Miracle-dz/UniFAR-FTUs huggingface dataset:
Matched filtering in improper complex noise and applications to DS-CDMA

Based on the criterion of maximizing signal to noise ratio (SNR), the optimum matched filter is, in general, the solution to a real integral equation. With regards to the detection of narrowband, bandpass signals, we present the full form of the integral equation based upon complex envelopes. The correct form of the integral equation includes a complementary or pseudo-autocorrelation function which can be ignored for most matched filtering applications. However, in the presence of improper complex noise, overlooking the function leads to a "non-optimal" matched filter. To illustrate the concept, we apply the theory to a simple direct-sequence code-division multiple-access (DS-CDMA) system.
Performance characteristics of a scanning laser imaging system through atmospheric turbulence

Abstract. The transverse angular (spatial) imaging performance of a laser imaging system through atmospheric turbulence is characterized and described in a practical sense. The system is assumed to scan a far-field scene with a collimated Gaussian beam truncated by a circular aperture and a single channel receiver records the returns. The system point spread function is defined as the average beam profile at the scene and the half-angle beam spread is used to characterize the point spread function. A system modulation transfer function is defined that includes the effects of beam diffraction and turbulence. Spatial sampling of the scene is also considered. A normalized resolution metric (Strehl ratio) is applied to investigate the effects of sampling, beam size, turbulence, and beam truncation. The analysis indicates that resolution will be reduced by more than 90% in homogenous turbulence when the beam waist size is on the order of the atmospheric coherence length. Consistency with conventional imaging performance is discussed.
Formation control of multiple elliptic agents with limited sensing ranges

This paper presents a design of cooperative controllers that force a group of N mobile agents with an elliptic shape and limited sensing ranges to perform a desired formation and that guarantee no collisions between any agents in the group. The desired formation can be stabilized at feasible reference trajectories with bounded time derivatives. The formation control design is based on explicit algebraic separation conditions between ellipses, root conditions of cubic polynomials, the Lyapunov direct method, and smooth or p‐times differentiable step functions. These functions are incorporated into novel potential functions to solve the collision avoidance problem without the need for switchings despite the agents' limited sensing ranges.
Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

Brain–computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.
0.1-/spl mu/m InAlP/InAlAs/InGaAs/InP HEMTs with improved breakdown voltages

InAlP/InAlAs/InGaAs/InP HEMTs that offer improved breakdown voltages and reduced gate leak currents are investigated. The InAlP-layer has a larger etching selectivity compared to InAlAs and InGaAs, which is very effective in Vth control. Moreover, it has a larger Eg and Schottky barrier values than InAlAs and InP. These characteristics are suitable for the HEMT's etch stop layer. A fabricated 0.1-/spl mu/m HEMT exhibits 8.1 V breakdown voltage and 1-nA/10-/spl mu/m gate leak current without any performance tradeoff. These results are enough to expand the application area of InP-based HEMTs.
Reanimation unter Covid-19: Geht persönlicher Schutz auf Kosten der Qualität?

Eine Wiederbelebung kann einen schon unter relativ normalen Umständen fordern. Doch leidet die Qualität, wenn man wegen des Verdachts auf eine Corona-Infektion beim Patienten im Plastikanzug und mit FFP-Schutzmaske arbeiten muss? Forschende aus Wien haben hier jetzt eine gute Nachricht.
Complexity control method for recurrent neural networks

This paper demonstrates that the Lyapunov exponents of recurrent neural networks can be controlled by our proposed methods. One of the control methods minimizes a squared error e/spl lambda/=(/spl lambda/-/spl lambda//sup obj/)/sup 2//2 by a gradient method, where /spl lambda/ is the largest Lyapunov exponent of the network and /spl lambda//sup obj/ is a desired exponent. /spl lambda/ implying the dynamical complexity is calculated by observing the state transition for a long-term period. This method is, however, computationally expensive for large-scale recurrent networks and the control is unstable for recurrent networks with chaotic dynamics since a gradient correction through time diverges due to the chaotic instability. We also propose an approximation method in order to reduce the computational cost and realize a "stable" control for chaotic networks. The new method is based on a stochastic relation which allows us to calculate the correction through time in a fashion without time evolution. Simulation results show that the approximation method can control the exponent for recurrent networks with chaotic dynamics under a restriction.
PLGA Particles in Immunotherapy

Poly(lactic-co-glycolic acid) (PLGA) particles are a widely used and extensively studied drug delivery system. The favorable properties of PLGA such as good bioavailability, controlled release, and an excellent safety profile due to the biodegradable polymer backbone qualified PLGA particles for approval by the authorities for the application as a drug delivery platform in humas. In recent years, immunotherapy has been established as a potent treatment option for a variety of diseases. However, immunomodulating drugs rely on targeted delivery to specific immune cell subsets and are often rapidly eliminated from the system. Loading of PLGA particles with drugs for immunotherapy can protect the therapeutic compounds from premature degradation, direct the drug delivery to specific tissues or cells, and ensure sustained and controlled drug release. These properties present PLGA particles as an ideal platform for immunotherapy. Here, we review recent advances of particulate PLGA delivery systems in the application for immunotherapy in the fields of allergy, autoimmunity, infectious diseases, and cancer.
FAD: Fine-Grained Adversarial Detection by Perturbation Intensity Classification

Adversarial examples present a severe threat to deep neural networks’ application in safetycritical domains such as autonomous driving. Although there are numerous defensive solutions, they all have some flaws, such as the fact that they can only defend against adversarial attacks with a limited range of adversarial intensities. Therefore, there is a need for a detection method that can distinguish the adversarial intensity in a fine-grained manner so that subsequent tasks can perform different defense processing against perturbations of various intensities. Based on thefact that adversarial attack samples of different intensities are significantly different in the highfrequency region, this paper proposes a method to amplify the high-frequency component of the image and input it into the deep neural network based on the residual block structure. To our best knowledge, the proposed method is the first to classify adversarial intensities at a fine-grained level, thus providing an attack detection component for a general AI firewall. Experimental results show that our proposed method not only has advanced performance in AutoAttack detection by perturbation intensity classification, but also can effectively apply to detect examples of unseen adversarial attack methods.
Impact of ecological restoration on ecosystem service trade‐offs: Insight from karst desertification control

Ecological restoration programmes (ERP) contribute to greening and regulation service improvement. However, whether greening exacerbates the social‐ecosystem trade‐offs in a degraded ecoregion remains unclear. In this study, we chose South China karst, a global hotspot of greening (improvement of vegetation), as a case‐study. First, we analyzed the degree of greening in different ecoregions and the contribution of landscape to greening during 2000–2020. Second, we investigated the relationship between greening and indicators (land use, gross domestic product [GDP], and population distribution) and explored the relationship between regulation and provision over time. The results showed that the degree of greening in different ecoregions was different, although ERP played a positive role in greening. The rapid greening in degraded ecoregion was relevant to net forest expansion (26.79% of the conversion area), while the slow greening in a non‐degraded ecoregion was associated with net built‐up land expansion (25.81% of the conversion area). Forest expansion in a degraded ecoregion increased out‐migration and exacerbated the trade‐off between greening and GDP reduction, regulation and provision. Our study found that weak service supply in degraded ecoregion limited farming income, which forced farmers to migrate for work and abandon slopeland. In conclusion, the combination of retiring land and emigrating for work triggers land functional transformation and aggravates social‐ecosystem trade‐offs, but agroforestry can mitigate these trade‐offs. Despite there are limitations in the quantification of population migration, it still provides new insight for the next stage of ERP.
Accelerated Dilatonic-Brans-Dicke cyclic and non-singular universe from string theory

We discuss the late-time dynamics of a particular four-dimensional Brans-Dicke cosmology with a dilaton field motivated by string theories (solitonic p-branes or D-branes) in which the dilaton field and the scale factor of the flat, homogeneous spacetime are correlated. We examine the late-time-evolution of the equations of motion where various attractive consequences are revealed and discussed in some detail.
ChatGPT: A Study of AI Language Processing and its Implications

Conversational AI models like ChatGPT, developed by OpenAI, are a testament to the rapid advancements in artificial intelligence and language processing capabilities. ChatGPT is a language model trained on massive amounts of data, capable of performing a range of language-related tasks, including answering questions, generating text, and even writing poems. Its impressive performance has attracted significant attention from both researchers and industry professionals, leading to its widespread use in a variety of applications. However, the deployment of conversational AI models like ChatGPT raises important ethical and social considerations. There are concerns about the potential for AI systems to perpetuate biases and stereotypes, and the impact they may have on employment. As such, it is crucial that the development and deployment of these models be guided by ethical considerations and principles. The potential benefits of conversational AI models like ChatGPT are significant and far-reaching. In the customer service sector, for example, they can provide 24/7 support and improve the overall customer experience. In the content creation and marketing industries, they can be used to generate high-quality content, freeing up human workers to focus on more creative and strategic tasks. Additionally, they have the potential to revolutionize the way we interact with technology, changing the way we communicate and access information. ChatGPT represents a significant breakthrough in the field of AI language processing, with the potential to transform various industries and improve our lives. However, it is essential that we approach its development and deployment with caution, taking into account the potential ethical and social implications. The continued growth and evolution of conversational AI models like ChatGPT will shape the future of human-computer interaction, and it is up to us to actively monitor and mitigate any adverse consequences. In this research we will be discussing about all of these topics.
Tear Film MicroRNAs as Potential Biomarkers: A Review

MicroRNAs are non-coding RNAs that serve as regulatory molecules in a variety of pathways such as inflammation, metabolism, homeostasis, cell machinery, and development. With the progression of sequencing methods and modern bioinformatics tools, novel roles of microRNAs in regulatory mechanisms and pathophysiological states continue to expand. Advances in detection methods have further enabled larger adoption of studies utilizing minimal sample volumes, allowing the analysis of microRNAs in low-volume biofluids, such as the aqueous humor and tear fluid. The reported abundance of extracellular microRNAs in these biofluids has prompted studies to explore their biomarker potential. This review compiles the current literature reporting microRNAs in human tear fluid and their association with ocular diseases including dry eye disease, Sjögren’s syndrome, keratitis, vernal keratoconjunctivitis, glaucoma, diabetic macular edema, and diabetic retinopathy, as well as non-ocular diseases, including Alzheimer’s and breast cancer. We also summarize the known roles of these microRNAs and shed light on the future progression of this field.
Disturbance rejection with saturating actuators for discrete-time linear systems

Several analysis and design problems related to disturbance rejection with saturating actuators are studied for linear discrete-time systems. Conditions are derived for set invariance in the presence of persistent disturbance. Based on these conditions, optimization approaches are proposed for maximizing/minimizing invariant ellipsoid and for disturbance rejection with guaranteed stability requirements.
Representative Subsets for Preference Queries

We focus on the two overlapping areas of preference queries a nd d taset summarization. A (linear) preference query specifies the relative importan ce of the attributes in a dataset and asks for the tuples that best match those preferences. Da taset summarization is the task of representing an entire dataset by a small, representativ e subset. Within these areas, we focus on three important sub-problems, significantly advan cing the state-of-the-art in each. We begin with an investigation into a new formulation of pref er nce queries, identifying a neglected and important subclass that we call threshold projection queries . While literature typically constrains the attribute preference s (which are real-valued weights) such that their sum is one, we show that this introduces bias when q u rying by threshold rather than cardinality. Using projection, rather than inner prod uct as in that literature, removes the bias. We then give algorithms for building and querying i dices for this class of query, based, in the general case, on geometric duality and halfspa ce range searching, and, in an important special case, on stereographic projection.
Genomic SELEX Reveals Pervasive Role of the Flagella Master Regulator FlhDC in Carbon Metabolism

Flagella are vital bacterial organs that allow microorganisms to move to favorable environments. However, their construction and operation consume a large amount of energy. The master regulator FlhDC mediates all flagellum-forming genes in E. coli through a transcriptional regulatory cascade, the details of which remain elusive. In this study, we attempted to uncover a direct set of target genes in vitro using gSELEX-chip screening to re-examine the role of FlhDC in the entire E. coli genome regulatory network. We identified novel target genes involved in the sugar utilization phosphotransferase system, sugar catabolic pathway of glycolysis, and other carbon source metabolic pathways in addition to the known flagella formation target genes. Examining FlhDC transcriptional regulation in vitro and in vivo and its effects on sugar consumption and cell growth suggested that FlhDC activates these new targets. Based on these results, we proposed that the flagella master transcriptional regulator FlhDC acts in the activation of a set of flagella-forming genes, sugar utilization, and carbon source catabolic pathways to provide coordinated regulation between flagella formation, operation and energy production.
On the stability of steady finite amplitude convection

The static state of a horizontal layer of fluid heated from below may become unstable. If the layer is infinitely large in horizontal extent, the Boussinesq equations admit many different steady solutions. A systematic method is presented here which yields the finite-amplitude steady solutions by means of successive approximations. It turns out that not every solution of the linear problem is an approximation to the non-linear problem, yet there are still an infinite number of finite amplitude solutions. A similar procedure has been applied to the stability problem for these steady finite amplitude solutions with the result that three-dimensional solutions are unstable but there is a class of two-dimensional flows which are stable. The problem has been treated for both rigid and free boundaries.
The effect of brand image and product knowledge on purchase intentions with e-WOM as a mediator variable

This study aims to explain the effect of product knowledge, brand image, e-WOM on purchase intentions for edtech products in Indonesia. Data were obtained using a closed questionnaire involving 230 respondents, from Indonesians who know about edtech startup products and are between 18 and 35 years old. The sampling technique used was convenience sampling. The analysis technique used is Structural Equation Modeling (SEM) and uses the help of the AMOS version 24 program. The results show that 1) product knowledge has a positive but not significant effect on purchase intention, 2) product knowledge and brand image have a positive and significant effect on e-commerce WOM, 3) brand image and e-WOM have a positive and significant effect on purchase intention. It was also found that e-WOM functions as a mediator variable from the effect of product knowledge and brand image on purchase intention. The effect of the e-WOM mediator is stronger than the direct effect of brand image on purchase intention.
Unconstrained Lagrangian Variational Principles for the Einstein Field Equations

This paper deals with the problem of establishing a systematic theoretical formulation of variational principles for the continuum gravitational field dynamics of classical General Relativity (GR). In this reference, the existence of multiple Lagrangian functions underlying the Einstein field equations (EFE) but having different physical connotations is pointed out. Given validity of the Principle of Manifest Covariance (PMC), a set of corresponding variational principles can be constructed. These are classified in two categories, respectively, referred to as constrained and unconstrained Lagrangian principles. They differ for the normalization properties required to be satisfied by the variational fields with respect to the analogous conditions holding for the extremal fields. However, it is proved that only the unconstrained framework correctly reproduces EFE as extremal equations. Remarkably, the synchronous variational principle recently discovered belongs to this category. Instead, the constrained class can reproduce the Hilbert–Einstein formulation, although its validity demands unavoidably violation of PMC. In view of the mathematical structure of GR based on tensor representation and its conceptual meaning, it is therefore concluded that the unconstrained variational setting should be regarded as the natural and more fundamental framework for the establishment of the variational theory of EFE and the consequent formulation of consistent Hamiltonian and quantum gravity theories.
Pharmacological Modulations of Nrf2 and Therapeutic Implications in Aneurysmal Subarachnoid Hemorrhage

An aneurysmal subarachnoid hemorrhage (aSAH) is a subtype of stroke with high morbidity and mortality. The main causes of a poor prognosis include early brain injury (EBI) and delayed vasospasm, both of which play a significant role in the pathophysiological process. As an important mechanism of EBI and delayed vasospasm, oxidative stress plays an important role in the pathogenesis of aSAH by producing reactive oxygen species (ROS) through the mitochondria, hemoglobin, or enzymatic pathways in the early stages of aSAH. As a result, antioxidant therapy, which primarily targets the Nrf2-related pathway, can be employed as a potential strategy for treating aSAH. In the early stages of aSAH development, increasing the expression of antioxidant enzymes and detoxifying enzymes can relieve oxidative stress, reduce brain damage, and improve prognosis. Herein, the regulatory mechanisms of Nrf2 and related pharmacological compounds are reviewed, and Nrf2-targeted drugs are proposed as potential treatments for aSAH.