Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image recognition to achieve both effectiveness and efficiency. We propose a KD technique for learning to rank problems, called ranking distillation (RD). Specifically, we train a smaller student model to learn to rank documents/items from both the training data and the supervision of a larger teacher model. The student model achieves a similar ranking performance to that of the large teacher model, but its smaller model size makes the online inference more efficient. RD is flexible because it is orthogonal to the choices of ranking models for the teacher and student. We address the challenges of RD for ranking problems. The experiments on public data sets and state-of-the-art recommendation models showed that RD achieves its design purposes: the student model learnt with RD has less than an half size of the teacher model while achieving a ranking performance similar tothe teacher model and much better than the student model learnt without RD.
Caging Polygonal Objects Using Formationally Similar Three-Finger Hands

Caging offers a robust strategy for grasping objects with robot hands. This letter describes an efficient caging-to-grasping algorithm for polygonal objects using minimalistic three-finger robot hands. This letter describes how to cage and then grasp polygonal objects, using single actuator triangular three-finger formations, whose shape is determined by any desired immobilizing grasp of the polygonal object. While the hand's configuration space is four-dimensional, the algorithm uses the hand's two-dimensional contact space, which represents all two- and three-finger contacts along the grasped object boundary. This letter describes how the problem of computing the critical cage formation that allows the object to escape the hand is reduced to a search along a caging graph constructed in the hand's contact space. Starting from a desired immobilizing grasp, the graph is searched for the critical cage formation, which is used to determine the caging regions surrounding the immobilizing grasp. Any three-finger placement within these regions guarantees robust object grasping. The technique is demonstrated with a detailed computational example and a video clip, which shows caging experiments with a single actuator three-finger robot hand.
Deep Variational Network Embedding in Wasserstein Space

Network embedding, aiming to embed a network into a low dimensional vector space while preserving the inherent structural properties of the network, has attracted considerable attentions recently. Most of the existing embedding methods embed nodes as point vectors in a low-dimensional continuous space. In this way, the formation of the edge is deterministic and only determined by the positions of the nodes. However, the formation and evolution of real-world networks are full of uncertainties, which makes these methods not optimal. To address the problem, we propose a novel Deep Variational Network Embedding in Wasserstein Space (DVNE) in this paper. The proposed method learns a Gaussian distribution in the Wasserstein space as the latent representation of each node, which can simultaneously preserve the network structure and model the uncertainty of nodes. Specifically, we use 2-Wasserstein distance as the similarity measure between the distributions, which can well preserve the transitivity in the network with a linear computational cost. Moreover, our method implies the mathematical relevance of mean and variance by the deep variational model, which can well capture the position of the node by the mean vectors and the uncertainties of nodes by the variance. Additionally, our method captures both the local and global network structure by preserving the first-order and second-order proximity in the network. Our experimental results demonstrate that our method can effectively model the uncertainty of nodes in networks, and show a substantial gain on real-world applications such as link prediction and multi-label classification compared with the state-of-the-art methods.
Low Affinity DnaA-ATP Recognition Sites in E. coli oriC Make Non-equivalent and Growth Rate-Dependent Contributions to the Regulated Timing of Chromosome Replication

Although the mechanisms that precisely time initiation of chromosome replication in bacteria remain unclear, most clock models are based on accumulation of the active initiator protein, DnaA-ATP. During each cell division cycle, sufficient DnaA-ATP must become available to interact with a distinct set of low affinity recognition sites in the unique chromosomal replication origin, oriC, and assemble the pre-replicative complex (orisome) that unwinds origin DNA and helps load the replicative helicase. The low affinity oriC-DnaA-ATP interactions are required for the orisome’s mechanical functions, and may also play a role in timing of new rounds of DNA synthesis. To further examine this possibility, we constructed chromosomal oriCs with equal preference for DnaA-ADP or DnaA-ATP at one or more low affinity recognition sites, thereby lowering the DnaA-ATP requirement for orisome assembly, and measured the effect of the mutations on cell cycle timing of DNA synthesis. Under slow growth conditions, mutation of any one of the six low affinity DnaA-ATP sites in chromosomal oriC resulted in initiation earlier in the cell cycle, but the shift was not equivalent for every recognition site. Mutation of τ2 caused a greater change in initiation age, suggesting its occupation by DnaA-ATP is a temporal bottleneck during orisome assembly. In contrast, during rapid growth, all origins with a single mutated site displayed wild-type initiation timing. Based on these observations, we propose that E. coli uses two different, DnaA-ATP-dependent initiation timing mechanisms; a slow growth timer that is directly coupled to individual site occupation, and a fast growth timer comprising DnaA-ATP and additional factors that regulate DnaA access to oriC. Analysis of origins with paired mutated sites suggests that Fis is an important component of the fast growth timing mechanism.
Hyperspectral Images Classification Using Energy Profiles of Spatial and Spectral Features

This paper proposes a spatial feature extraction method based on energy of the features for classification of the hyperspectral data. A proposed orthogonal filter set extracts spatial features with maximum energy from the principal components and then, a profile is constructed based on these features. The important characteristic of the proposed approach is that the filter sets coefficients are extracted from statistical properties of data, thus they are more consistent with the type and texture of the remotely sensed images compared with those of other filters such as Gabor. To assess the performance of the proposed feature extraction method, the extracted features are fed into a support vector machine (SVM) classifier. Experiments on the widely used hyperspectral images namely, Indian Pines, and Salinas data sets reveal that the proposed approach improves the classification results in comparison with some recent spectral spatial classification methods.
Causal Inference in Time Series via Supervised Learning

Causal inference in time series is an important problem in many fields. Traditional methods use regression models for this problem. The inference accuracies of these methods depend greatly on whether or not the model can be well fitted to the data, and therefore we are required to select an appropriate regression model, which is difficult in practice. This paper proposes a supervised learning framework that utilizes a classifier instead of regression models. We present a feature representation that employs the distance between the conditional distributions given past variable values and show experimentally that the feature representation provides sufficiently different feature vectors for time series with different causal relationships. Furthermore, we extend our framework to multivariate time series and present experimental results where our method outperformed the model-based methods and the supervised learning method for i.i. d. data.
Deliberation period during easy and difficult decisions: re-examining Libet’s “veto” window in a more ecologically valid framework

Abstract Whether consciousness plays a causal role in cognitive processing remains debated. According to Benjamin Libet, consciousness is needed to deliberate and veto an action that is initiated unconsciously. Libet offered that the deliberation window takes place between the time of conscious intent (W) and action (MR). We further examined this deliberation–veto hypothesis by measuring the length of the temporal window (W-MR) when making easy and difficult choices. If Libet were correct that the W-MR is intended for evaluation and cancelation, we should expect a shorter W-MR for an easy decision since less deliberation is presumably needed. Instead, we observed a less intuitive effect: The W-MR window in the easy trials was longer than the W-MR window in the difficult ones. Our results suggest several interpretations including the idea that consciousness may play a causal role in decision making but not in a straightforward manner as assumed by Libet’s veto hypothesis.
HaaS: Cloud-Based Real-Time Data Analytics with Heterogeneity-Aware Scheduling

Real-time data analytics has become increasingly important in modern times as many organizations and companies are generating and analyzing high volume of data constantly. Despite of the impressive technical development, it remains a challenging job to analyze the stream data effectively and efficiently because traditional hardware and software lack specific designs and optimizations for those emerging requirements. In this paper, we discuss our experience on real-time data analytics, with our in-house processing framework HaaS. HaaS is designed to exploit existing data analytics tools and libraries as well as distributed computing technologies to embrace heterogeneous computation resources in the cloud. HaaS utilizes hierarchical clustering to partition physical topology of clusters weighted with task topology information into densely connected sub-graphs. HaaS is also equipped with a heterogeneity-aware scheduling algorithm to facilitate holistic optimization over multiple running tasks with various service level agreements. To the best of our knowledge, HaaS is the first ever streaming analytical framework providing users with flexible and optimized usage with CPUs, GPUs and FPGAs in the cloud. Users with stream processing tasks can easily enjoy remarkable advantages of CPUs, GPUs and FPGAs in throughput, power consumption and monetary cost over others. In our empirical evaluations with highly diversified workloads, HaaS saves over 18% on power consumption and 24% on monetary cost over existing system design architecture, while the overall throughput of HaaS remains no lower than 90% of the theoretical limit.
Spectral Video in Image-Guided Microsurgical Applications: Integrating Imaging Technology into the Clinical Environment and Ergonomic Considerations

Numerous visualization tools are involved in surgeon's decision making during a procedure. Image-guided navigation systems such as computed tomography and magnetic resonance have become an integral part of many surgical procedures. In tumor removal microsurgeries, the distinction between a tumor tissue and surrounding normal tissues are often negligible, highly impacted by color contrast and illumination of operative field. To enhance surgical decision making, we investigate the use of real-time spectral imaging in operating room. Coupling a spectral camera with a surgical microscope, however, is challenging due to numerous standards and ergonomic requirements of operating room. In this paper, we fulfill these ergonomic considerations and describe the process of integration of spectral camera in the clinical environment.
User-Click-Data-Based Fine-Grained Image Recognition via Weakly Supervised Metric Learning

We present a novel fine-grained image recognition framework using user click data, which can bridge the semantic gap in distinguishing categories that are similar in visual. As query set in click data is usually large-scale and redundant, we first propose a click-feature-based query-merging approach to merge queries with similar semantics and construct a compact click feature. Afterward, we utilize this compact click feature and convolutional neural network (CNN)-based deep visual feature to jointly represent an image. Finally, with the combined feature, we employ the metriclearning-based template-matching scheme for efficient recognition. Considering the heavy noise in the training data, we introduce a reliability variable to characterize the image reliability, and propose a weakly-supervised metric and template leaning with smooth assumption and click prior (WMTLSC) method to jointly learn the distance metric, object templates, and image reliability. Extensive experiments are conducted on a public Clickture-Dog dataset and our newly established Clickture-Bird dataset. It is shown that the click-data-based query merging helps generating a highly compact (the dimension is reduced to 0.9%) and dense click feature for images, which greatly improves the computational efficiency. Also, introducing this click feature into CNN feature further boosts the recognition accuracy. The proposed framework performs much better than previous state-of-the-arts in fine-grained recognition tasks.
Coherent Plane-Wave Compounding Based on Normalized Autocorrelation Factor

Coherent plane-wave compounding (CPWC) is an effective method to improve the image quality of ultrasound plane-wave imaging (PWI), which coherently sums the delay-and-sum (DAS) beamforming outputs of PWI at different steering angles. However, due to the data-independent nature, the DAS obtains limited imaging resolution and contrast. Therefore, various adaptive beamforming methods have been presented to achieve improved imaging quality. In this paper, an adaptive weighting factor named normalized autocorrelation factor (NAF), which is derived by the normalized autocorrelation function (NACF), is proposed for CPWC. Simulated and experimental data provided by the plane wave imaging challenge in medical ultrasound organizers are used to evaluate the proposed method. Results show that the NAF-weighted CPWC can achieve an improved imaging quality in terms of lateral resolution, contrast ratio (CR), and contrast-to-noise ratio. In simulated images, the maximum improvements of the lateral full width at half maximum and CR are 60.3% and 29.7%, respectively. For experimental images, the corresponding improvements are 32.1% and 23.3%. The NAF is also compared with the generalized coherence factor (GCF) and short-lag spatial coherence (SLSC), and the simulation and phantom experimental results show that the NAF can get better lateral resolution and speckle signal-to-noise ratio (sSNR). For in-vivo data, the NAF shows distinct advantages over GCF and SLSC in preserving anatomical structures so as to get better CR, CNR, and sSNR. In addition, with a low computational complexity and load, the proposed method has potential to be applied to ultrafast ultrasound imaging for high quality images.
Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization

Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models for non-white noise, etc. Conventional optimization techniques for tuning can get stuck in poor local minima and can be expensive to implement with real sensor data. To address these issues, a new “black box” Bayesian optimization strategy is developed for automatically tuning Kalman filters. In this approach, performance is characterized by one of two stochastic objective functions: normalized estimation error squared (NEES) when ground truth state models are available, or the normalized innovation error squared (NIS) when only sensor data is available. By intelligently sampling the parameter space to both learn and exploit a nonparametric Gaussian process surrogate function for the NEESINIS costs, Bayesian optimization can efficiently identify multiple local minima and provide uncertainty quantification on its results.
Divide and Conquer for Fast SRLG Disjoint Routing

Ensuring transmission survivability is a crucial problem for high-speed networks. Path protection is a fast and capacity-efficient approach for increasing the availability of end to end connections. The emerging SDN infrastructure makes it feasible to provide diversity routing in a practical network. For more robust path protection, it is desirable to provide an alternative path that does not share any risk resource with the active path. We consider finding the SRLG-Disjoint paths, where a Shared Risk Link Group (SRLG) is a group of network links that share a common physical resource whose failure will cause the failure of all links of the group. Since the traffic is carried on the active path most of time, it is useful that the weight of the shorter path of the disjoint path pair is minimized, and we call it Min-Min SRLG-Disjoint routing problem. The key issue faced by SRLG-Disjoint routing is the trap problem, where the SRLG-disjoint backup path (BP) can not be found after an active path (AP) is decided. Based on the min-cut of the graph, we design an efficient algorithm that can take advantage of existing search results to quickly look for the SRLG-Disjoint path pair. Our performance studies demonstrate that our algorithm can outperform other approaches with a higher routing performance while also at a much faster speed.
Pro-BDNF Contributes to Hypoxia/Reoxygenation Injury in Myocardial Microvascular Endothelial Cells: Roles of Receptors p75NTR and Sortilin and Activation of JNK and Caspase 3

The aim of this study was to identify the role of the precursor of the brain-derived neurotrophic factor (pro-BDNF) in myocardial hypoxia/reoxygenation injury (H/R) and to address the underlying mechanisms. For this purpose, myocardial microvascular endothelial cells (MMECs) exposed to a high concentration of glucose (30 mM) for 48 h were subjected to 4 h of hypoxia followed by 2 h of reoxygenation. Terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL) staining and flow-cytometric analysis were performed to detect apoptosis. Cell scratch and capillary-like-structure formation assays were employed to evaluate cell function. The levels of apoptosis-related proteins were evaluated by Western blotting and immunofluorescence assays. Our results showed that H/R resulted in MMEC injury, as indicated by significant increases in TUNEL-positive cell numbers and a reduction in MMEC migration and in capillary-like-structure formation coupled with increased pro-BDNF protein expression. In addition, overexpression of pro-BDNF in MMECs via a viral vector led to increased pro-BDNF expression, and this upregulation induced apoptosis. Mechanistic experiments revealed that H/R did not influence BDNF, JNK, and caspase 3 expression, but upregulated pro-BDNF, p75NTR, sortilin, phospho-JNK, and cleaved caspase 3 protein levels. In contrast, neutralization of endogenous pro-BDNF with an antibody significantly attenuated H/R-induced upregulation of pro-BDNF, p75NTR, sortilin, p-JNK, and cleaved caspase 3 protein levels, indicating that p75NTR-sortilin signaling and activation of JNK and caspase 3 may be involved in these effects. In conclusion, H/R-induced injury may be mediated by pro-BDNF, at least in part through the regulation of p75NTR-sortilin signaling and activation of JNK and caspase 3.
Network Event Classification for Security of IT Infrastructure

The number of devices connected over the Internet are expected to grow tremendously over the next few years. Maintaining secure communications between these network-enabled devices would be a major challenge. By carefully examining the events generated by these devices it is expected to gain some insights into their behavior and identifying if a device has been compromised. One of the major challenges in classifying the events generated by these devices is the inconsistencies in the data formats of these events and the separators between them. The approach presented in this paper is based on identifying and grouping similar events generated by these devices using an Agglomerative Hierarchical Clustering technique. To deal with the inconsistencies of formats and delimiters, some data preprocessing was used. The methodology proposed in this study was successful in identifying events stored in fifteen data files tested for analysis. The results indicate that the combination of text processing techniques in conjunction with machine learning based unsupervised learning offers promising alternatives in separating events generated by the network-enabled devices and, thus, facilitating a better understanding of their behavior and identifying potential security breaches.
ZOE: Content-Based Anomaly Detection for Industrial Control Systems

Due its complexity and a multitude of proprietary components, industrial control systems are an immanently difficult field of application for intrusion detection. Proprietary binary protocols and the lack of public specifications have forced the research community to move away from content-based detection to more abstract concepts. In this paper, we show that in contrast to prior belief the content of unknown binary protocols can very well be modeled. ZOE derives prototype models that are specific to individual types of messages in order to capture the characteristics of arbitrary binary protocols and enable detecting different forms of attacks as anomalies. In an evaluation based on 6 days of network traffic recorded at a large power plant (1,900 MW) with over 92,000 unique devices, we demonstrate that ZOE improves upon related approaches by up to an order of magnitude in detection performance, but also significantly decreases false positives.