Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward demand-aware networking: a theory for self-adjusting networks

The physical topology is emerging as the next frontier in an ongoing effort to render communication networks more flexible. While first empirical results indicate that these flexibilities can be exploited to reconfigure and optimize the network toward the workload it serves and, e.g., providing the same bandwidth at lower infrastructure cost, only little is known today about the fundamental algorithmic problems underlying the design of reconfigurable networks. This paper initiates the study of the theory of demand-aware, self-adjusting networks. Our main position is that self-adjusting networks should be seen through the lense of self-adjusting datastructures. Accordingly, we present a taxonomy classifying the different algorithmic models of demand-oblivious, fixed demand-aware, and reconfigurable demand-aware networks, introduce a formal model, and identify objectives and evaluation metrics. We also demonstrate, by examples, the inherent advantage of demand-aware networks over state-of-the-art demand-oblivious, fixed networks (such as expanders). We conclude by observing that the usefulness of self-adjusting networks depends on the spatial and temporal locality of the demand; as relevant data is scarce, we call for community action.
Failure of Ploidy and Proliferative Fraction to Predict Long-Term Outcome After Prostatectomy

Background Historically, ploidy and S phase percentage appeared to be promising predictors for prostate cancer recurrence. Lack of uniformity and consistency hampered their development. We evaluated ploidy and S phase for prostate cancer death in a cohort of patients with long-term follow-up. Methods We identified 127 patients that had ploidy and S phase determined at the time of their radical prostatectomy for prostate cancer. With 15 years of follow-up, we determined the risk of biochemical failure and risk of death from prostate cancer. We correlated the S phase and ploidy findings with standard pathology findings. Results A total of 107 (84%) had diploid and 20 (16%) had non-diploid cancers. The median S phase was 6.6%. There was no correlation of ploidy (P = 0.472) or S phase with preoperative PSA or Gleason score. On univariate analysis, EPE, margin positivity, seminal vesicle involvement, lymph node involvement, high Gleason score and PSA > 10 ng/mL were all predictive of biochemical failure. Ploidy and S phase were not. For prostate cancer death, only Gleason score was predictive. Conclusions With long-term follow-up in our cohort, Gleason score was predictive of prostate cancer death. Ploidy and S phase were not predictive for biochemical failure or prostate cancer mortality.
Nematodes as Important Source for Omega-3 Long-Chain Fatty Acids in the Soil Food Web and the Impact in Nutrition for Higher Trophic Levels

PUFA (polyunsaturated fatty acids) content of food sources, in particular 3 long-chain PUFA with three or more double bonds, are considered essential for growth, reproduction, and neural development of higher animals. Surprisingly, and in contrast to aquatic ecosystems, 3 long-chain PUFA seem not widely available in terrestrial food webs. Far-reaching ideas indeed proclaim aquatic ecosystems as the principal source of these long-chain PUFA in the whole biosphere, including inhabitants of terrestrial ecosystems. Interestingly, de novo synthesis of 3 long-chain PUFA, which requires the presence of Δ12 (fat-2) and 3 (fat-1) desaturases absent in vertebrates, has been observed in nematodes, such as Caenorhabditis elegans. This raises the question if nematodes or other soil invertebrates present an important trophic link offering substantial supply in these valuable nutritional compounds in terrestrial food webs. This work followed the dietary routing of fatty acids of different C. elegans strains, including mutants defective in the PUFA biosynthesis, to two omnivorous Collembola species, Folsomia candida and Protaphorura fimata. The laboratory approach comprised microcosms offering binary links under various feeding conditions and analyzed growth, fecundity and dietary preference of consumers. Collembola did not prefer individual C. elegans strains as food source but they clearly reflected the PUFA-richness or -poorness of their nematode prey in their neutral lipid fraction. Moreover, Collembola did benefit from 3 long-chain PUFA rich diet, as shown by significant weight gain and increased number of laid eggs. Interestingly, the comparatively high PUFA-content of Collembola’s phospholipid fraction remained unchanged, even in response to almost PUFA-depleted nematode prey, suggesting that these Collembola species also possess the metabolic capability to de novo synthesize PUFA, including 3 long-chain forms. These findings broaden the basis of long-chain PUFA sources in terrestrial food webs and question the impact of aquatic ecosystems as principal source.
Discrete Sampling using Semigradient-based Product Mixtures

We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in machine learning, such as determinantal point processes and Ising models. Locally-moving Markov chain Monte Carlo algorithms, such as the Gibbs sampler, are commonly used for inference in such models, but their convergence is, at times, prohibitively slow. This is often caused by state-space bottlenecks that greatly hinder the movement of such samplers. We propose a novel sampling strategy that uses a specific mixture of product distributions to propose global moves and, thus, accelerate convergence. Furthermore, we show how to construct such a mixture using semigradient information. We illustrate the effectiveness of combining our sampler with existing ones, both theoretically on an example model, as well as practically on three models learned from real-world data sets.
Study on three-dimensional flow characteristics on the port of an engine

The current assessment methods of intake port by adopting discharge coefficient and swirl intensity measurement from a steady-flow test rig is a high-cost and time-consuming process. The three-dimensional flow model was built; flow characteristics and the law of variation in discharge were analysed in this paper. The measurement in the test rig verify the numerical results. The local optimisation and simulation analysis of the intake port were made in this study and reversed flow in the inside of the bending segment was eliminated, which increased the minimum circulation area and lowered the intake resistance. As a result, the flow coefficient of the intake port was greatly increased.
Identification of Inhibitors of MMPS Enzymes via a Novel Computational Approach

Matrix metalloproteases (MMPs) are a family of zinc-dependent proteinases that play complex and diverse roles in metabolism, which are vital for physiological development. In this paper, we present a novel method to identify peptide binding to seven matrix metalloproteases. First, we propose a novel sampling criteria for constructing a training set for each new peptide motif. Then, we select nine physicochemical properties of amino acids and compute their auto-cross covariance to effectively extract features for both natural and non-natural amino acids. Finally, we adopt random forest to predict binding values of each peptide motif respectively with seven MMPs. Our method verifies on 1300 known peptide motifs binding to seven MMPs and achieved preeminent Pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) on all seven MMPs, especially of 0.9181 and 9.3827 on MMP-7. We predict binding values of 4000 peptide motifs and identify peptides preferentially bind to MMP-2 and MMP-7. We herein report 4 novel inhibitor candidates of Asp-Ile-Phe, Asp-Ile-Tyr, Asp-Ile-Lys and Hser-Gly-Phe with high potency and selectivity binding to MMP-2, as well as 6 novel inhibitor candidates of Chg-Ile-Ile, Chg-Ile-Leu, Chg-Ile-Glu, Chg-Ile-Met, Chg-Val-Ile and Chg-Val-Leu selectively binding to MMP-7. Our findings facilitate the identification of inhibitors with good potency as well as desirable selectivity, providing significant insights of candidate inhibitor drugs.
Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks

Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source singer to the target singer. This paper presents a method that learns from unpaired data for singing style transfer using generative adversarial networks.
Enhanced 5G V2X services using sidelink device-to-device communications

In this paper, we present advances of sidelink (SL) device-to-device (D2D) communications as a key-enabling technology for 5G enhanced vehicular-to-everything (eV2X) communications. We provide an overview about the resource allocation and scheduling of different SL D2D modes under in-coverage and out-of-coverage application scenarios. Moreover, we present the scheduling for SL D2D V2X communications, which relies on semi-persistent scheduling (SPS) as proposed within the 3GPP specification. Simulations were carried out to evaluate the performance in terms of collision probability assuming different values of the key parameters such as resource reselection interval (RRI) and resource selection window. We finally discuss about the open technical challenges for ultra-reliable and low-latency communications as distilled from the 5G V2X use cases introduced in 3GPP Rel.15. A cooperative resource allocation and scheduling solution is also given, where a more detailed version is considered our future work on this topic.
Method for generating expert recommendations to advise students on ubiquitous learning experiences

Ubiquitous learning environments overcome the limitations of a traditional learning environment or class and extend it by making reality the idea of anywhere and anytime learning, allowing people to access better learning experiences in their daily lives. In ubiquitous environments, it is essential to provide a personalized education. To offer personalized recommendations to students in ubiquitous contexts, it is not enough to consider only student profile. It is also necessary to take into account contextual aspects. This work presents a method to generate recommendations of experts in the subject that a student wants to learn, considering the experiences of these experts with other students, availability and physical closeness with the student in face-to-face consultation. An example of application of the method is provided.
Classification of gastric slices based on deep learning and sparse representation

In this paper, a classification method based on deep learning and sparse representation is proposed for gastric slice images. First of all, the convolution features of images are extracted through the convolutional neural network. Then an overcomplete learning dictionary of convolution features can be gained by K-SVD algorithm. Convolution features can obtain their sparse representation through decomposition on the overcomplete dictionary. Finally, a linear SVM classifier is used to classify the sparse representation of convolution features instead of the commonly used nonlinear classifiers, so as to achieve the purpose of classifying gastric slices. Experiments show that using linear SVM reduces the computation cost of classifier and achieves better classification effect than convolutional neural network with sigmoid output layer or SVM with RBF kernel. Therefore, the method proposed in this paper has better classification effect and lower computation cost.
READ: Reliability Enhancement in 3D-Memory Exploiting Asymmetric SER Distribution

3D-memory is one of promising applications in 3D-IC technology. With a 3D integration technology, the effective density of memories can increase and the interconnect distance from processor to memory can be shortened. Due to its stacked structure, the upper dies behave as shields blocking outer particles from reaching lower dies, and it makes error rate of the top layer largest among all layers. From a heat perspective, the lower dies would suffer from reliability problems since the lower dies are placed on top of logic die. The heat dissipation can more influence lower dies than upper dies. This creates unequal a reliability distribution for each layer in 3D-memories. A novel ECC organization scheme for 3D-memory to secure reliable operations under soft error rate (SER) profiles is introduced in this paper. The proposed scheme does not require additional redundant arrays. Instead, it utilizes unused spare columns of relatively reliable layer memories to store additional check-bits of less reliable layer memories. It forms a heterogeneous ECC organization across different layers which enhances ECC capabilities in less reliable layers. In addition, redundancy sharing scheme for yield enhancement can be implemented with the proposed scheme. Experimental results show that a memory with the proposed method can tolerate more than three times of a bit-error rate compared to the conventional memory.
Controller and topology co-optimization for consensus of multi-agent systems

In this paper, the co-optimization problem between controller and topology for consensus of multi-agent systems is investigated. Firstly, the distributed control protocol for consensus of multi-agent systems is optimized for the given quadratic performance index. The proposed optimal controller depends on the Laplace matrix of the topological graph. Secondly, in order to further reduce the communication energy of the topology, but not affect the convergence speed of multi-agent systems, the topology optimization algorithm and the eigenvalue optimization method for multi-agent systems are presented, which balances the communication energy and control energy of the systems. Finally, the simulation results show that the topology optimization based on the controller optimization is able to improve the system performance.
Control under tracking and weights: Parameterization of stabilizing controllers and H performance

This paper considers the problem of measurement output feedback H∞ control subject to asymptotic tracking and unsatble output weights. Based on two Sylvester equations, a specifically structured controller is conducted, and the necessary and sufficient condition is given. By this parameterization, additional performance objective is further tackled via an LMI-based approach and a reduced-order controller synthesis is also discussed. An example is given to show the effectiveness of the current results.
Data driven point packing for fast clustering

Modern data acquisition has forced the field of large data on the scientific community. This papers gives a rapid technique for clustering data. The technique is based on an off-line process for packing points chosen from a data space. Once the off-line process has been run, the clustering may be re-run on different data sets of the same type in linear time. The clustering takes the form of a Voronoi tiling of the data space with the tile centres being the elements of the point packing. The data items within each tile form the clusters. The evolutionary algorithm is an adaptation of one, based on the Conway crossover operator, that has been used to create error correcting codes over the Levenstein metric; the tile centres are a form of code, but over the Euclidean metric. The technique generalizes smoothly to other metric spaces and may be used on any type of data for which a distance metric can be devised. The data set used in this study captures information about codon usage bias in human genes. The clustering is validated by looking for GO term over- representation in the clusters, with significant results.
A 3D Point Cloud Segmentation Method Based on Local Convexity and Dimension Features

Segmentation of 3D point clouds is an essential part of automatic tasks, such as object classification, recognition, and localization. The segmentation results pose a direct impact on the further processing. In this paper, we present an improved region-growing algorithm based on local convexity and dimension features for 3D point clouds segmentation. The point clouds on tabletop is removed from the original dataset by using RANSAC algorithm. Then the seed point and growing rules are set according to the local convexity and dimension features. Our method can reduce the uncorrect segmentation to some extent, and reduce the impact from the selection of seed points on the segmentation results. Experiments are provided to demonstrate that the proposed algorithm outperforms the traditional region-growing one from the perspective of segmenting the adjacent objects.
Embodiment and Humiliation Moderation of Neural Responses to Others' Suffering in Female Submissive BDSM Practitioners

Giving and receiving pain are common in the practice of BDSM (bondage-discipline, dominance-submission, and sadism-masochism). Playing a submissive role during BDSM practice weakens both the behavioral and neural empathic responses of female individuals to others' suffering, suggesting that long-term BDSM experience affects BDSM practitioners' empathic ability. This study further investigates whether physical restriction during BDSM practice also modulates individuals' neural responses to others' suffering. We measured neural responses to others' suffering by recording event-related potentials (ERPs) in female submissives while they viewed painful and neutral expressions in sexual sadistic/general social contexts under ball gag Blocking and Relaxed conditions. The neural responses recorded during 92–112 ms (N1), 132–172 ms (P2), 200–340 ms (N2), early late positive potential (LPP, 400–600 ms), and late LPP (700–1,000 ms) were included in the analyses. Compared to the relaxed condition, when a ball gag was used to prevent facial muscle movement and facial mimicry, the N1, early LPP, and late LPP responses neural responses to others' suffering were inhibited. The moderation effect of ball gag blocking on the N1 and early LPP amplitudes was positively correlated with the subjective feelings of facial muscle stillness, and the blocking moderation effect on the late LPP amplitudes was positively correlated with subjective feelings of humiliation. This study is the first neuropsychological investigation of the transient BDSM-related physical restriction effects on BDSM practitioners. These findings suggest that physical restriction (via a ball gag) during BDSM practices increases the wearer's facial muscle stillness and sense of humiliation. This physical restriction inhibits both early automatic responses and late controlled processes in response to the suffering of others.
HBPred: a tool to identify growth hormone-binding proteins

Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.
Theoretical Study of the First Higher Order Mode in Grounded Graphene Nanoribbons

The dispersive properties of the first higher order (EH<inline-formula><tex-math notation="LaTeX">$_1$</tex-math> </inline-formula>) mode in uniform graphene nanoribbons over a ground plane are numerically studied to point out the characteristics of the bound-mode and leaky-mode regimes. The graphene nanoribbon is then suitably designed to operate on the EH<inline-formula><tex-math notation="LaTeX">$_1$</tex-math></inline-formula> mode as a frequency-scannable leaky-wave antenna in both the millimeter and terahertz ranges.
Road crack detection using a single stage detector based deep neural network

Condition and deterioration of public and private infrastructure is an issue that directly affects the majority of the world population. In this paper we propose the application of a Residual Neural Network to automatically detect road and pavement surface cracks. The high amount of variance in the texture of the surface and variation in illumination levels makes the task of automatically detecting defects within public and private infrastructure a difficult task. The system developed utilises a feature pyramid core with an underlying feed-forward ResNet architecture. The output from the feature pyramid then feeds into two sub-networks. One sub-network associates a class with the output from the feature pyramid. The other sub-network regresses the offset from each of the output bounding boxes of the feature pyramid to the corresponding ground truth boxes during training. The network was trained on real world data from an already established dataset. The data used to train and test on is very limited, due to the lack of available road crack datasets in the public domain. Despite the limited amount of data, the proposed method achieves a very positive results with minimal error.
Analysis of Morphology-Based Features for Classification of Crop and Weeds in Precision Agriculture

Determining the types of vegetation present in an image is a core step in many precision agriculture tasks. In this letter, we focus on pixel-based approaches for classification of crops versus weeds, especially for complex cases involving overlapping plants and partial occlusion. We examine the benefits of multiscale and content-driven morphology-based descriptors called attribute profiles. These are compared to the state-of-the-art keypoint descriptors with a fixed neighborhood previously used in precision agriculture, namely histograms of oriented gradients and local binary patterns. The proposed classification technique is especially advantageous when coupled with morphology-based segmentation on a max-tree structure, as the same representation can be reused for feature extraction. The robustness of the approach is demonstrated by an experimental evaluation on two datasets with different crop types, while being able to provide descriptors at a higher resolution. The proposed approach compared favorably to the state-of-the-art approaches without an increase in computational complexity, while being able to provide descriptors at a higher resolution.