Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Distillation from Few Samples

Current knowledge distillation methods require full training data to distill knowledge from a large "teacher" network to a compact "student" network by matching certain statistics between "teacher" and "student" such as softmax outputs and feature responses. This is not only time-consuming but also inconsistent with human cognition in which children can learn knowledge from adults with few examples. This paper proposes a novel and simple method for knowledge distillation from few samples. Taking the assumption that both "teacher" and "student" have the same feature map sizes at each corresponding block, we add a 1x1 conv-layer at the end of each block in the student-net, and align the block-level outputs between "teacher" and "student" by estimating the parameters of the added layer with limited samples. We prove that the added layer can be absorbed/merged into the previous conv-layer to formulate a new conv-layer with the same size of parameters and computation cost as the previous one. Experiments verify that the proposed method is very efficient and effective to distill knowledge from teacher-net to student-net constructing in different ways on various datasets.
Carbon Dioxide Laser Microsurgery versus Low-Temperature Plasma Radiofrequency Ablation for T1a Glottic Cancer: A Single-Blind Randomized Clinical Trial

Background Very few studies have been conducted to compare carbon dioxide laser microsurgery (CO2-LS) with low-temperature plasma radiofrequency ablation (LTP-RFA) in treating T1a glottic cancer. Therefore, we conducted this study to compare the efficacy of CO2-LS and LTP-RFA to define a superior therapeutic modality for T1a glottic cancer. Methods Patients (n=131) with T1a glottic cancer were recruited between January 2010 and September 2014. The included patients were randomly assigned to either receive CO2-LS (n=65) or LTP-RFA (n=66). We conducted the following multidimensional vocal assessments: (i) videostroboscopic evaluation; (ii) auditory-perceptual evaluation; (iii) aerodynamics/ efficiency; (iv) acoustics; and (v) self-assessment questionnaires. Meanwhile, the surgery time and three-year overall survival rates in two groups were recorded. The predefined primary endpoint was overall survival, and the minimum follow-up time was set to six months. Results After treatment, we found that the structure and vibration of vocal cord might recover more quickly in patients receiving LTP-RFA than in patients receiving CO2-LS, and moreover, the patients in the LTP-RFA group had the better vocal functions. Meanwhile, the surgery time was significantly less in the LTP-RFA group (8.83±1.59 minutes) than in the CO2-LS group (12.49±1.40 minutes) (p<0.00001). In addition, the two intervention methods had the similar three-year overall survival rates (94% versus 96%, p=0.58). Conclusion These results indicated that both LTP-RFA and CO2-LS could effectively treat T1a glottic cancer, and LTP-RFA might have some advantages in voice function. Limited by the relatively small sample size, future studies were needed to validate our conclusion.
Low-Complexity Data-Parallel Earth Mover's Distance Approximations

The Earth Mover's Distance (EMD) is a state-of-the art metric for comparing discrete probability distributions, but its high distinguishability comes at a high cost in computational complexity. Even though linear-complexity approximation algorithms have been proposed to improve its scalability, these algorithms are either limited to vector spaces with only a few dimensions or they become ineffective when the degree of overlap between the probability distributions is high. We propose novel approximation algorithms that overcome both of these limitations, yet still achieve linear time complexity. All our algorithms are data parallel, and thus, we take advantage of massively parallel computing engines, such as Graphics Processing Units (GPUs). On the popular text-based 20 Newsgroups dataset, the new algorithms are four orders of magnitude faster than a multi-threaded CPU implementation of Word Mover's Distance and match its nearest-neighbors-search accuracy. On MNIST images, the new algorithms are four orders of magnitude faster than a GPU implementation of the Sinkhorn's algorithm while offering a slightly higher nearest-neighbors-search accuracy.
Cancelable Fingerprint Features Using Chaff Points Encapsulation

Recently, Biometrics imaging is widely used in several security areas such as security monitoring, database access, border control and immigration, and for reliable personal verification, identification and recognition schemes. To determine or confirm the identity of an individual's based on their physiological and/or behavioral characteristics, biometric features must be used. The aim of this paper is to review cancelable biometric generation and protection schemes. An approach for generating chaff points for fingerprint template features encapsulation as fingerprint cancelability infrastructure has been presented. Results show that strong positive correlation of original minutiae scores go with high decapsulated minutiae scores. To test the given cancelable approach performance two indexes are used, FAR (false accept rate) and FRR (false reject rate).
Genetic diversity and differentiation among Prosopis alba (Leguminosae) populations from dry valleys of Bolivia with different levels of human disturbance and altitude

Abstract The fast expansion of human population around La Paz, Bolivia (3,200–4,100 m.a.s.l.) triggered new suburban settlements in nearby areas in valleys and mountain feet. The white mesquite, Prosopis alba Griseb. (Leguminosae), is a resource (originally used by native communities) that is strongly affected by changes in land use. A gradient in the level of disturbance is found moving away from the La Paz city toward less altitude areas. The main objective of this study was to characterize genetically three P. alba populations with different levels of human disturbance located at different altitudes in Bolivia, in order to provide some guidelines for management and conservation of these species. Based on 10 SSR loci, the populations showed high level of genetic diversity in comparison with other forest species. The population less disturbed and situated at the lowest altitude was the most variable (H e = 0.51–0.42), whereas the less variable was the most disturbed and situated at the highest altitude. Heterozygote excess was observed in all populations. Most of genetic diversity (99%) is contained within populations. Genetic differentiation among populations is low (1%), suggesting low gene flow among populations. No evidence of recent bottlenecks events was detected. The estimates of the effective population size were low in all populations. The results are in agreement with the hypothesis that genetic diversity is reduced by the impact of anthropic disturbance in the population located at higher altitude in comparison with the lightly disturbed situated at lower altitude and farther from urban settlements.
Pronounced genetic differentiation in Fokienia hodginsii revealed by simple sequence repeat markers

Abstract Fokienia hodginsii is a Tertiary relict conifer of the monotypic genus Fokienia (Cupressaceae s.l.). Currently, the species is distributed in southern China, northern Vietnam, and northern Laos and listed as a “near threatened” species by the IUCN. In this study, a total of 427 individuals of F. hodginsii were sampled from China and Vietnam to characterize its genetic diversity and population differentiation. Based on the profiles of 12 simple sequence repeat (SSR) markers, we observed a high level of genetic diversity in F. hodginsii at the species level (H e =0.635), albeit slightly lower than that of its sister species Chamaecyparis obtusa. Signals of bottleneck events were detected in the populations GXDMS, GXHJ, V‐PXB, and V‐HB, probably due to Pleistocene glaciations or overexploitation in recent years. Pronounced genetic differentiation (F st = 0.157) was found in this species. The inbreeding index (F is = 0.176 ± 0.024) indicated that F. hodginsii has a mixed mating system. Significant correlation was found between the pairwise genetic differentiation and geographic distance (r = 0.882, p = 0.01), suggesting that genetic differentiation among the populations follows the model of isolation by distance (IBD). STRUCTURE analysis and principal coordinate analysis revealed that these populations were divided into four groups: the western China group located mainly in the Yunnan–Guizhou Plateau, the central China group located mostly in the Luoxiao Mountains and Nanling Mountains, the eastern China group located in the Wuyi Mountains and the Vietnam group containing two populations in Vietnam. The different terrains and elevations of populations may be the most likely factors leading to the differentiation between the western China group and the central China group, while the geographic isolation caused by the lack of appropriate habitats may greatly contribute to the differentiation between the central China group and the eastern China group. Based on the results, some conservation suggestions for this species are provided, such as establishing seed orchards and multiple nature reserves.
Phosphoprotein Contributes to the Thermostability of Newcastle Disease Virus

Newcastle disease (ND), caused by Newcastle disease virus (NDV), is highly contagious and represents a major threat to the poultry industry. The thermostable vaccines are not insensitive to heat and ease of storage and transportation, but the mechanism of NDV thermostability remains unknown. The phosphoprotein (P), fusion protein (F), hemagglutinin-neuraminidase protein (HN), and large polymerase protein (L) are associated with NDV virulence. The association between F, HN, or L and viral thermostability has been, respectively, studied in different reports. However, the effects of P on NDV thermostability have not been demonstrated. Here, we utilized an existing reverse genetics system in our laboratory, to generate chimeric viruses by exchanging the P protein between the thermostable NDV4-C strain and the thermolabile LaSota strain. Chimeric viruses were found to possess similar growth properties, passage stability, and virulence, as compared to those of these parental strains. Interestingly, the thermostability of the chimera with P derived from the thermolabile LaSota strain was reduced compared to that of the parental virus, and P of the thermostable NDV4-C strain enhanced chimeric virus thermostability. Our data demonstrate that P is an important factor for the thermostability of NDV and provides information regarding the molecular mechanism of NDV thermostability; moreover, these results suggest a theoretical basis for using the NDV4-C strain as a thermostable vaccine.
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies

Medical imaging machine learning algorithms are usually evaluated on a single dataset. Although training and testing are performed on different subsets of the dataset, models built on one study show limited capability to generalize to other studies. While database bias has been recognized as a serious problem in the computer vision community, it has remained largely unnoticed in medical imaging research. Transfer learning thus remains confined to the re-use of feature representations requiring re-training on the new dataset. As a result, machine learning models do not generalize even when trained on imaging datasets that were captured to study the same variable of interest. The ability to transfer knowledge gleaned from one study to another, without the need for re-training, if possible, would provide reassurance that the models are learning knowledge fundamental to the problem under study instead of latching onto the idiosyncracies of a dataset. In this paper, we situate the problem of dataset bias in the context of medical imaging studies. We show empirical evidence that such a problem exists in medical datasets. We then present a framework to unlearn study membership as a means to handle the problem of database bias. Our main idea is to take the data from the original feature space to an intermediate space where the data points are indistinguishable in terms of which study they come from, while maintaining the recognition capability with respect to the variable of interest. This will promote models which learn the more general properties of the etiology under study instead of aligning to dataset-specific peculiarities. Essentially, our proposed model learns to unlearn the dataset bias.
A Dynamic Placement Mechanism of Service Function Chaining Based on Software-defined Networking

To cope with the explosive growth of Internet services, Service Function Chaining (SFC) based on Software-defined Networking (SDN) is an emerging and promising technology that has been suggested to meet this challenge. Determining the placement of Virtual Network Functions (VNFs) and routing paths that optimize the network utilization and resource consumption is a challenging problem, particularly without violating service level agreements (SLAs). This problem is called the optimal SFC placement problem and an Integer Linear Programming (ILP) formulation is provided. A greedy heuristic solution is also provided based on an improved two-step mapping algorithm. The obtained experimental results show that the proposed algorithm can automatically place VNFs at the optimal locations and find the optimal routing paths for each online request. This algorithm can increase the average request acceptance rate by about 17.6% and provide more than 20-fold reduction of the computational complexity compared to the Greedy algorithm. The feasibility of this approach is demonstrated via NetFPGA-10G prototype implementation.
Endocytic Adaptor Protein HIP1R Controls Intracellular Trafficking of Epidermal Growth Factor Receptor in Neuronal Dendritic Development

Huntington-interacting protein 1-related protein (HIP1R) was identified on the basis of its structural homology with HIP1. Based on its domain structure, HIP1R is a putative endocytosis-related protein. Our previous study had shown that knockdown of HIP1R induces a dramatic decrease of dendritic growth and branching in cultured rat hippocampal neurons. However, the underlying mechanism remains elucidative. In this study, we found that knockdown of HIP1R impaired the endocytosis of activated epidermal growth factor receptor (EGFR) and the consequent activation of the downstream ERK and Akt proteins. Meanwhile, it blocked the EGF-induced dendritic outgrowth. We also showed that the HIP1R fragment, amino acids 633–822 (HIP1R633–822), interacted with EGFR and revealed a dominant negative effect in disrupting the HIP1R-EGFR interaction-mediated neuronal development. Collectively, these results reveal a novel mechanism that HIP1R plays a critical role in neurite initiation and dendritic branching in cultured hippocampal neurons via mediating the endocytosis of EGFR and downstream signaling.
Skyline Diagram: Efficient Space Partitioning for Skyline Queries

Skyline queries are important in many application domains. In this paper, we propose a novel structure <italic>Skyline Diagram</italic>, which given a set of points, partitions the plane into a set of regions, referred to as skyline polyominos. All query points in the same skyline polyomino have the same skyline query results. Similar to <italic><inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2923914.gif"/></alternatives></inline-formula>th-order Voronoi diagram</italic> commonly used to facilitate <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq2-2923914.gif"/></alternatives></inline-formula> nearest neighbor (<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq3-2923914.gif"/></alternatives></inline-formula>NN) queries, skyline diagram can be used to facilitate skyline queries and many other applications. However, it may be computationally expensive to build the skyline diagram. By exploiting some interesting properties of skyline, we present several efficient algorithms for building the diagram with respect to three kinds of skyline queries, quadrant, global, and dynamic skylines. In addition, we propose an approximate skyline diagram which can significantly reduce the space cost. Experimental results on both real and synthetic datasets show that our algorithms are efficient and scalable.
Constant Factor Approximation Algorithm for Uniform Hard Capacitated Knapsack Median Problem

In this paper, we give first constant factor approximation for capacitated knapsack median problem (CKM) for hard uniform capacities, violating the budget only by an additive factor of $f_{max}$ where $f_{max}$ is the maximum cost of a facility opened by the optimal and violating capacities by $(2+\epsilon)$ factor. Natural LP for the problem is known to have an unbounded integrality gap when any one of the two constraints is allowed to be violated by a factor less than $2$. Thus, we present a result which is very close to the best achievable from the natural LP. To the best of our knowledge, the problem has not been studied earlier. For capacitated facility location problem with uniform capacities, a constant factor approximation algorithm is presented violating the capacities a little ($1 + \epsilon$). Though constant factor results are known for the problem without violating the capacities, the result is interesting as it is obtained by rounding the solution to the natural LP, which is known to have an unbounded integrality gap without violating the capacities. Thus, we achieve the best possible from the natural LP for the problem. The result shows that natural LP is not too bad. Finally, we raise some issues with the proofs of the results presented in \cite{capkmByrkaFRS2013} for capacitated $k$-facility location problem (C$k$FLP). \cite{capkmByrkaFRS2013} presents $O(1/\epsilon^2)$ approximation violating the capacities by a factor of $(2 + \epsilon)$ using dependent rounding. We first fix these issues using our techniques. Also, it can be argued that (deterministic) pipage rounding cannot be used to open the facilities instead of dependent rounding. Our techniques for CKM provide a constant factor approximation for CkFLP violating the capacities by $(2 + \epsilon)$.
Metastatic Colorectal Cancer in the Era of Personalized Medicine: A More Tailored Approach to Systemic Therapy

Colorectal cancer is the second most common malignancy diagnosed in Canada. Despite declining incidence and mortality rates in recent years, there is still a significant number of cases that are metastatic at presentation. Fluoropyrimidine-based chemotherapy was the backbone of colorectal cancer treatment, but the addition of irinotecan and oxaliplatin to form combination regimens has significantly improved overall survival. In the past decade, the development of novel biologic agents including therapies directed against vascular endothelial growth factor and epidermal growth factor receptor has further altered the landscape of metastatic colorectal cancer treatment. However, clinical trials have demonstrated that not all patients respond to these therapies similarly and consideration must be given to individual patient- and tumor-related factors. A more tailored and biomarker driven approach to treatment selection can optimize outcomes and avoid unnecessary adverse effects. In this review article, we offer a comprehensive overview of the panel of clinical- and tumor-associated characteristics that influence treatment decisions in metastatic colorectal cancer and how this sets the foundation for a more personalized treatment strategy in oncology.
Bartter syndrome: causes, diagnosis, and treatment

Bartter syndrome is an inherited renal tubular disorder caused by a defective salt reabsorption in the thick ascending limb of loop of Henle, resulting in salt wasting, hypokalemia, and metabolic alkalosis. Mutations of several genes encoding the transporters and channels involved in salt reabsorption in the thick ascending limb cause different types of Bartter syndrome. A poor phenotype–genotype relationship due to the interaction with other cotransporters and different degrees of compensation through alternative pathways is currently reported. However, phenotypic identification still remains the first step to guide the suspicion of Bartter syndrome. Given the rarity of the syndrome, and the lack of genetic characterization in most cases, limited clinical evidence for treatment is available and the therapy is based mainly on the comprehension of renal physiology and relies on the physician’s personal experiences. A better understanding of the mutated channels and transporters could possibly generate targets for specific treatment in the future, also encompassing drugs aiming to correct deficiencies in folding or plasma membrane expression of the mutated proteins.
Defending Against Adversarial Attack Towards Deep Neural Networks Via Collaborative Multi-Task Training

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples. This becomes a critical problem since a defender usually does not have the type of attack as <italic>a priori</italic> knowledge. Moreover, existing two-pronged defences (e.g., MagNet), which take advantage of both proactive and reactive methods, have been reported as broken under transferring attacks. To address this problem, this article proposed a novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks. The proposed defence first encodes training labels into label pairs and counters black-box attacks leveraging adversarial training supervised by the encoded label pairs. The defence further constructs a detector to identify and reject high-confidence adversarial examples that bypass the black-box defence. In addition, the proposed collaborative architecture can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed. In the experiments, we evaluated our defence against four state-of-the-art attacks on <inline-formula><tex-math notation="LaTeX">$MNIST$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mi>N</mml:mi><mml:mi>I</mml:mi><mml:mi>S</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="wen-ieq1-3014390.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$CIFAR10$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mi>F</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mn>10</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="wen-ieq2-3014390.gif"/></alternatives></inline-formula> datasets. The results showed that our defending method achieved up to 96.3 percent classification accuracy on black-box adversarial examples, and detected up to 98.7 percent of the high confidence adversarial examples. It only decreased the model accuracy on benign example classification by 2.1 percent for the <inline-formula><tex-math notation="LaTeX">$CIFAR10$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mi>F</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mn>10</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="wen-ieq3-3014390.gif"/></alternatives></inline-formula> dataset.