Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors

Compressive phase retrieval is a popular variant of the standard compressive sensing problem in which the measurements only contain magnitude information. In this paper, motivated by recent advances in deep generative models, we provide recovery guarantees with near-optimal sample complexity for phase retrieval with generative priors. We first show that when using i.i. d. Gaussian measurements and an $L$-Lipschitz continuous generative model with bounded $k$-dimensional inputs, roughly $O(k \log L)$ samples suffice to guarantee that any signal minimizing an amplitude-based empirical loss function is close to the true signal. Attaining this sample complexity with a practical algorithm remains a difficult challenge, and finding a good initialization for gradient-based methods has been observed to pose a major bottleneck. To partially address this, we further show that roughly $O(k \log L)$ samples ensure sufficient closeness between the underlying signal and any {\em globally optimal} solution to an optimization problem designed for spectral initialization (though finding such a solution may still be challenging). We also adapt this result to sparse phase retrieval, and show that $O(s \log n)$ samples are sufficient for a similar guarantee when the underlying signal is $s$-sparse and $n$-dimensional, matching an information-theoretic lower bound. While these guarantees do not directly correspond to a practical algorithm, we propose a practical spectral initialization method motivated by our findings, and experimentally observe performance gains over various existing spectral initialization methods for sparse phase retrieval.
Meta-learning for Matrix Factorization without Shared Rows or Columns

We propose a method that meta-learns a knowledge on matrix factorization from various matrices, and uses the knowledge for factorizing unseen matrices. The proposed method uses a neural network that takes a matrix as input, and generates prior distributions of factorized matrices of the given matrix. The neural network is meta-learned such that the expected imputation error is minimized when the factorized matrices are adapted to each matrix by a maximum a posteriori (MAP) estimation. We use a gradient descent method for the MAP estimation, which enables us to backpropagate the expected imputation error through the gradient descent steps for updating neural network parameters since each gradient descent step is written in a closed form and is differentiable. The proposed method can meta-learn from matrices even when their rows and columns are not shared, and their sizes are different from each other. In our experiments with three user-item rating datasets, we demonstrate that our proposed method can impute the missing values from a limited number of observations in unseen matrices after being trained with different matrices.
Semi-supervised learning with Bayesian Confidence Propagation Neural Network

Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data. In this work, we use the Bayesian Confidence Propagation Neural Network (BCPNN) model developed as a biologically plausible model of the cortex. Recent work has demonstrated that these networks can learn useful internal representations from data using local Bayesian-Hebbian learning rules. In this work, we show how such representations can be leveraged in a semi-supervised setting by introducing and comparing different classifiers. We also evaluate and compare such networks with other popular semi-supervised classifiers.
Zoo-Tuning: Adaptive Transfer from a Zoo of Models

With the development of deep networks on various large-scale datasets, a large zoo of pretrained models are available. When transferring from a model zoo, applying classic single-model based transfer learning methods to each source model suffers from high computational burden and cannot fully utilize the rich knowledge in the zoo. We propose \emph{Zoo-Tuning} to address these challenges, which learns to adaptively transfer the parameters of pretrained models to the target task. With the learnable channel alignment layer and adaptive aggregation layer, Zoo-Tuning \emph{adaptively aggregates channel aligned pretrained parameters} to derive the target model, which promotes knowledge transfer by simultaneously adapting multiple source models to downstream tasks. The adaptive aggregation substantially reduces the computation cost at both training and inference. We further propose lite Zoo-Tuning with the temporal ensemble of batch average gating values to reduce the storage cost at the inference time. We evaluate our approach on a variety of tasks, including reinforcement learning, image classification, and facial landmark detection. Experiment results demonstrate that the proposed adaptive transfer learning approach can transfer knowledge from a zoo of models more effectively and efficiently.
Benchmarking Unsupervised Object Representations for Video Sequences

Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding. Recently, several methods have been proposed for unsupervised learning of object-centric representations. However, since these models were evaluated on different downstream tasks, it remains unclear how they compare in terms of basic perceptual abilities such as detection, figure-ground segmentation and tracking of objects. To close this gap, we design a benchmark with four data sets of varying complexity and seven additional test sets featuring challenging tracking scenarios relevant for natural videos. Using this benchmark, we compare the perceptual abilities of four object-centric approaches: ViMON, a video-extension of MONet, based on recurrent spatial attention, OP3, which exploits clustering via spatial mixture models, as well as TBA and SCALOR, which use explicit factorization via spatial transformers. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking than the spatial transformer based architectures. We also observe that none of the methods are able to gracefully handle the most challenging tracking scenarios despite their synthetic nature, suggesting that our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
Forest Fire Clustering: Iterative Label Propagation Clustering and Monte Carlo Validation for Single-cell Sequencing Analysis

With the rise of single-cell sequencing technologies, there is a growing need for robust clustering algorithms to extract deeper insights from data. Here, we introduce an intuitive and efficient clustering method, Forest Fire Clustering, for discovering and validating cell types in single-cell sequencing analysis. Compared to existing methods, our clustering algorithm makes minimum prior assumptions about the data distribution and can provide a point-wise significance value via Monte Carlo simulations for internal validation. Additionally, point-wise label entropies can highlight novel transition cell types de novo along developmental pseudo-time manifolds. Lastly, our inductive algorithm has the ability to make robust inferences in an online-learning context. In this paper, we describe the method, provide a summary of its performance against common clustering benchmarks, and demonstrate that Forest Fire Clustering is uniquely suitable for single-cell sequencing analysis.
Prediction Based Dynamic Controller Placement in SDN

The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and control planes from the network devices and enables programmability of network devices. The major challenge in SDN is identification of number of controllers to be placed and its optimal placements in the network. To address this issue, this work proposes a Traffic Engineering mechanism that leverages the performance of Machine Learning to predict controller numbers by analysing and predicting the controller’s traffic. The optimal locations of controllers are identified by using the K-Means++ algorithm. The proposed method is simulated using Mininet and the results depict that the proposed methodology outperforms the existing methodologies in terms of performance parameters.
Methodology for the Optimisation of Battery Hybrid Energy Storage Systems for Mass and Volume Using a Power-To-Energy Ratio Analysis

Increasingly stringent emission regulations and environmental concerns have propelled the development of electrification technology in the transport industry. Yet, the greatest hurdle to developing fully electric vehicles is electrochemical energy storage, which struggles to achieve profitable specific power, specific energy and cost targets. Hybrid energy storage systems (HESSs), which combine energy- and power-optimised sources, seem to be the most promising solution for improving the overall performance of energy storage. The potential for gravimetric and volumetric reduction is strictly dependent on the overall power-to-energy ratio (PE ratio) of the application, packaging factors, the minimum and maximum PE ratio achievable for the system’s energy- and power-optimised sources and the performance of power electronics. This paper presents a simple optimisation methodology that considers these factors and identifies the optimal HESS requirements that may present new opportunities for a variety of vehicles where low weight and volume are of high importance. The simplicity of the method means that decisions relating to a HESS can be made earlier in the system design process. This method of analysis showed that a battery HESS has the potential to reduce cell mass and volume by over 30% for applications that are well suited to optimal HESS characteristics.
Cordia africana but not Juniperus procera and Podocarpus falcatus respond positively to arbuscular mycorrhizal fungi at the early stages of seedling development

Abstract. Asmelash F, Bekele T, Belay Z, Kebede F. 2021. Cordia africana but not Juniperus procera and Podocarpus falcatus respond positively to arbuscular mycorrhizal fungi at the early stages of seedling development. Biodiversitas 22: 2971-2980. AMF (Arbuscular mycorrhizal fungi) inoculation could be an important technology to improve the growth and field survival of trees. Hence, we evaluated the mycorrhizal responsiveness of Cordia africana Lam. , Juniperus procera (Hoechst. ex Endl.), and Podocarpus falcatus (Thumb.) Mirb. seedlings. Seedlings germinated on sterile sand were transplanted to 1-liter plastic pots filled with sterile and non-sterile degraded bulk soil. Rhizospheric soil from adult C. africana and J. procera were used as whole-soil AMF inocula. Cordia africana and J. procera received conspecific whole-soil AMF inocula while P. falcatus received J. procera inoculum. Hence, in the two-by-two factorial experiment, we also evaluated the growth effects of AMF inoculation, soil type, and their interaction. On the sterile potting soil, MRi (mycorrhizal responsiveness due to AMF inoculation) of C. africana was positive and significantly (p<0.05) greater than the MRi of J. procera and P. falcatus. However, on the non-sterile potting soil, it was significantly greater than the MRi of P. falcatus only. MRs (MR due to the existing potting soil inocula) and considering all growth variables were mostly positive for C. africana but negative for J. procera and P. falcatus. AMF inoculation significantly increased most growth variables of C. africana seedlings and no significant “inoculation” x “soil type” interaction effects were detected. Hence, AMF inoculation of C. africana seedlings could be merited and under wide range of field conditions. In the case of J. procera and P. falcatus, after-planting care could be more appropriate
Modeling and Analysis on the Five Degrees of Freedom Integrated AC- DC Hybrid Magnetic Bearing

1Faculty of Automation, Huaiyin Institute of Technology, Huai’an, China, zhangtaohyit@126.com 2Jiangsu Engineering Research Center on Meteorological Energy Using and Control, Nanjing University of Information Science and Technology, Nanjing, China, zhangtaohyit@126.com 3Faculty of Mechanical and Material Engineering, Huaiyin Institute of Technology, Huai’an, China, zwg2007@hyit.edu. cn 4School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia, Gang.lei@uts.eud. au
Spaced Learning Strategy in Teaching Mathematics

Students’ low mastery of the lesson in Mathematics is one of the alarming problems confronted by Mathematics teachers. It is in this light that this study was conceptualized to determine the effectiveness of spaced learning strategy on the performance and mastery of DMMMSU Laboratory High School students (Grade 7) in Mathematics. This study used the true experimental design, specifically the pretest-posttest control group design. The main instrument used to gather data was the pretest-posttest which was subjected to validity and reliability tests. It was found out that the experimental and control groups were comparable in the pretest and posttest. Comparison on their gain scores revealed significant difference with the performance of the experimental higher than the control group. It was also found out that the effect size of using the spaced learning strategy was large. This indicates that the intervention is effective in increasing the performance and mastery of high school students in Mathematics. It is recommended the use of the Spaced Learning Strategy to improve the performance of the high school students in Mathematics.
U.S. Environmental Protection Agency Radiogenic Risk Models and Projections for the U.S. Population

AbstractThe U.S. Environmental Protection Agency has updated its estimates of cancer incidence and mortality risks due to low doses of ionizing radiation for the U.S. population as well as their scientific basis. For the most part, these estimates were calculated using models recommended in the recent National Academy of Sciences’ (BEIR VII) report on health effects from low levels of ionizing radiation. However, a number of modifications and extensions to the BEIR VII approach have been implemented. Most notably, risk estimates are included for: 1) alpha particles; 2) additional cancer sites (basal cell carcinomas, kidney cancer, bone sarcomas); and 3) cancers from prenatal exposures. In addition, unlike in BEIR VII, the primary numerical estimates of lifetime risk are for a stationary population instead of the U.S. census population. For uniform whole-body exposures of low dose gamma radiation to the entire population, the cancer incidence risk coefficient (Gy−1) is 1.16 × 10−1 (5.6 × 10−2 to 2.1 × 10−1), where the numbers in parentheses represent an estimated 90% uncertainty interval. The corresponding coefficient for cancer mortality (Gy−1) is about one-half that for incidence: 5.8 × 10−2 (2.8 × 10−2 to 1.0 × 10−1). In addition, a somewhat altered and expanded analysis of uncertainties in the cancer risk estimates is provided. It is anticipated that results summarized here will be applied to update the Agency’s next revision of Federal Guidance Report 13, which tabulates cancer risk coefficients for specific radionuclides.
Fishing behaviours and fisher effect in decision‐making processes when facing depredation by marine predators

Fishers are decisionmakers who have a top predatorlike foraging behaviour when searching and exploiting patchily distributed fish resources (Bertrand et al., 2007; Bez et al., 2011; Planque et al., 2011). Decisionmaking processes may be driven by both external factors (e.g. resource availability, environmental conditions, economic circumstances, fishing regulations and presence of other predators) and internal factors (e.g. fishers’ skills/personality and characteristics of boats – Holley & Marchal, 2004; Marchal et al., 2006; Simpson et al., 2011). To model these human behaviours, ecologists have used optimal foraging theory (OFT) to inform the decisionmaking process as regards alternatives for optimising cost– benefit ratios (Aswani, 1998; Begossi, 1992; McCay, 1981). Decisions made by fishers, in Received: 14 May 2020 | Revised: 26 April 2021 | Accepted: 24 May 2021 DOI: 10.1111/fme.12503
The Overall Distribution Pattern of the Hand’s Cutaneous Nerves and Its Clinical Implications in Sensory Reconstruction

The objective of this study was to reveal the overall distribution pattern of the hand’s cutaneous nerves to provide a morphological basis for the selection and matching of the hand skin for sensory reconstruction during flap transplantation. The hands of 12 adult cadavers were used for the study. Palmar region and dorsum of the hand were divided into regions I–VI. The skin of the ha d containing subcutaneous fat was removed close to the muscle surface. The modified Sihler’s staining method was used to visualiz e the overall distribution pattern of the cutaneous nerves and the areas they innervate. The median nerve, superficial branch of the ul ar nerv , and the superficial branch of the radial nerve innervated 59.27 % (containing 4.65 % of the palmar cutaneous branch of the medi an nerve), 36.91 %, and 3.82 % of the palm area, respectively. The superficial branch of the radial nerve, the dorsal branch of the ulnar nerv , a d the median nerve innervated 45.16 %, 47.25 %, and 7.59 % of the hand’s dorsal skin area, respectively. Communication was found betw een the arborized branches of these cutaneous nerves. Region III of the palm and region VI of the dorsum of the hand had relatively mor e dense nerve distribution. Except for region V, the density of the total nerve branches in each palm region was higher than that of th e dorsum of the hand. The total number of nerve branches in the distal phalanx and dorsum decreased from the thumb to the digitus minimus. Our result provide morphological guidance when designing a reasonable matching flap to improve the hand’s sensory function reconstruction.
POLITENESS STRATEGIES USED BY EFL TEACHER IN CLASSROOM INTERACTION

The objective of this research were to identify the types of politeness strategies which are used by EFL teacher in classroom interaction and to find out the politeness strategies which are dominantly used by the teacher in EFL classroom interaction. The researcher applied descriptive qualitative research in order to get more understanding about phenomena of teacher’s politeness strategies. The subject of this research was one of teacher in SMA 21 Makassar who teaches in the classroom. The researcher found that some politeness strategies that the teacher used. It was divided into three types. First bald on record based on what the researcher found in the first until the fourth meeting can be drawn out such as (cases of non minimization of the face threat and case of FTA oriented usage (welcoming), positive politeness based on what the researcher found in the first until the last meeting can be drawn out such as (giving gift to the hearer, noticing attend good to the hearer, exaggerating interest to the hearer, seeking agreement, offering promise, being optimistic, involving both S and H in the activity, giving gift interest to hearer, and making a joke), negative politeness based on what the researcher found in the first until fourth meeting can be drawn out such as (be direct’ sub strategy, be pessimistic sub strategy, and apologize sub strategy). And the dominant politeness strategies used found that positive politeness strategies occurred in 59 utterances (54, 2%). Negative politeness occurred in 30 utterances (22, 5%) and Bald on record occurred in 20 teacher’s utterances. This type happened because the spontaneous condition in the teaching and learning process. From the result of data analysis shows that, the teacher used positive politeness most dominantly in EFL. Furthermore, positive politeness is used as a communicative strategy to create good communication between the teacher and students. Keyword: Politeness strategies, Teacher, Classroom Interaction
International Journal of Innovative Technology and Exploring Engineering (IJITEE)

Since deep learning applications in object recognition, object detection, segmentation, and image generation are needed increasingly, related research has been actively conducted. In this paper, using segmentation and style transfer together, a method of producing desired images in the desired area in real-time video is proposed. Two deep neural networks were used to enable as possible as in real-time with the trade-off relationship between speed and accuracy. Modified BiSeNet for segmentation and CycleGAN for style transfer were processed on a desktop PC equipped with two RTX-2080-Ti GPU boards. This enables real-time processing over SD video in decent level. We obtained good results in subjective quality to segment Road area in city street video and change into the Grass style at no less than 6(fps).
Compound Effective Microorganisms Treatment Increases Growth and Yield of Vegetables

Misuse or excessive use of chemical fertilizers cause non-point source pollution in the vegetable production system. Fertilizer is a key factor affecting nutrient absorption and biomass production of vegetables. However, there is little information on the role of Compound Effective Microorganisms (CEM) in vegetable production. In field trials, three fertilizer treatments were used to study their effects on the growth characteristics and quality of lettuce, spinach, and pakchoi. There were five treatments in the experiment including Control Treatment (CK), 0.3% (Mass volume ratio: w/v) of urea: Water (T1), 0.3% (w/v) of compound fertilizer: Water (T2), CEM fertilizer was diluted to 1: 1,000 (CEM fertilizer: Water, Volume ratio: v/v) before application (T3), 1: 500 (CEM fertilizer: Water, v/v) (T4), 1: 100 (CEM fertilizer: Water, v/v) (T5). Ten representative plants were selected for each treatment to measure plant growth performance. Compared with the control, the plant height gradually increased after applying fertilizer during the entire growth period of the plant. Total leaf chlorophyll concentration of the CEM treatment was also significantly higher than the control treatments. CEM led to an increase in leaf area and leaf number. The effect of EM fertilizer on vegetable growth and yield was studied. In T4 treatment, spinach had the highest plant fresh yield, which was 15.60 g plant. Under different fertilizer treatments, the yield of pakchoi varied greatly, while under T5 treatment, the yield of pakchoi increased significantly. The maximum yield of pakchoi in T5 treatment was 30.66 g plant . The yield of lettuce in T3 treatment was the highest, 12.32 g plant. CEM could maintain productivity of green vegetable and contained a variety of beneficial bacteria. The cultivation of EM increased the yield of plants and increased the growth of vegetables. In conclusion, these results showed that vegetables could produce high yield and high quality through CEM management.