OpenRaiser/Intern-Atlas huggingface dataset:
Relatives in the same university faculty: nepotism or merit?
Venue: arXiv cs.DL 2018
Year: 2018

In many countries culture, practice or regulations inhibit the co-presence of relatives within the university faculty. We test the legitimacy of such attitudes and provisions, investigating the phenomenon of nepotism in Italy, a nation with high rates of favoritism. We compare the individual research performance of "children" who have "parents" in the same university against that of the "non-children" with the same academic rank and seniority, in the same field. The results show non-significant differences in performance. Analyses of career advancement show that children's research performance is on average superior to that of their colleagues who did not advance. The study's findings do not rule out the existence of nepotism, which has been actually recorded in a low percentage of cases, but do not prove either the most serious presumed consequences of nepotism, namely that relatives who are poor performers are getting ahead of non-relatives who are better performers. In light of these results, many attitudes and norms concerning parental ties in academia should be reconsidered.
Application of Deep Learning on Predicting Prognosis of Acute Myeloid Leukemia with Cytogenetics, Age, and Mutations
Venue: arXiv cs.LG 2018
Year: 2018

We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD). In our DL network, autoencoders are stacked to form a hierarchical DL model from which raw data are compressed and organized and high-level features are extracted. The network is written in R language and is designed to predict prognosis of AML for a given case (DTD of more than or less than 730 days). The DL network achieves an excellent accuracy of 83% in predicting prognosis. As a proof-of-concept study, our preliminary results demonstrate a practical application of DL in future practice of prognostic prediction using next-gen sequencing (NGS) data.
Bisimulation as path type for guarded recursive types
Venue: arXiv cs.LO 2018
Year: 2018

In type theory, coinductive types are used to represent processes, and are thus crucial for the formal verification of non-terminating reactive programs in proof assistants based on type theory, such as Coq and Agda. Currently, programming and reasoning about coinductive types is difficult for two reasons: The need for recursive definitions to be productive, and the lack of coincidence of the built-in identity types and the important notion of bisimilarity. Guarded recursion in the sense of Nakano has recently been suggested as a possible approach to dealing with the problem of productivity, allowing this to be encoded in types. Indeed, coinductive types can be encoded using a combination of guarded recursion and universal quantification over clocks. This paper studies the notion of bisimilarity for guarded recursive types in Ticked Cubical Type Theory, an extension of Cubical Type Theory with guarded recursion. We prove that, for any functor, an abstract, category theoretic notion of bisimilarity for the final guarded coalgebra is equivalent (in the sense of homotopy type theory) to path equality (the primitive notion of equality in cubical type theory). As a worked example we study a guarded notion of labelled transition systems, and show that, as a special case of the general theorem, path equality coincides with an adaptation of the usual notion of bisimulation for processes. In particular, this implies that guarded recursion can be used to give simple equational reasoning proofs of bisimilarity. This work should be seen as a step towards obtaining bisimilarity as path equality for coinductive types using the encodings mentioned above.
Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions
Venue: arXiv cs.NI 2018
Year: 2018

Caching at the wireless edge is a promising way of boosting spectral efficiency and reducing energy consumption of wireless systems. These improvements are rooted in the fact that popular contents are reused, asynchronously, by many users. In this article, we first introduce methods to predict the popularity distributions and user preferences, and the impact of erroneous information. We then discuss the two aspects of caching systems, namely content placement and delivery. We expound the key differences between wired and wireless caching, and outline the differences in the system arising from where the caching takes place, e.g., at base stations, or on the wireless devices themselves. Special attention is paid to the essential limitations in wireless caching, and possible tradeoffs between spectral efficiency, energy efficiency and cache size.
Multilevel Planarity
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we introduce and study the multilevel-planarity testing problem, which is a generalization of upward planarity and level planarity. Let $G = (V, E)$ be a directed graph and let $\ell: V \to \mathcal P(\mathbb Z)$ be a function that assigns a finite set of integers to each vertex. A multilevel-planar drawing of $G$ is a planar drawing of $G$ such that the $y$-coordinate of each vertex $v \in V$ is $y(v) \in \ell(v)$, and each edge is drawn as a strictly $y$-monotone curve. We present linear-time algorithms for testing multilevel planarity of embedded graphs with a single source and of oriented cycles. Complementing these algorithmic results, we show that multilevel-planarity testing is NP-complete even in very restricted cases.
Convolutional Neural Network Quantization using Generalized Gamma Distribution
Venue: arXiv cs.NE 2018
Year: 2018

As edge applications using convolutional neural networks (CNN) models grow, it is becoming necessary to introduce dedicated hardware accelerators in which network parameters and feature-map data are represented with limited precision. In this paper we propose a novel quantization algorithm for energy-efficient deployment of the hardware accelerators. For weights and biases, the optimal bit length of the fractional part is determined so that the quantization error is minimized over their distribution. For feature-map data, meanwhile, their sample distribution is well approximated with the generalized gamma distribution (GGD), and accordingly the optimal quantization step size can be obtained through the asymptotical closed form solution of GGD. The proposed quantization algorithm has a higher signal-to-quantization-noise ratio (SQNR) than other quantization schemes previously proposed for CNNs, and even can be more improved by tuning the quantization parameters, resulting in efficient implementation of the hardware accelerators for CNNs in terms of power consumption and memory bandwidth.
The VQR, Italy's second national research assessment: Methodological failures and ranking distortions
Venue: arXiv cs.DL 2018
Year: 2018

The 2004-2010 VQR, completed in July 2013, was Italy's second national research assessment exercise. The VQR performance evaluation followed a pattern also seen in other nations, in being based on a selected subset of products. In this work we identify the exercise's methodological weaknesses and measure the distortions that result from them in the university performance rankings. First we create a scenario in which we assume the efficient selection of the products to be submitted by the universities and from this simulate a set of rankings applying the precise VQR rating criteria. Next we compare these "VQR rankings" with those that would derive from application of more appropriate bibliometrics. Finally we extend the comparison to university rankings based on the entire scientific production for the period, as indexed in the Web of Science.
Finite Relation Algebras with Normal Representations
Venue: arXiv cs.LO 2018
Year: 2018

One of the traditional applications of relation algebras is to provide a setting for infinite-domain constraint satisfaction problems. Complexity classification for these computational problems has been one of the major open research challenges of this application field. The past decade has brought significant progress on the theory of constraint satisfaction, both over finite and infinite domains. This progress has been achieved independently from the relation algebra approach. The present article translates the recent findings into the traditional relation algebra setting, and points out a series of open problems at the interface between model theory and the theory of relation algebras.
The relationship between the number of authors of a publication, its citations and the impact factor of the publishing journal: Evidence from Italy
Venue: arXiv cs.DL 2018
Year: 2018

Empirical evidence shows that co-authored publications achieve higher visibility and impact. The aim of the current work is to test for the existence of a similar correlation for Italian publications. We also verify if such correlation differs: i) by subject category and macro-area; ii) by document type; iii) over the course of time. The results confirm world-level evidence, showing a consistent and significant linear growth in the citability of a publication with number of co-authors, in almost all subject categories. The effects are more remarkable in the fields of Social Sciences and Art & Humanities than in the Sciences-a finding not so obvious scrutinizing previous studies. Moreover, our results partly disavow the positive association between number of authors and prestige of the journal, as measured by its impact factor.
Privacy Preserving Multi-Agent Planning with Provable Guarantees
Venue: arXiv cs.AI 2018
Year: 2018

In privacy-preserving multi-agent planning, a group of agents attempt to cooperatively solve a multi-agent planning problem while maintaining private their data and actions. Although much work was carried out in this area in past years, its theoretical foundations have not been fully worked out. Specifically, although algorithms with precise privacy guarantees exist, even their most efficient implementations are not fast enough on realistic instances, whereas for practical algorithms no meaningful privacy guarantees exist. Secure-MAFS, a variant of the multi-agent forward search algorithm (MAFS) is the only practical algorithm to attempt to offer more precise guarantees, but only in very limited settings and with proof sketches only. In this paper we formulate a precise notion of secure computation for search-based algorithms and prove that Secure MAFS has this property in all domains.
Securing IoT Apps with Fine-grained Control of Information Flows
Venue: arXiv cs.CR 2018
Year: 2018

Internet of Things is growing rapidly, with many connected devices now available to consumers. With this growth, the IoT apps that manage the devices from smartphones raise significant security concerns. Typically, these apps are secured via sensitive credentials such as email and password that need to be validated through specific servers, thus requiring permissions to access the Internet. Unfortunately, even when developers are well-intentioned, such apps can be non-trivial to secure so as to guarantee that user's credentials do not leak to unauthorized servers on the Internet. For example, if the app relies on third-party libraries, as many do, those libraries can potentially capture and leak sensitive credentials. Bugs in the applications can also result in exploitable vulnerabilities that leak credentials. This paper presents our work in-progress on a prototype that enables developers to control how information flows within the app from sensitive UI data to specific servers. We extend FlowFence to enforce fine-grained information flow policies on sensitive UI data.
Some New Layer Architectures for Graph CNN
Venue: arXiv cs.LG 2018
Year: 2018

While convolutional neural networks (CNNs) have recently made great strides in supervised classification of data structured on a grid (e.g. images composed of pixel grids), in several interesting datasets, the relations between features can be better represented as a general graph instead of a regular grid. Although recent algorithms that adapt CNNs to graphs have shown promising results, they mostly neglect learning explicit operations for edge features while focusing on vertex features alone. We propose new formulations for convolutional, pooling, and fully connected layers for neural networks that make more comprehensive use of the information available in multi-dimensional graphs. Using these layers led to an improvement in classification accuracy over the state-of-the-art methods on benchmark graph datasets.
A task in a suit and a tie: paraphrase generation with semantic augmentation
Venue: arXiv cs.CL 2018
Year: 2018

Paraphrasing is rooted in semantics. We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank labels via a multi-encoder. Evaluating on MSCOCO and WikiAnswers, we find that transformers are fast and effective, and that semantic augmentation for both transformers and LSTMs leads to sizable 2-3 point gains in BLEU, METEOR and TER. More importantly, we find surprisingly large gains on human evaluations compared to previous models. Nevertheless, manual inspection of generated paraphrases reveals ample room for improvement: even our best model produces human-acceptable paraphrases for only 28% of captions from the CHIA dataset (Sharma et al. 2018), and it fails spectacularly on sentences from Wikipedia. Overall, these results point to the potential for incorporating semantics in the task while highlighting the need for stronger evaluation.
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

Many knowledge graph embedding methods operate on triples and are therefore implicitly limited by a very local view of the entire knowledge graph. We present a new framework MOHONE to effectively model higher order network effects in knowledge-graphs, thus enabling one to capture varying degrees of network connectivity (from the local to the global). Our framework is generic, explicitly models the network scale, and captures two different aspects of similarity in networks: (a) shared local neighborhood and (b) structural role-based similarity. First, we introduce methods that learn network representations of entities in the knowledge graph capturing these varied aspects of similarity. We then propose a fast, efficient method to incorporate the information captured by these network representations into existing knowledge graph embeddings. We show that our method consistently and significantly improves the performance on link prediction of several different knowledge-graph embedding methods including TRANSE, TRANSD, DISTMULT, and COMPLEX(by at least 4 points or 17% in some cases).
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
Venue: arXiv cs.LG 2018
Year: 2018

The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence. There are several criteria available to measure the level of competence of base classifiers, such as local accuracy estimates and ranking. However, using only one criterion may lead to a poor estimation of the classifier's competence. In order to deal with this issue, we have proposed a novel dynamic ensemble selection framework using meta-learning, called META-DES. An important aspect of the META-DES framework is that multiple criteria can be embedded in the system encoded as different sets of meta-features. However, some DES criteria are not suitable for every classification problem. For instance, local accuracy estimates may produce poor results when there is a high degree of overlap between the classes. Moreover, a higher classification accuracy can be obtained if the performance of the meta-classifier is optimized for the corresponding data. In this paper, we propose a novel version of the META-DES framework based on the formal definition of the Oracle, called META-DES.Oracle. The Oracle is an abstract method that represents an ideal classifier selection scheme. A meta-feature selection scheme using an overfitting cautious Binary Particle Swarm Optimization (BPSO) is proposed for improving the performance of the meta-classifier. The difference between the outputs obtained by the meta-classifier and those presented by the Oracle is minimized. Thus, the meta-classifier is expected to obtain results that are similar to the Oracle. Experiments carried out using 30 classification problems demonstrate that the optimization procedure based on the Oracle definition leads to a significant improvement in classification accuracy when compared to previous versions of the META-DES framework and other state-of-the-art DES techniques.
Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
Venue: arXiv cs.CV 2018
Year: 2018

The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary biometric traits (e.g., face and fingerprint) based person identification, a single biometric trait alone cannot meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft biometric traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.
CariGANs: Unpaired Photo-to-Caricature Translation
Venue: arXiv cs.CV 2018
Year: 2018

Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transformation from face photos to caricatures, and CariStyGAN, which transfers the style appearance from caricatures to face photos without any geometry deformation. In this way, a difficult cross-domain translation problem is decoupled into two easier tasks. The perceptual study shows that caricatures generated by our CariGANs are closer to the hand-drawn ones, and at the same time better persevere the identity, compared to state-of-the-art methods. Moreover, our CariGANs allow users to control the shape exaggeration degree and change the color/texture style by tuning the parameters or giving an example caricature.
Social Learning with Questions
Venue: arXiv cs.GT 2018
Year: 2018

This work studies sequential social learning (also known as Bayesian observational learning), and how private communication can enable agents to avoid herding to the wrong action/state. Starting from the seminal BHW (Bikhchandani, Hirshleifer, and Welch, 1992) model where asymptotic learning does not occur, we allow agents to ask private and finite questions to a bounded subset of their predecessors. While retaining the publicly observed history of the agents and their Bayes rationality from the BHW model, we further assume that both the ability to ask questions and the questions themselves are common knowledge. Then interpreting asking questions as partitioning information sets, we study whether asymptotic learning can be achieved with finite capacity questions. Restricting our attention to the network where every agent is only allowed to query her immediate predecessor, an explicit construction shows that a 1-bit question from each agent is enough to enable asymptotic learning.
Multiple Kernel $k$-Means Clustering by Selecting Representative Kernels
Venue: arXiv cs.LG 2018
Year: 2018

To cluster data that are not linearly separable in the original feature space, $k$-means clustering was extended to the kernel version. However, the performance of kernel $k$-means clustering largely depends on the choice of kernel function. To mitigate this problem, multiple kernel learning has been introduced into the $k$-means clustering to obtain an optimal kernel combination for clustering. Despite the success of multiple kernel $k$-means clustering in various scenarios, few of the existing work update the combination coefficients based on the diversity of kernels, which leads to the result that the selected kernels contain high redundancy and would degrade the clustering performance and efficiency. In this paper, we propose a simple but efficient strategy that selects a diverse subset from the pre-specified kernels as the representative kernels, and then incorporate the subset selection process into the framework of multiple $k$-means clustering. The representative kernels can be indicated as the significant combination weights. Due to the non-convexity of the obtained objective function, we develop an alternating minimization method to optimize the combination coefficients of the selected kernels and the cluster membership alternatively. We evaluate the proposed approach on several benchmark and real-world datasets. The experimental results demonstrate the competitiveness of our approach in comparison with the state-of-the-art methods.