Miracle-dz/UniFAR-FTUs huggingface dataset:
A Statistical Story of Visual Illusions

This paper explores the wholly empirical paradigm of visual illusions, which was introduced two decades ago in Neuro-Science. This data-driven approach attempts to explain visual illusions by the likelihood of patches in real-world images. Neither the data, nor the tools, existed at the time to extensively support this paradigm. In the era of big data and deep learning, at last, it becomes possible. This paper introduces a tool that computes the likelihood of patches, given a large dataset to learn from. Given this tool, we present an approach that manages to support the paradigm and explain visual illusions in a unified manner. Furthermore, we show how to generate (or enhance) visual illusions in natural images, by applying the same principles (and tool) reversely.
Migration by soaring or flapping flight in birds: the relative importance of energy cost and speed

Birds migrating over land use either of two basic flight strategies, i.e. flapping or gliding/soaring flight. In soaring flight the birds gain altitude mainly by circling in thermals, i.e. rising air, and then they glide off until another thermal is encountered. Powered flapping flight is energetically much more expensive than gliding flight. This leaves us with the question why do not most birds adopt the soaring strategy rather than flapping flight on migration? I present optimization criteria, based on flight mechanical theory, for (i) energy-selected migration and (ii) time-selected migration, for flapping and soaring flight migration, respectively. These are evaluated in relation to general body size and rate of climb in thermals. I also consider the effects of wing morphology and horizontal winds. The general conclusion is that minimization of transport costs probably cannot be the only critical selective factor. In time-selected migration the size range of birds for which flapping flight is advantageous over thermal soaring flight, is significantly larger than in energy-selected migration, and this is in better agreement with what is found in real birds. Therefore, resulting migration speed probably constitutes an important selective force in bird migration. I also evaluate criteria for mixed strategies, i.e. when birds should use soaring flight when thermals are available and proceed by flapping flight otherwise. Finally, I also discuss some other factors, e.g. sensitivity to crosswinds, abundance of thermals and topography, which may affect the evolution of migration strategy.
Optimal Bounds for the k-cut Problem

In the k-cut problem, we want to find the lowest-weight set of edges whose deletion breaks a given (multi)graph into k connected components. Algorithms of Karger and Stein can solve this in roughly O(n2k) time. However, lower bounds from conjectures about the k-clique problem imply that Ω (n(1-o(1))k) time is likely needed. Recent results of Gupta, Lee, and Li have given new algorithms for general k-cut in n1.98k + O(1) time, as well as specialized algorithms with better performance for certain classes of graphs (e.g., for small integer edge weights). In this work, we resolve the problem for general graphs. We show that the Contraction Algorithm of Karger outputs any fixed k-cut of weight α λ k with probability Ωk(n-α k), where λ k denotes the minimum k-cut weight. This also gives an extremal bound of Ok(nk) on the number of minimum k-cuts and an algorithm to compute λ k with roughly nk polylog(n) runtime. Both are tight up to lower-order factors, with the algorithmic lower bound assuming hardness of max-weight k-clique. The first main ingredient in our result is an extremal bound on the number of cuts of weight less than 2 λk/k, using the Sunflower lemma. The second ingredient is a fine-grained analysis of how the graph shrinks—and how the average degree evolves—in the Karger process.
Voice Activity Detection Scheme by Combining DNN Model with GMM Model

Due to the superior modeling ability of deep neural network (DNN), it is widely used in voice activity detection (VAD). However, the performance may degrade if no sufficient data especially for practical data could be used for training, thus, leading to inferior ability of adaption to environment. Moreover, large model structure could not always be used in practical, especially for low cost devices where restricted hardware is used. This is on the contrary for Gaussian mixture model (GMM) where model parameters can be updated in real-time, but, with low modeling ability. In this paper, deeply integrated scheme combining these two models are proposed to improve adaptability and modeling ability. This is done by directly combining the results of models and feeding it back, together with the result of the DNN model, to update the GMM model. Besides, a control scheme is elaborately designed to detect the endpoints of speech. The superior performance by employing this scheme is validated through experiments in practical, which give an insight into the advantage of combining supervised learning and unsupervised learning.
Exploring Low-degree nodes first accelerates Network Exploration

We consider information diffusion on Web-like networks and how random walks can simulate it. A well-studied problem in this domain is Partial Cover Time, i.e., the calculation of the expected number of steps a random walker needs to visit a given fraction of the nodes of the network. We notice that some of the fastest solutions in fact require that nodes have perfect knowledge of the degree distribution of their neighbors, which in many practical cases is not obtainable, e.g., for privacy reasons. We thus introduce a version of the Cover problem that considers such limitations: Partial Cover Time with Budget. The budget is a limit on the number of neighbors that can be inspected for their degree; we have adapted optimal random walks strategies from the literature to operate under such budget. Our solution is called Min-degree (MD) and, essentially, it biases random walkers towards visiting peripheral areas of the network first. Extensive benchmarking on six real datasets proves that the—perhaps counter-intuitive strategy—MD strategy is in fact highly competitive wrt. state-of-the-art algorithms for cover.
Hard-X-ray-selected active galactic nuclei – I. A radio view at high frequencies

A thorough study of radio emission in Active Galactic Nuclei (AGN) is of fundamental importance to understand the physical mechanisms responsible for the emission and the interplay between accretion and ejection processes. High frequency radio observations can target the nuclear contribution of smaller emitting regions and are less affected by absorption. We present JVLA 22 and 45 GHz observations of 16 nearby (0.003$le$z$le$0.3) hard - X-rays selected AGN at the (sub)-kpc scale with tens uJy beam$^{-1}$ sensitivity. We detected 15/16 sources, with flux densities ranging from hundreds uJy beam$^{-1}$ to tens Jy (specific luminosities from $sim$10$^{20}$ to $sim$10$^{25},W,Hz^{-1}$ at 22 GHz). All detected sources host a compact core, with 8 being core-dominated at either frequencies, the others exhibiting also extended structures. Spectral indices range from steep to flat/inverted. We interpret this evidence as either due to a core+jet system (6/15), a core accompanied by surrounding star formation (1/15), to a jet oriented close to the line of sight (3/15), to emission from a corona or the base of a jet (1/15), although there might be degeneracies between different processes. Four sources require more data to shed light on their nature. We conclude that, at these frequencies, extended, optically-thin components are present together with the flat-spectrum core. The ${L_R}/{L_X}sim10^{-5}$ relation is roughly followed, indicating a possible contribution to radio emission from a hot corona. A weakly significant correlation between radio core (22 and 45 GHz) and X-rays luminosities is discussed in the light of an accretion-ejection framework.
Small sample whole-genome amplification

Many challenges arise when trying to amplify and analyze human samples collected in the field due to limitations in sample quantity, and contamination of the starting material. Tests such as DNA fingerprinting and mitochondrial typing require a certain sample size and are carried out in large volume reactions; in cases where insufficient sample is present whole genome amplification (WGA) can be used. WGA allows very small quantities of DNA to be amplified in a way that enables subsequent DNA-based tests to be performed. A limiting step to WGA is sample preparation. To minimize the necessary sample size, we have developed two modifications of WGA: the first allows for an increase in amplified product from small, nanoscale, purified samples with the use of carrier DNA while the second is a single-step method for cleaning and amplifying samples all in one column. Conventional DNA cleanup involves binding the DNA to silica, washing away impurities, and then releasing the DNA for subsequent testing. We have eliminated losses associated with incomplete sample release, thereby decreasing the required amount of starting template for DNA testing. Both techniques address the limitations of sample size by providing ample copies of genomic samples. Carrier DNA, included in our WGA reactions, can be used when amplifying samples with the standard purification method, or can be used in conjunction with our single-step DNA purification technique to potentially further decrease the amount of starting sample necessary for future forensic DNA-based assays.
Multi-scale Grouped Dense Network for VVC Intra Coding

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDNGAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams "IMC" and "haha" at the bit-rate of 0.15 in Lowrate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.
Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition

Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data shifts. The design of robust models for accurate SER is challenging, which limits its use in practical applications. In this paper we propose a deeper neural network architecture wherein we fuse DenseNet, LSTM and Highway Network to learn powerful discriminative features which are robust to noise. We also propose data augmentation with our network architecture to further improve the robustness. We comprehensively evaluate the architecture coupled with data augmentation against (1) noise, (2) adversarial attacks and (3) cross-corpus settings. Our evaluations on the widely used IEMOCAP and MSP-IMPROV datasets show promising results when compared with existing studies and state-of-the-art models.
Frequency range dependent topological phases and photonic detouring in valley photonic crystals

Here, the frequency degree of freedom is introduced into valley photonic crystals with dual band gaps. Based on the high-order plane wave expansion model, we derive an effective Hamiltonian which characterizes dual band gaps. Metallic valley photonic crystals are demonstrated as examples in which all four topological phases are found. At the domain walls between topologically distinct valley photonic crystals, frequency-dependent edge states are demonstrated and a broadband photonic detouring is proposed. Our findings provide the guidance for designing the frequency-dependent property of topological structures and show its potential applications in wavelength division multiplexers.
Metric Learning for Keyword Spotting

The goal of this work is to train effective representations for keyword spotting via metric learning. Most existing works address keyword spotting as a closed-set classification problem, where both target and non-target keywords are predefined. Therefore, prevailing classifier-based keyword spot-ting systems perform poorly on non-target sounds which are unseen during the training stage, causing high false alarm rates in real-world scenarios. In reality, keyword spotting is a detection problem where predefined target keywords are detected from a variety of unknown sounds. This shares many similarities to metric learning problems in that the unseen and unknown non-target sounds must be clearly differentiated from the target keywords. However, a key difference is that the target keywords are known and predefined. To this end, we propose a new method based on metric learning that maximises the distance between target and non-target key-words, but also learns per-class weights for target keywords as in classification objectives. Experiments on the Google Speech Commands dataset show that our method significantly reduces false alarms to unseen non-target keywords, while maintaining the overall classification accuracy.
Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer

Quality of questions and answers from community support websites (e.g. Microsoft Developers Network, Stackoverflow, Github, etc.) is difficult to define and a prediction model of quality questions and answers is even more challenging to implement. Previous works have addressed the question quality models and answer quality models separately using meta-features like number of up-votes, trustworthiness of the person posting the questions or answers, titles of the post, and context naive natural language processing features. However, there is a lack of an integrated question-answer quality model for community question answering websites in the literature. In this brief paper, we tackle the quality Q&A modeling problems from the community support websites using a recently developed deep learning model using bidirectional transformers. We investigate the applicability of transfer learning on Q&A quality modeling using Bidirectional Encoder Representations from Transformers (BERT) trained on a separate tasks originally using Wikipedia. It is found that a further pre-training of BERT model along with finetuning on the Q&As extracted from Microsoft Developer Network (MSDN) can boost the performance of automated quality prediction to more than 80%. Furthermore, the implementations are carried out for deploying the finetuned model in real-time scenario using AzureML in Azure knowledge base system.
Number of kernels in wheat crops and the influence of solar radiation and temperature

SUMMARY The number of kernels per m2 (K) in well managed and watered wheat crops was studied using results of experiments in Mexico and Australia in which short spring wheat cultivars were subjected to independent variation in radiation, largely via artificial shading, and in temperature. Also crops subjected to differences in weather (year), sowing date and location within Mexico, revealed responses to the natural and simultaneous variation which occurs in radiation and temperature. Responses in K were interpreted in terms of spike dry weight at anthesis (g/m2) and number of kernels per unit of spike weight. K was linearly and most closely related to incident solar radiation in the 30 days or so preceding anthesis, herein termed the spike growth period; for the cultivar Yecora 70 with full ground cover the slope was 19 kernels/MJ. This response seemed largely due to a linear response of crop growth rate to intercepted solar radiation. The proportion of dry weight increase partitioned to the spike increased somewhat with reduced radiation. Also increasing temperature in the range 14–22 °C during this period reduced K (slope approximately 4% per CC at 15 °C). The cause appeared to be lower spike dry weight due to accelerated development. The number of kernels per unit spike weight at anthesis was little affected by radiation or temperature, and averaged 78±2/g for the cultivar Yecora 70. With natural variation in radiation and temperature, K was closely and linearly correlated with the ratio of mean daily incident or intercepted radiation to mean temperature above 4·5 °C in the 30 days preceding anthesis. As this ratio, termed the photothermal quotient, increased from 0·5 to 2·0 MJ/m2/day/degree, K increased from 70 to 196 × 102/m2. These responses of K to weather, sowing date and location were closely associated with variation in spike dry weight. It was concluded that the ratio of solar radiation to temperature could be very useful for estimating K in wheat crop models. Also the analysis of K determination in terms of spike dry weight appeared promising, and suggests that wheat physiologists should place greater emphasis on the growth period immediately before anthesis.
Hierarchical and Efficient Learning for Person Re-Identification

Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e.g. model size and latency, which are critical for practical application. In this paper, we propose a novel Hierarchical and Efficient Network (HENet) that learns hierarchical global, partial, and recovery features ensemble under the supervision of multiple loss combinations. To further improve the robustness against the irregular occlusion, we propose a new dataset augmentation approach, dubbed Random Polygon Erasing (RPE), to random erase irregular area of the input image for imitating the body part missing. We also propose an Efficiency Score (ES) metric to evaluate the model efficiency. Extensive experiments on Market1501, DukeMTMC-ReID, and CUHK03 datasets shows the efficiency and superiority of our approach compared with epoch-making methods.
Joint Multi-Dimension Pruning

We present joint multi-dimension pruning (named as JointPruning), a new perspective of pruning a network on three crucial aspects: spatial, depth and channel simultaneously. The joint strategy enables to search a better status than previous studies that focused on individual dimension solely, as our method is optimized collaboratively across the three dimensions in a single end-to-end training. Moreover, each dimension that we consider can promote to get better performance through colluding with the other two. Our method is realized by the adapted stochastic gradient estimation. Extensive experiments on large-scale ImageNet dataset across a variety of network architectures MobileNet V1&V2 and ResNet demonstrate the effectiveness of our proposed method. For instance, we achieve significant margins of 2.5% and 2.6% improvement over the state-of-the-art approach on the already compact MobileNet V1&V2 under an extremely large compression ratio.
A Linear Time Algorithm for Computing the Eternal Vertex Cover Number of Cactus Graphs

The eternal vertex cover problem is a dynamic variant of the classical vertex cover problem. It is NP-hard to compute the eternal vertex cover number of graphs and known algorithmic results for the problem are very few. This paper presents a linear time recursive algorithm for computing the eternal vertex cover number of cactus graphs. Unlike other graph classes for which polynomial time algorithms for eternal vertex cover number are based on efficient computability of a known lower bound directly derived from minimum vertex cover, we show that it is a certain substructure property that helps the efficient computation of eternal vertex cover number of cactus graphs. An extension of the result to graphs in which each block is an edge, a cycle or a biconnected chordal graph is also presented.
Analyzing the relationship between text features and research proposal success

Predicting the success of research proposals is of considerable relevance to research funding bodies, scientific entities and government agencies. In this study, we investigate whether text features extracted from proposals title and abstracts are able to identify successful funded research proposals. Our analysis was conducted in three distinct areas, namely Medicine, Dentistry and Veterinary Medicine. Topical and complexity text features were used to identify predictors of success. The results indicate that both topical and complexity measurements are relevant for identifying successful proposals in the considered dataset. A feature relevance analysis revealed that abstract text length and metrics derived from lexical diversity are among the most discriminative features. We also found that the prediction accuracy has no significant dependence on the considered proposal language. Our findings suggest that textual information, in combination with other features, are potentially useful to assist the identification of relevant research ideas.
Adaptive Influence Maximization

Influence maximization problem attempts to find a small subset of nodes that makes the expected influence spread maximized, which has been researched intensively before. They all assumed that each user in the seed set we select is activated successfully and then spread the influence. However, in the real scenario, not all users in the seed set are willing to be an influencer. Based on that, we consider each user associated with a probability with which we can activate her as a seed, and we can attempt to activate her many times. In this article, we study the adaptive influence maximization with multiple activations (Adaptive-IMMA) problem, where we select a node in each iteration, observe whether she accepts to be a seed, if yes, wait to observe the influence diffusion process; if no, we can attempt to activate her again with a higher cost or select another node as a seed. We model the multiple activations mathematically and define it on the domain of integer lattice. We propose a new concept, adaptive dr-submodularity, and show our Adaptive-IMMA is the problem that maximizing an adaptive monotone and dr-submodular function under the expected knapsack constraint. Adaptive dr-submodular maximization problem is never covered by any existing studies. Thus, we summarize its properties and study its approximability comprehensively, which is a non-trivial generalization of existing analysis about adaptive submodularity. Besides, to overcome the difficulty to estimate the expected influence spread, we combine our adaptive greedy policy with sampling techniques without losing the approximation ratio but reducing the time complexity. Finally, we conduct experiments on several real datasets to evaluate the effectiveness and efficiency of our proposed policies.