Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimensionality Reduction Flows

Deep generative modelling using flows has gained popularity owing to the tractable exact log-likelihood estimation with efficient training and synthesis process. Trained flow models carry rich information about the structure and local variance in input data. However, a bottleneck for flow models to scale with increasing dimensions is that the latent space has same size as the high-dimensional input space. In this paper, we propose methods to reduce the latent space dimension of flow models. Our first approach includes replacing standard high dimensional prior with a learned prior from a low dimensional noise space. Further improving to achieve exact log-likelihood with reduced dimensionality, our second approach presents an improved multi-scale architecture (Dinh et al., 2016) via likelihood contribution based factorization of dimensions. Using our method over state-of-the-art flow models, we demonstrate improvements in log-likelihood score on standard image benchmarks. Our work ventures a data dependent factorization scheme which is more efficient than static counterparts in prior works.
Theme-Aware Aesthetic Distribution Prediction With Full-Resolution Photographs

Aesthetic quality assessment (AQA) is a challenging task due to complex aesthetic factors. Currently, it is common to conduct AQA using deep neural networks (DNNs) that require fixed-size inputs. The existing methods mainly transform images by resizing, cropping, and padding or use adaptive pooling to alternately capture the aesthetic features from fixed-size inputs. However, these transformations potentially damage aesthetic features. To address this issue, we propose a simple but effective method to accomplish full-resolution image AQA by combining image padding with region of image (RoM) pooling. Padding turns inputs into the same size. RoM pooling pools image features and discards extra padded features to eliminate the side effects of padding. In addition, the image aspect ratios are encoded and fused with visual features to remedy the shape information loss of RoM pooling. Furthermore, we observe that the same image may receive different aesthetic evaluations under different themes, which we call the theme criterion bias. Hence, a theme-aware model that uses theme information to guide model predictions is proposed. Finally, we design an attention-based feature fusion module to effectively use both the shape and theme information. Extensive experiments prove the effectiveness of the proposed method over state-of-the-art methods.
Cross-validation tests for cryo-EM maps using an independent particle set

Cryo-electron microscopy is a revolutionary technique that can provide 3D density maps at near-atomic resolution. However, map validation is still an open issue in the field. Despite several efforts from the community, it is possible to overfit the reconstructions to noisy data. Here, inspired by modern statistics, we develop a novel methodology that uses a small independent particle set to validate the 3D maps. The main idea is to monitor how the map probability evolves over the control set during the refinement. The method is complementary to the gold-standard procedure, which generates two reconstructions at each iteration. We low-pass filter the two reconstructions for different frequency cutoffs, and we calculate the probability of each filtered map given the control set. For high-quality maps, the probability should increase as a function of the frequency cutoff and of the refinement iteration. We also compute the similarity between the probability distributions of the two reconstructions. As higher frequencies are added to the maps, more dissimilar are the distributions. We optimized the BioEM software package to perform these calculations, and tested the method on several systems, some which were overfitted. Our results show that our method is able to discriminate the overfitted sets from the non-overfitted ones. We conclude that having a control particle set, not used for the refinement, is essential for cross-validating cryo-EM maps.
On the Relationship Between Coupling and Refactoring: An Empirical Viewpoint

Background] Refactoring has matured over the past twenty years to become part of a developer’s toolkit. However, many fundamental research questions still remain largely unexplored. [Aim] The goal of this paper is to investigate the highest and lowest quartile of refactoring-based data using two coupling metrics – the Coupling between Objects metric and the more recent Conceptual Coupling between Classes metric to answer this question. Can refactoring trends and patterns be identified based on the level of class coupling? [Method] In this paper, we analyze over six thousand refactoring operations drawn from releases of three open-source systems to address one such question. [Results] Results showed no meaningful difference in the types of refactoring applied across either lower or upper quartile of coupling for both metrics; refactorings usually associated with coupling removal were actually more numerous in the lower quartile in some cases. A lack of inheritance-related refactorings across all systems was also noted. [Conclusions] The emerging message (and a perplexing one) is that developers seem to be largely indifferent to classes with high coupling when it comes to refactoring types – they treat classes with relatively low coupling in almost the same way.
Behavior Pattern and Compiled Information Based Performance Prediction in MOOCs

With the development of MOOCs massive open online courses, increasingly more subjects can be studied online. Researchers currently show growing interest in the field of MOOCs, including dropout prediction, cheating detection and achievement prediction. Previous studies on achievement prediction mainly focused on students' video and forum behaviors, and few researchers have considered how well students perform their assignments. In this paper, we choose a C programming course as the experimental subject, which involved 1528 students. This paper mainly focuses on the students' accomplishment behaviors in programming assignments and compiled information from programming assignments. In this paper, feature sequences are extracted from the logs according to submission times, submission order and plagiarism. The experimental results show that the students who did not pass the exam had obvious sequence patterns but that the students who passed the test did not have an obvious sequence pattern. Then, we extract 23 features from the compiled information of students' programming assignments and select the most distinguishing features to predict the students' performances. The experimental results show that we can obtain an accuracy rate of 0.7049 for predicting students' performances.
CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition

Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to enable a model to generate a rich variety of samples from the entire target distribution, rather than only from a distribution confined to the training data. In other words, we would like the generative model to go beyond the observed samples and learn to generate ``unseen'', yet still plausible, data. In our work, we present CompoNet, a generative neural network for 2D or 3D shapes that is based on a part-based prior, where the key idea is for the network to synthesize shapes by varying both the shape parts and their compositions. Treating a shape not as an unstructured whole, but as a (re-)composable set of deformable parts, adds a combinatorial dimension to the generative process to enrich the diversity of the output, encouraging the generator to venture more into the ``unseen''. We show that our part-based model generates richer variety of plausible shapes compared with baseline generative models. To this end, we introduce two quantitative metrics to evaluate the diversity of a generative model and assess how well the generated data covers both the training data and unseen data from the same target distribution.
Automatic segmentation of kidney and liver tumors in CT images

Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have chosen MICCAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores.
Construction of Macro Actions for Deep Reinforcement Learning

Conventional deep reinforcement learning typically determines an appropriate primitive action at each timestep, which requires enormous amount of time and effort for learning an effective policy, especially in large and complex environments. To deal with the issue fundamentally, we incorporate macro actions, defined as sequences of primitive actions, into the primitive action space to form an augmented action space. The problem lies in how to find an appropriate macro action to augment the primitive action space. The agent using a proper augmented action space is able to jump to a farther state and thus speed up the exploration process as well as facilitate the learning procedure. In previous researches, macro actions are developed by mining the most frequently used action sequences or repeating previous actions. However, the most frequently used action sequences are extracted from a past policy, which may only reinforce the original behavior of that policy. On the other hand, repeating actions may limit the diversity of behaviors of the agent. Instead, we propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent. Our approach appends a macro action to the primitive action space once at a time and evaluates whether the augmented action space leads to promising performance or not. We perform extensive experiments and show that the constructed macro actions are able to speed up the learning process for a variety of deep reinforcement learning methods. Our experimental results also demonstrate that the macro actions suggested by our approach are transferable among deep reinforcement learning methods and similar environments. We further provide a comprehensive set of ablation analysis to validate the proposed methodology.
Dependence Control for Reliability Optimization in Vehicular Networks

Vehicular networks will play an important role in enhancing road safety, improving transportation efficiency, and providing seamless Internet service for users on the road. Reaping the benefit of vehicular networks is contingent upon meeting stringent wireless communication performance requirements, particularly in terms of delay and reliability. In this paper, a dependence control mechanism is proposed to improve the overall reliability of vehicular networks. In particular, the dependence between the communication delays of different vehicleto-vehicle (V2V) links is first modeled. Then, the concept of a concordance order, stemming from stochastic ordering theory, is introduced to show that a higher dependence can lead to a better reliability. Using this insight, a power allocation problem is formulated to maximize the concordance, thereby optimizing the overall communication reliability of the V2V system. To obtain an efficient solution to the power allocation problem, a dual update method is introduced. Simulation results verify the effectiveness of performing dependence control for reliability optimization in a vehicular network, and show that the proposed mechanism can achieve up to 25% reliability gain compared to a baseline system that uses a random power allocation.
Heat engine efficiency of the Hayward-AdS black hole

In this paper, we attempt to further study the heat engine efficiency for the regular black hole with an anti-de Sitter background where the working material is the Hayward-AdS black hole, obtain exact results for the efficiency of a rectangular engine cycle. In the extended phase space, the cosmological constant defines the thermodynamic pressure P, we study the efficiency of heat engines that perform mechanical work via the PdV terms now present in the first law of thermodynamics, and it is obtained via a thermodynamic cycle in the P-V plane. We obtain the expression of efficiency of the Hayward-AdS black hole and analyze the relationship between efficiency and entropy n-S, efficiency and pressure n-P. We also compare the similarities and differences between the Hayward-AdS black hole and the Bardeen-AdS black hole in the thermal engine efficiency. Those make us a deeper understanding of the thermal efficiency of regular black holes.
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models

Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters. In this paper, we consider high-dimensional linear regression model in which a regularization procedure such as LASSO is applied to yield a sparse model. To establish a simultaneous post-model selection inference, we propose a method of contraction and expansion (MOCE) along the line of debiasing estimation that enables us to balance the bias-and-variance trade-off so that the super-sparsity assumption may be relaxed. We establish key theoretical results for the proposed MOCE procedure from which the expanded model can be selected with theoretical guarantees and simultaneous confidence regions can be constructed by the joint asymptotic normal distribution. In comparison with existing methods, our proposed method exhibits stable and reliable coverage at a nominal significance level with substantially less computational burden, and thus it is trustworthy for its application in solving real-world problems.
Exact BER Performance Analysis for Downlink NOMA Systems Over Nakagami- $m$ Fading Channels

In this paper, the performance of a promising technology for the next generation wireless communications, non-orthogonal multiple access (NOMA), is investigated. In particular, the bit error rate (BER) performance of downlink NOMA systems over Nakagami- $m$ flat fading channels, is presented. Under various conditions and scenarios, the exact BER of downlink NOMA systems considering successive interference cancellation (SIC) is derived. The transmitted signals are randomly generated from quadrature phase shift keying (QPSK) and two NOMA systems are considered; two users’ and three users’ systems. The obtained BER expressions are then used to evaluate the optimum power allocation for two different objectives, achieving fairness and minimizing average BER. The two objectives can be used in a variety of applications such as satellite applications with constrained transmitted power. Numerical results and Monte Carlo simulations perfectly match with the derived BER analytical results and provide valuable insight into the advantages of optimum power allocation which show the full potential of downlink NOMA systems.
Co-degrees Resilience for Perfect Matchings in Random Hypergraphs

In this paper we prove an optimal co-degrees resilience property for the binomial $k$-uniform hypergraph model $H_{n,p}^k$ with respect to perfect matchings. That is, for a sufficiently large $n$ which is divisible by $k$, and $p\geq C_k\log {n}/n$, we prove that with high probability every subgraph $H\subseteq H^k_{n,p}$ with minimum co-degree (meaning, the number of supersets every set of size $k-1$ is contained in) at least $(1/2+o(1))np$ contains a perfect matching.
Random attractor for stochastic Hindmarsh-Rose equations with multiplicative noise

The longtime and global pullback dynamics of stochastic Hindmarsh-Rose equations with multiplicative noise on a three-dimensional bounded domain in neurodynamics is investigated in this work. The existence of a random attractor for this random dynamical system is proved through the exponential transformation and uniform estimates showing the pullback absorbing property and the pullback asymptotically compactness of this cocycle in the $L^2$ Hilbert space.
Upper limit on the fraction of alien civilizations that develop communication technology

We re-examine the likelihood for alien civilizations to develop communication technology on the basis of the general assumption that life elsewhere could have a non-carbon chemical foundation. We particularized the discussion to a complex silicon-based biochemistry in a nitrogen solvent, and elaborate on the environment in which such a chemistry is feasible, and if so, on what scales. More concretely, we determine the region outside the habitable zone where such organisms can grow and flourish and after that we study how our findings impact the recently derived upper limit on the fraction of living intelligent species that develop communication technology $\langle \xi_{\rm biotec} \rangle$. We also compare this new restriction on $\langle \xi_{\rm biotec} \rangle$ with that resulting from the extension of the habitable zone to accommodate subsurface exolife, originating in planets with subsurface (water) oceans.
The tunnel number of all 11 and 12 crossing alternating knots

Using exhaustive techniques and results from Lackenby and many others, we compute the tunnel number of all 1655 alternating 11 and 12 crossing knots and of 881 non-alternating 11 and 12 crossing knots. We also find all 5525 Montesinos knots with 14 crossings or fewer.
A Structural Condition for Model Reference Adaptive Control Systems to Enforce Partial Performance Constraints

A fundamental challenge in model reference adaptive control synthesis is to achieve apriori, user-defined performance guarantees. To this end, a new architecture entitled settheoretic model reference adaptive control has been recently developed, where the key feature of this control synthesis methodology is to allow the weighted Euclidean norm of the system error vector, which represents the error between the state vector of an uncertain dynamical system and the state vector of a reference model (capturing a desired closed-loop system performance), to be less than a-priori, user-defined scalar performance bound. Although this architecture has the capability to enforce performance constraints on the entire system error vector, this vector can contain, for example, system error states resulting from internal compensator dynamics or simply a subset of vehicle dynamics that one does not necessarily need to enforce any performance constraints. Motivated from this standpoint, this paper presents a structural condition that allows the set-theoretic model reference adaptive control architecture to enforce partial performance constraints — enforcing performance constraints to a desired subset of the system error vector when this condition holds. An illustrative numerical example is also presented on an aircraft short period dynamics to complement the proposed theoretical contribution.
The TALP-UPC System for the WMT Similar Language Task: Statistical vs Neural Machine Translation

Although the problem of similar language translation has been an area of research interest for many years, yet it is still far from being solved. In this paper, we study the performance of two popular approaches: statistical and neural. We conclude that both methods yield similar results; however, the performance varies depending on the language pair. While the statistical approach outperforms the neural one by a difference of 6 BLEU points for the Spanish-Portuguese language pair, the proposed neural model surpasses the statistical one by a difference of 2 BLEU points for Czech-Polish. In the former case, the language similarity (based on perplexity) is much higher than in the latter case. Additionally, we report negative results for the system combination with back-translation. Our TALP-UPC system submission won 1st place for Czech->Polish and 2nd place for Spanish->Portuguese in the official evaluation of the 1st WMT Similar Language Translation task.
How to kill symbolic deobfuscation for free (or: unleashing the potential of path-oriented protections)

Code obfuscation is a major tool for protecting software intellectual property from attacks such as reverse engineering or code tampering. Yet, recently proposed (automated) attacks based on Dynamic Symbolic Execution (DSE) shows very promising results, hence threatening software integrity. Current defenses are not fully satisfactory, being either not efficient against symbolic reasoning, or affecting runtime performance too much, or being too easy to spot. We present and study a new class of anti-DSE protections coined as path-oriented protections targeting the weakest spot of DSE, namely path exploration. We propose a lightweight, efficient, resistant and analytically proved class of obfuscation algorithms designed to hinder DSE-based attacks. Extensive evaluation demonstrates that these approaches critically counter symbolic deobfuscation while yielding only a very slight overhead.
LEILA: Formal Tool for Identifying Mobile Malicious Behaviour

With the increasing diffusion of mobile technologies, nowadays mobile devices represent an irreplaceable tool to perform several operations, from posting a status on a social network to transfer money between bank accounts. As a consequence, mobile devices store a huge amount of private and sensitive information and this is the reason why attackers are developing very sophisticated techniques to extort data and money from our devices. This paper presents the design and the implementation of LEILA (formaL tool for idEntifying mobIle maLicious behAviour), a tool targeted at Android malware families detection. LEILA is based on a novel approach that exploits model checking to analyse and verify the Java Bytecode that is produced when the source code is compiled. After a thorough description of the method used for Android malware families detection, we report the experiments we have conducted using LEILA. The experiments demonstrated that the tool is effective in detecting malicious behaviour and, especially, in localizing the payload within the code: we evaluated real-world malware belonging to several widespread families obtaining an accuracy ranging between 0.97 and 1.
Noise processing by networks

Cell behaviour is determined by complex molecular regulatory networks. Signalling networks are particularly important since they are responsible for the robust transmission of noisy environmental information to the cell's nucleus. However, although important signalling pathways have been well studied, and the manner of noise propagation through regulatory networks has been discussed, the relationship between network architecture and the cell's ability to process environmental noise is not well understood. To approach this problem in this thesis we derive a mathematical formula relating a network's structure to its noise processing ability. We noticed that noise processing is highly affected by the networks complexity, and in particular by the number and length of the weighted paths from the noisy input(s) to the output. In order to explore the utility of this mathematical expression, we apply it to the regulatory network for pluripotency in mouse embryonic stem (ES) cells and assess the effects of network topology on the propagation of noise through this system. We conclude by using the underlying theory to explain the interaction patterns in the ES cell's transcriptional circuit.