Miracle-dz/UniFAR-FTUs huggingface dataset:
An Energy Efficient Coherent IR-UWB Receiver With Non-Coherent-Assisted Synchronization

This paper presents a non-coherent assisted synchronization mechanism for low data rate coherent impulse radio ultra-wide band (IRUWB) receivers. A two-step coarse and fine acquisition mechanism is utilized to simplify the coherent synchronization and minimize the total required packet length. This hybrid scheme reduces the total power consumption of the receiver. The reception of a UWB packet begins with an energy efficient non-coherent synchronization portion in on-off keying (OOK) modulation non-coherent acquisition. This reduces the search space for the coherent reception by first finding the best integration window that has the most energy of the received signal. Afterwards, the receiver searches coherently in binary phase-shift keying (BPSK) modulation within only the pre-selected integration window instead of the whole symbol time. Self-mixing and template-based correlation are utilized for the non-coherent and coherent reception, respectively. Most of the front-end blocks are shared between the coherent and non-coherent modes, including the LNA, mixer, and the baseband circuitry that follows to minimize the total power consumption of the receiver. A differential architecture including a low noise amplifier (LNA), a mixer, a fast start-up template generator, an integrator, and a differential comparator are utilized for the receiver front-end. A prototype of the proposed receiver operating from 3.5 to 5 GHz over four bands, each spaced by 500 MHz, is implemented in 65-nm CMOS technology. The proposed receiver architecture achieves a −68 dBm, −70.5 dBm, and −70.8 dBm sensitivity at a 10−3 BER at 50 MHz pulse repetition frequency (PRF) in the non-coherent, coherent, and proposed hybrid synchronization modes, respectively. The receiver consumes 6.8 mW and 8.8 mW in the non-coherent and coherent mode, respectively, when continuously ON. Using its novel synchronization mechanism, the receiver can reduce the ON time of the receiver by 68% and 34% in order to coherently synchronize and receive a 100-bit and 1024-bit payload, respectively. As a result, the energy per useful bit (EPUB) of the receiver is reduced by a factor of 2.9 from 1.24 nJ/b to 422 pJ/b for 100-bit payloads, and by a factor of 1.5 from 309 pJ/b to 204 pJ/b for 1024-bit payloads, increasing energy efficiency while maintaining the sensitivity benefits of coherent detection.
Bayes, Bounds, and Rational Analysis

While Bayesian models have been applied to an impressive range of cognitive phenomena, methodological challenges have been leveled concerning their role in the program of rational analysis. The focus of the current article is on computational impediments to probabilistic inference and related puzzles about empirical confirmation of these models. The proposal is to rethink the role of Bayesian methods in rational analysis, to adopt an independently motivated notion of rationality appropriate for computationally bounded agents, and to explore broad conditions under which (approximately) Bayesian agents would be rational. The proposal is illustrated with a characterization of costs inspired by thermodynamics.
Deriving the Vertical Variations in the Diffuse Attenuation Coefficient of Photosynthetically Available Radiation in the North Pacific Ocean from Remote Sensing

Diffuse attenuation coefficient of photosynthetically available radiation (PAR), KPAR, is a key product of ocean color remote sensing. Current ocean color algorithms generally detect only the average KPAR within one optical depth, KPARRS. Due to the marked vertical variations of KPAR, knowledge of KPARRS is insufficient to accurately evaluate the submarine light field. By using field in situ observations, a two-step approach, based on the development of an ocean color algorithm for KPARRS and the relationships between KPARRS and the average KPAR from the surface down to depth Z (K¯PARZ), was developed to remotely estimate the vertical variations in K¯PARZ in the North Pacific from the MODerate-resolution Imaging Spectrometer (MODIS) imagery. The root mean square difference of log(K¯PARZ) in depths within the euphotic zone was around ±0.059 (in unit of m−1 for K¯PARZ), which corresponded to a deviation of ±15% for the estimated K¯PARZ and the penetration depths of PAR. Our study may provide a promising approach to detect the vertical variations of K¯PARZ and underwater PAR distributions in the North Pacific Ocean.
On ridge regression and least absolute shrinkage and selection operator

This thesis focuses on ridge regression (RR) and least absolute shrinkage and selection operator (lasso). Ridge properties are being investigated in great detail which include studying the bias, the variance and the mean squared error as a function of the tuning parameter. We also study the convexity of the trace of the mean squared error in terms of the tuning parameter. In addition, we examined some special properties of RR for factorial experiments. Not only do we review ridge properties, we also review lasso properties because they are somewhat similar. Rather than shrinking the estimates toward zero in RR, the lasso is able to provide a sparse solution, setting many coefficient estimates exaclty to zero. Furthermore, we try a new approach to solve the lasso problem by formulating it as a bilevel problem and implementing a new algorithm to solve this bilevel program.
PRACH in Self-Powered Satellite Internet of Things: Low-Cubic Metric Preamble Design and Detection

This article focuses on the energy-efficient physical random access channel (PRACH) design for self-powered sensors integrated satellite Internet of Things (S-IoT), where two novel preamble design schemes are proposed by jointly considering cubic metric (CM) reduction, coverage enhancement, resistance to carrier frequency offset (CFO), and low-complexity detection. Specifically, both the schemes possess a uniform preamble format by repeatedly cascading a series of conjugate Zadoff–Chu sequence pairs in the frequency domain, which can effectively mitigate the adverse effect of large CFO under the premise of ensuring PRACH coverage. Furthermore, phase rotation optimization and inverse discrete Fourier transform (IDFT) scaling are, respectively, leveraged in the two schemes to cope with the problem of CM performance degradation caused by sequence repetitions. We also develop the corresponding preamble detection mechanisms that are capable of performing joint timing advance (TA) and CFO estimation and conduct mathematical analysis in terms of CM and maximum coupling loss. Simulation results verify the feasibility of our schemes in self-powered S-IoT and demonstrate that with the increment of the number of sequence repetitions, the proposed schemes can maintain relatively lower CM values in comparison to the state-of-the-art ones, while having significantly enhanced PRACH coverage and detection performance.
Accurate Link Prediction Metric Based on Node Centrality and LSBC

- During the last two decades, there has been a lot of interest in social network analysis. These networks are dynamic, with new links appearing and disappearing all the time. The challenge of suggesting future links from the current state of the network is recognized as link prediction. We calculate user similarity using information from nodes and edges. The more similar two users are, the more likely they will connect. In the domain of link prediction, similarity measures are quite essential. Many authors have suggested and analyzed numerous metrics, such as Jaccard, AA, and Katz, because of their simplicity and flexibility. In this work, we extend a new parameterized approach [21] to enhance the AUC value of link prediction metrics by combining them with eigenvector centrality. This work proposes to enhance local similarity metrics due to their interpretability and low complexity. Experiments reveal that the suggested technique outperforms the state-of-the-art metrics in terms of AUC, and it also outperforms the LSBC metric in terms of time. In addition to that, we have used machine learning algorithms to solve the link prediction problem as a classification task.
Transcriptome Analysis of Embryogenic and Non-Embryogenic Callus of Picea Mongolica

Picea mongolica is a rare tree species in China, which is of great significance in combating desertification and improving the harsh ecological environment. Due to the low rate of natural regeneration, high mortality, and susceptibility to pests and cold springs, Picea mongolica has gradually become extinct. At present, somatic embryogenesis (SE) is the most effective method of micro-proliferation in conifers, but the induction rate of embryogenic callus (EC) is low, and EC is difficult to differentiate from non-embryonic callus (NEC). Therefore, the EC and NEC of Picea mongolica were compared from the morphology, histological, physiological, and transcriptional levels, respectively. Morphological observation showed that the EC was white and transparent filamentous, while the NEC was compact and brownish-brown lumpy. Histological analyses showed that the NEC cells were large and loosely arranged; the nuclei attached to the edge of the cells were small; the cytoplasm was low; and the cell gap was large and irregular. In the EC, small cells, closely arranged cells, and a large nucleus and nucleolus were observed. Physiological studies showed significant differences in ROS-scavenging enzymes between the EC and NEC. Transcriptome profiling revealed that 13,267 differentially expressed genes (DEGs) were identified, 3682 were up-regulated, and 9585 were down-regulated. In total, 63 GO terms had significant enrichment, 32 DEGs in plant hormone signal transduction pathway were identified, and 502 different transcription factors (TFs) were characterized into 38 TF families. Meanwhile, we identified significant gene expression trends associated with somatic embryo development in plant hormones (AUX/IAA, YUCCA, LEA, etc.), stress (GST, HSP, GLP, etc.), phenylpropanoid metabolism (4CL, HCT, PAL, etc.), and transcription factors (AP2/ERF, MYB, WOX, etc.). In addition, nine genes were chosen for RT-qPCR, and the results were consistent with RNA-Seq data. This study revealed the changes in morphology, histology, physiology, and gene expression in the differentiation of NEC into EC and laid the foundation for finding the key genes to promote EC formation.
Income Over Feed Cost and Efficiency Protein Ratio (PER) BoerPE Goats Through Different Content of Crude Protein Levels

Penerapan teknologi pakan yang saat ini terus diterapkan dan mutlak diperlukan dalam rangka meningkatkan efisiensi produksi, terutama pada kambing boePE. Tujuan penelitian ini adalah untuk menganalisis pengaruh tingkat kandungan protein pada pakan lengkap terhadap kinerja produksi, nilai ekonomi dan rasio efisiensi protein. Materi yang digunakan dalam penelitian ini adalah 9 ekor bangsa kambing BoerPE berumur 8 bulan–13 bulan dengan bobot badan 20,8 kg–34,6 kg dan dibagi dalam kelompok kecil rataan bobot badan =21,8 kg sedang = 28,4 kg dan besar 31,7 kg, bahan pakan yang digunakan adalah rumput gajah, kulit kopi, jagung kuning giling, onggok, tetes, bekatul, roti kering, bungkil kedelai, bungkil kelapa dan pollard. Penelitian ini menggunakan metode percobaan rancangan acak kelompok (RAK) dengan 3 perlakuan dan 3 kelompok berdasarkan bobot badan yang diberikan pakan komplit dengan tingkat kandungan PK yang berbeda yaitu P1= PK 11%, P2= PK 13% dan P3= PK 15%. Variabel yang diamati dalam penelitian ini adalah konsumsi pakan, pertambahan bobot badan, konversi pakan, rasio protein efisiensi, dan analisis ekonomi. Data yang diperoleh ditabulasi menggunakan uji ANOVA dengan uji lanjut Uji Duncan. Hasil penelitian menunjukan bahwa tingkat kandungan PK dalam pakan komplit berpengaruh nyata (p<0,01) terhadap konsumsi pakan, pertambahan bobot badan dan konversi pakan serta Income Over Feed Cost (IOFC). Sedangkan tidak berpengaruh nyata (p<0,05) terhadap Protein Efficiency Ratio (PER). Hasil tersebut dapat disimpulkan bahwa protein kasar dalam pakan juga menunjukkan kualitas pakan kambing BoePE dimana efisiensi rasio protein menunjukkan peningkatan dan dengan perlakuan protein kasar pada taraf 15% memberikan nilai keuntungan yang baik.
FRACTURE-FAULTDETECTIONUSING DEEP LEARNING WITH STEPWISE ELIMINATION FROM SATELLITE IMAGES IN DJIBOUTI

: Accurate estimation of groundwater flow is crucial in arid regions where permanent surface water is absent. In several groundwater simulation models, an important parameter for identifying areas with high potential for groundwater resources is the accurate fracture-fault detection. In the present study we propose a deep learning approach to detect fracture-fault structures in the Ali Faren sub-catchment of Ambouli Wadi in Djibouti. Our deep convolutional neural network (Deep-CNN) model is trained on high-spatial resolution multispectral satellite images using wadi streamline as labels. Fracture-fault structures are extracted using stepwise elimination based on geological characteristics observed in relief images derived from PALSAR-1/2 data. Our results demonstrate that the proposed Deep-CNN model accurately detects fracture-fault lines, achieving a validation accuracy of 0.9684, precision of 0.9124, recall of 0.9701, and F1 of 0.8997. The proposed model has the potential to identify potential areas for groundwater resources across the country, contributing to sustainable water management and improving Djibouti's water security. Our study highlights the potential of deep learning techniques in addressing challenges related to sustainable water management in arid regions.
Conditionally unbiased estimation in the normal setting with unknown variances

ABSTRACT To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes.
Assessing the Relevance and Adequacy of the Performance Indicators to the School Strategic Goals and Evaluation Report in a K-12 School1

This study assessed the relevance and adequacy of the performance indicators to the strategic goals and evaluation report of one private school in Sharjah, UAE. It employs one case study framed after the PDSA model. Coding was used as a technique to content analyze texts from the school's BSC and further supported by a review of the minutes of the meeting and interview of the selected school leaders who have a hand in the performance monitoring. Research revealed that the performance indicators mapped out in the BSC are relevant, however, there are missing indicators for some educational factors identified in the school evaluation report. These are SOD learning achievement, lesson delivery, and planning, participation of the governing body, and performance monitoring by the school subject leaders. When these factors are not monitored, it will affect learning equity, internal locus of control, strategic leadership, and accountability. School leaders need to supply these factors with relevant indicators, make the entire community aware of the performance measures and allow other school leaders to create their unique indicators being monitored regularly to support the organization's success.
A wideband dual-polarized patch antenna fed with the aperture-coupled microstrip

ABSTRACT In this paper, a compact broadband dual-polarized stacked patch antenna with the aperture coupled microstrip feed is proposed. By adding a parasitic disc patch, the bandwidth is effectively broadened. Meanwhile, to improve the isolation at the low frequency band, a rectangular slot is etched along the diagonal line of the ground plane. The measured results show that the proposed antenna can operate from 9.6 to 22.4 GHz and the relative bandwidth is 80%. Also, the isolation within the operating frequency band is better than 15 dB and the maximum gain reaches 4.08 dBi.
Active disturbance rejection controller design for stable walking of a compass-like biped

This paper aims to develop an active disturbance rejection controller design scheme for compass-like biped robots. In the previous study, with a special designed mechanical structure on compass-like biped, we have generated a high-efficient walking gait. The original controller applied linearization approximation based on the method transverse coordinate control, with which the ability of disturbance rejection is insufficient. We introduce the active disturbance rejection controller method into the control scheme, which only requires the information of angular momentum and has the ability to compensate the model error and the external disturbance. With the new control strategy, the linearization approximation method is replaced by an active disturbance rejection controller and the robustness is improved. In order to further apply this control method, a switching control strategy has been proposed and a series of numerical simulations show that active disturbance rejection controller has a good disturbance rejection effect.
Fractional-order systems and PIλDμ-controllers

Dynamic systems of an arbitrary real order (fractional-order systems) are considered. A concept of a fractional-order PI λ D μ -controller, involving fractional-order integrator and fractional-order differentiator, is proposed. The Laplace transform formula for a new function of the Mittag-Leffler-type made it possible to obtain explicit analytical expressions for the unit-step and unit-impulse response of a linear fractional-order system with fractional-order controller both for the open and closed loop. An example demonstrating the use of the obtained formulas and the advantages of the proposed PI λ D μ -controllers is given.
Bayesian probability theory and inverse problems

Bayesian probability theory is applied to approximate solving of the inverse problems. In order to solve the moment problem with the noisy data, the entropic prior is used. The expressions for the solution and its error bounds are presented. When the noise level tends to zero, the Bayesian solution tends to the classic maximum entropy solution in the L 1 norm. The way of using spline prior is also shown
Stochasticity of Nonlinear Systems of Van der Pol-Mathieu Type

In this paper, we study stochasticity of nonlinear systems of Van der Pol-Mathieu type. At first, a sine circle map to the system is derived. Secondly, an approximate piecewise continuous map to the sine circle map is derived. Finally, we study stochasticity of the system based on Lyapunov exponents to mapped points of approximate piecewise continuous map and to oscillation of the original nonlinear systems. We show that if the oscillation of approximate piecewise continuous map is stochastic, that is Lyapunov exponent to the map is positive, then the oscillation of original system of Van der Pol-Mathieu type exhibits stochastic motion.
New User Intent Discovery With Robust Pseudo Label Training and Source Domain Joint Training

Discovering new user intents based on existing intents from constantly incoming unlabeled data is an important task in many intelligent systems deployed in the real world (e.g., dialogue systems). Since data with new intents are completely unlabeled, most current approaches employ clustering methods to generate pseudo labels to train their models. However, due to intent gaps between existing and new intents, pseudo labels generated by these models are noisy, and prior knowledge from existing intents is not fully utilized. To mitigate these issues, we propose a robust pseudo label training and source domain joint-training network to refine the noisy pseudo labels and make full use of prior knowledge. Experimental results on three intent detection datasets show that our model is more effective and robust than state-of-the-art methods. The code and data are released at https://github.com/Lackel/PTJN.
A Null-space Algorithm for Overcomplete Independent Component Analysis

Author(s): Chen, Ray-Bing; Wu, Ying N | Abstract: Independent component analysis (ICA) is an important method for blind source separation and unsupervised learning. Recently, the method has been extended to the overcomplete situation where the number of sources is greater than the number of receivers. Comparing complete ICA and overcomplete ICA in existing literature, one can notice that complete ICA does not assume noise in observations, and the sources can be ex- plicitly solved from the receivers, whereas the overcomplete ICA in gen- eral assumes noise in observations and the sources are implicitly solved by gradient type algorithms. In this paper, we present an explicit null- space representation for overcomplete ICA in the noiseless situation based on singular value decomposition (SVD), and develop an algorithm for estimating mixing matrix and recovering the sources. The null-space representation makes the connection between complete ICA and over- complete ICA more apparent, and leads to a mathematical explanation of lateral inhibition in the context of overcomplete linear model. It also appears to work well on the experimental examples. Moreover, it can be extended to the situation where there is noise in observations, and may lead to more efficient algorithms in this situation.
Regularity results for nonlinear elliptic systems

We prove partial regularity results for solutions to systems of elliptic partial differential equations with divergence structure, under nonstandard growth conditions. We consider solutions to −div a(x, u,Du) = b(x, u,Du), and use the method of A-harmonic approximation to show C1,α regularity almost-everywhere. We further calculate the optimal exponent, provided the operator a has Hölder continuous coefficients. We then relax the continuity assumption to allow for VMO or small BMO coefficients, and while a loss of regularity in the solution is to be expected, we retain almost-everywhere C0,α regularity in the solution. We then modify a technique of Campanato to further reduce the Hausdorff dimension of the singular set, assuming restrictions on the exponent and ambient dimension.
A Comprehensive Overview of the ELECTRE Method in Multi Criteria Decision-Making

The ELECTRE (ELimination Et Choix Traduisant la REalite) method has gained widespread recognition as one of the most effective multi-criteria decision-making (MCDM) methods. Its versatility allows it to be applied in a wide range of areas such as engineering, economics, business, environmental management and many others. This paper aims to provide an overview of the ELECTRE method, including its fundamental concepts, applications, advantages, and limitations. At its core, the ELECTRE method is an outranking family of MCDM techniques, which allows for the direct comparison of alternatives based on a set of criteria. The method takes into account the preferences and importance of decision-makers and generates a ranking of the alternatives based on their relative strengths and weaknesses. The ELECTRE method is a powerful tool for decision-making, and its applicability to a wide range of fields demonstrates its versatility and adaptability. By understanding its concepts, applications, merits, and demerits, decision-makers can use the ELECTRE method to make informed and effective decisions in a variety of contexts.