude K2 of the secondary and extract the spectra of the two components, we used two independent disentangling methods: the shift-and-add technique and Fourier disentangling with FDBinary. We used atmosphere models to constrain the surface properties and abundances. Results. Our disentangling and spectral analysis shows that LB-1 contains two components of comparable brightness in the optical. The narrow-lined primary, which we estimate to contribute ≈55% in the optical, has spectral properties that suggest that it is a stripped star: it has a small spectroscopic mass (≈1 M⊙) for a B-type star and it is He- and N-rich. Unlike previous reports, the abundances of heavy elements are found to be solar. The “hidden” secondary, which contributes about 45% of the optical flux, is a rapidly rotating (vsini ≈ 300 km s−1) B3 V star with a decretion disk – a Be star. As a result of its rapid rotation and dilution, the photospheric absorption lines of the secondary are not readily apparent in the individual observations. We measure a semi-amplitude for this star of K2 = 11.2 ± 1.0 km s−1 and adopting a mass of M2 = 7 ± 2 M⊙ typical for B3 V stars, we derive an orbital mass for the stripped primary of M1 = 1.5 ± 0.4 M⊙. The orbital inclination of 39 ± 4° implies a near-critical rotation for the Be secondary (veq ≈ 470 km s−1). Conclusions. LB-1 does not contain a compact object. Instead, it is a rare Be binary system consisting of a stripped star (the former mass donor) and a Be star rotating at near its critical velocity (the former mass accretor). This system is a clear example that binary interactions play a decisive role in the production of rapid stellar rotators and Be stars.
Evolutionary Stochastic Policy Distillation

Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging reinforcement learning problem due to the sparsity of reward signals. In this work, we propose a new formulation of GCRS tasks from the perspective of the drifted random walk on the state space, and design a novel method called Evolutionary Stochastic Policy Distillation (ESPD) to solve them based on the insight of reducing the First Hitting Time of the stochastic process. As a self-imitate approach, ESPD enables a target policy to learn from a series of its stochastic variants through the technique of policy distillation (PD). The learning mechanism of ESPD can be considered as an Evolution Strategy (ES) that applies perturbations upon policy directly on the action space, with a SELECT function to check the superiority of stochastic variants and then use PD to update the policy. The experiments based on the MuJoCo robotics control suite show the high learning efficiency of the proposed method.
Predictions of the L [C ii]–SFR and [Cii] Luminosity Function at the Epoch of Reionization

We present the first predictions for the L [C ii]–SFR relation and [Cii] luminosity function (LF) in the epoch of reionization (EOR) based on cosmological hydrodynamics simulations using the simba suite plus radiative transfer calculations via sígame. The sample consists of 11,137 galaxies covering halo mass log M halo ∈ [9, 12.4] M ⊙, star formation rate SFR ∈ [0.01, 330] M ⊙ yr−1, and metallicity 〈Z gas〉SFR ∈ [0.1, 1.9] Z ⊙. The simulated L [C ii]–SFR relation is consistent with the range observed, but with a spread of ≃0.3 dex at the high end of SFR (>100 M ⊙ yr−1) and ≃0.6 dex at the lower end, and there is tension between our predictions and the values of L [C ii] above 108.5 L ⊙ observed in some galaxies reported in the literature. The scatter in the L [C ii]–SFR relation is mostly driven by galaxy properties, such that at a given SFR galaxies with higher molecular mass and metallicity have higher L [C ii]. The [Cii] LF predicted by simba is consistent with the upper limits placed by the only existing untargeted flux-limited [Cii] survey at the EOR and those predicted by semianalytic models. We compare our results with existing models and discuss the differences responsible for the discrepant slopes in the L [C ii]–SFR relation.
Hyperspectral image classification based on multi-scale residual network with attention mechanism

Compared with traditional machine learning methods, deep learning methods such as convolutional neural networks (CNNs) have achieved great success in the hyperspectral image (HSI) classification task. HSI contain abundant spatial and spectral information, but they also contain a lot of invalid information, which may introduce noises and weaken the performance of CNNs. In order to make full use of the useful information in HSI, we propose a multi-scale residual network integrated with attention mechanism (MSRN-A) for HSI classification in this letter. In our method, we built two different multi-scale feature extraction blocks to extract the joint spatial-spectral features and the advanced spatial features, respectively. Moreover, a spatial-spectral attention module and a spatial attention module were set up to focus on the salient spatial parts and valid spectral information. Experimental results demonstrate that our method achieves high accuracy on the Indian Pines, Pavia University and Salinas datasets. The source code can be found at https://github.com/XiangdongZ/MSRN-A.
Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems

Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However, conventional HBF methods are still with high complexity and strongly rely on the quality of channel state information. We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Specifically, to provide accurate labels for training, we first propose an factional-programming and majorization-minimization based HBF method (FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with existing methods. Moreover, ELM-HBF cannot only provide robust HBF performance, but also consume very short computation time.
Investigation of turbulent transport regimes in the tokamak edge by using two-fluid simulations

The results of flux-driven, two-fluid simulations in single-null configurations are used to investigate the processes determining the turbulent transport in the tokamak edge. Three turbulent transport regimes are identified: (i) a developed transport regime with turbulence driven by an interchange instability, which shares a number of features with the standard L-mode of tokamak operation; (ii) a suppressed transport regime, characterized by a higher value of the energy confinement time, low-amplitude relative fluctuations driven by a Kelvin–Helmholtz instability, a strong $\boldsymbol {E}\times \boldsymbol {B}$ sheared flow and the formation of a transport barrier, which recalls the H-mode; and (iii) a degraded confinement regime, characterized by a catastrophically large interchange-driven turbulent transport, which recalls the crossing of the Greenwald density limit. We derive an analytical expression of the pressure gradient length in the three regimes. The transition from the developed transport regime to the suppressed transport regime is obtained by increasing the heat source or decreasing the collisionality and vice versa for the transition from the developed transport regime to the degraded confinement regime. An analytical expression of the power threshold to access the suppressed transport regime, linked to the power threshold for H-mode access, as well as the maximum density achievable before entering the degraded confinement regime, related to the Greenwald density, are also derived. The experimental dependencies of the power threshold for H-mode access on density, tokamak major radius and isotope mass are retrieved. The analytical estimate of the density limit contains the correct dependence on the plasma current and on the tokamak minor radius.
The Adversarial Stackelberg Value in Quantitative Games

In this paper, we study the notion of adversarial Stackelberg value for two-player non-zero sum games played on bi-weighted graphs with the mean-payoff and the discounted sum functions. The adversarial Stackelberg value of Player 0 is the largest value that Player 0 can obtain when announcing her strategy to Player 1 which in turn responds with any of his best response. For the mean-payoff function, we show that the adversarial Stackelberg value is not always achievable but epsilon-optimal strategies exist. We show how to compute this value and prove that the associated threshold problem is in NP. For the discounted sum payoff function, we draw a link with the target discounted sum problem which explains why the problem is difficult to solve for this payoff function. We also provide solutions to related gap problems.
Some Relations of Two Type 2 Polynomials and Discrete Harmonic Numbers and Polynomials

Harmonic numbers appear, for example, in many expressions involving Riemann zeta functions. Here, among other things, we introduce and study discrete versions of those numbers, namely the discrete harmonic numbers. The aim of this paper is twofold. The first is to find several relations between the Type 2 higher-order degenerate Euler polynomials and the Type 2 high-order Changhee polynomials in connection with the degenerate Stirling numbers of both kinds and Jindalrae–Stirling numbers of both kinds. The second is to define the discrete harmonic numbers and some related polynomials and numbers, and to derive their explicit expressions and an identity.
Necessary and Sufficient Conditions for Frequency-Based Kelly Optimal Portfolio

In this letter, we consider a discrete-time portfolio with <inline-formula> <tex-math notation="LaTeX">$m \geq 2$ </tex-math></inline-formula> assets optimization problem which includes the <italic>rebalancing frequency</italic> as an additional parameter in the maximization. The so-called <italic>Kelly Criterion</italic> is used as the performance metric; i.e., maximizing the expected logarithmic growth of a trader’s account, and the portfolio obtained is called the frequency-based Kelly optimal portfolio. The focal point of this letter is to extend upon the results of our previous work to obtain various optimality characterizations on the portfolio. To be more specific, using Kelly’s criterion in our new frequency-based formulation, we first prove necessary and sufficient conditions for the frequency-based Kelly optimal portfolio. With the aid of these conditions, we then show several new optimality characterizations such as <italic>expected ratio optimality</italic> and <italic>asymptotic relative optimality</italic>, and a result which we call the Extended Dominant Asset Theorem. That is, we prove that the <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>th asset is <italic>dominant</italic> in the portfolio if and only if the Kelly optimal portfolio consists of that asset only. The word “extended” on the theorem comes from the fact that it was only a sufficiency result that was proved in our previous work. Hence, in this letter, we improve it to involve a proof of the necessity part. In addition, the trader’s survivability issue (no bankruptcy consideration) is also studied in detail in our frequency-based trading framework. Finally, to bridge the theory and practice, we propose a simple trading algorithm using the notion called <italic>dominant asset condition</italic> to decide when should one triggers a trade. The corresponding trading performance using historical price data is reported as supporting evidence.
Computation on Sparse Neural Networks: an Inspiration for Future Hardware

Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are very computationally intensive and reach the hardware limit of the existing server and IoT devices. Thus, finding better model architectures with much less amount of computation while maximally preserving the accuracy is a popular research topic. Among various mechanisms that aim to reduce the computation complexity, identifying the zero values in the model weights and in the activations to avoid computing them is a promising direction. In this paper, we summarize the current status of the research on the computation of sparse neural networks, from the perspective of the sparse algorithms, the software frameworks, and the hardware accelerations. We observe that the search for the sparse structure can be a general methodology for high-quality model explorations, in addition to a strategy for high-efficiency model execution. We discuss the model accuracy influenced by the number of weight parameters and the structure of the model. The corresponding models are called to be located in the weight dominated and structure dominated regions, respectively. We show that for practically complicated problems, it is more beneficial to search large and sparse models in the weight dominated region. In order to achieve the goal, new approaches are required to search for proper sparse structures, and new sparse training hardware needs to be developed to facilitate fast iterations of sparse models.
Induction and in vitro Proliferation of Adventitious Roots in Phyllanthus urinaria

Phyllanthus urinaria was an important species in Korea and distributed in all around of Korea. The roots and stems of this plant have been used for natural medicine for the treatment of diabetes, the hepatitis B virus and disturbances of the kidney and urinary bladder. Production of adventitious roots in P. urinaria by in vitro cultures could be used as alternatives materials. Shoot and root segments from P. urinaria seedling were cultured on Murashige and Skoog (MS) medium supplemented with 3.0 mg/L IBA and 30 g/L sucrose. After 4 weeks of culture, the highest induction of adventitious roots was obtained from the shoot part. Frequency of adventitious root formation on medium with various kinds of auxins (IAA, NAA, 2,4-D, and IBA) and various concentrations of IBA (0, 0.1, 0.5, 1.0, 3.0, and 5.0 mg/L) was tested. The maximun induction of adventitious root was obtained on medium with 0.5 mg/L IBA. In liquid culture, growth of root was best on medium supplemented with 30 g/L sucrose. Adventitious roots were cultured in 5 L bioreactor containing 1/2 MS medium supplemented with 0.5 mg/L IBA and 30 g/L sucrose and mass-production of adventitious roots was successfully achieved. These results revealed the first attempt for the production of adventitious roots in P. urinaria.