OpenRaiser/Intern-Atlas huggingface dataset:
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an alternative for predicting upcoming machine failures based on relevant data points. In this paper, we describe three different machine learning tasks that can be used for that purpose: (i) predicting Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF intervals of an equipment. Our results show that trained machine learning models can outperform benchmarks resembling human judgments in all three tasks. This suggests that machine learning offers a viable alternative to currently deployed plasma etching equipment maintenance strategies and decision making processes.
Matrix approach to synchronizing automata
Venue: arXiv cs.FL 2019
Year: 2019

A word $w$ of letters on edges of underlying graph $Γ$ of deterministic finite automaton (DFA) is called synchronizing if $w$ sends all states of the automaton to a unique state. J. Černy discovered in 1964 a sequence of $n$-state complete DFA possessing a minimal synchronizing word of length $(n-1)^2$. The hypothesis, well known today as Černy conjecture, claims that $(n-1)^2$ is a precise upper bound on the length of such a word over alphabet $Σ$ of letters on edges of $Γ$ for every complete $n$-state DFA. The hypothesis was formulated distinctly in 1966 by Starke. A special classes of matrices induced by words in the alphabet of labels on edges of the underlying graph of DFA are used for the study of synchronizing automata.
Channel Correlation Diversity in MU-MIMO Systems -- Analysis and Measurements
Venue: arXiv cs.IT 2019
Year: 2019

In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signal-to-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.
Large-scale 3D Mapping of Subarctic Forests
Venue: arXiv cs.RO 2019
Year: 2019

The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit mining. In this paper, we explore possibilities of large-scale lidar mapping in a boreal forest. Computational and sensory requirements with regards to contemporary hardware are considered as well. The lidar mapping is often based on the SLAM technique relying on pose graph optimization, which fuses the Iterative Closest Point (ICP) algorithm, Global Navigation Satellite System (GNSS) positioning, and Inertial Measurement Unit (IMU) measurements. To handle those sensors directly within the ICP minimization process, we propose an alternative approach of embedding external constraints. Furthermore, a novel formulation of a cost function is presented and cast into the problem of handling uncertainties from GNSS and lidar points. To test our approach, we acquired a large-scale dataset in the Foret Montmorency research forest. We report on the technical problems faced during our winter deployments aiming at building 3D maps using our new cost function. Those maps demonstrate both global and local consistency over 4.1km.
Evaluating the Applicability of Bandwidth Allocation Models for EON Slot Allocation
Venue: arXiv cs.NI 2019
Year: 2019

Bandwidth Allocation Models (BAMs) configure and handle resource allocation (bandwidth, LSPs, fiber, slots) in networks in general (IP/MPLS/DS-TE, optical domain, other). In this paper, BAMs are considered for elastic optical networks slot allocation targeting an improvement in resource utilization. The paper focuses initially on proposing a BAM basic configuration parameter mapping suitable for elastic optical circuits. Following that, MAM, RDM and ATCS BAMs are applied for elastic optical networks resource allocation and the overall network resource utilization is evaluated. A set of simulation results and BAM behaviors are presented as a proof of concept to evaluate BAM applicability for elastic optical network slot allocation. Authors argue that a slot allocation model for EON based on BAMs may improve utilization by dynamically managing the aggregated traffic profile.
Extractors for small zero-fixing sources
Venue: arXiv cs.CC 2019
Year: 2019

A random variable $X$ is an $(n,k)$-zero-fixing source if for some subset $V\subseteq[n]$, $X$ is the uniform distribution on the strings $\{0,1\}^n$ that are zero on every coordinate outside of $V$. An $ε$-extractor for $(n,k)$-zero-fixing sources is a mapping $F:\{0,1\}^n\to\{0,1\}^m$, for some $m$, such that $F(X)$ is $ε$-close in statistical distance to the uniform distribution on $\{0,1\}^m$ for every $(n,k)$-zero-fixing source $X$. Zero-fixing sources were introduced by Cohen and Shinkar in [10] in connection with the previously studied extractors for bit-fixing sources. They constructed, for every $μ>0$, an efficiently computable extractor that extracts a positive fraction of entropy, i.e., $Ω(k)$ bits, from $(n,k)$-zero-fixing sources where $k\geq(\log\log n)^{2+μ}$. In this paper we present two different constructions of extractors for zero-fixing sources that are able to extract a positive fraction of entropy for $k$ essentially smaller than $\log\log n$. The first extractor works for $k\geq C\log\log\log n$, for some constant $C$. The second extractor extracts a positive fraction of entropy for $k\geq \log^{(i)}n$ for any fixed $i\in \mathbb{N}$, where $\log^{(i)}$ denotes $i$-times iterated logarithm. The fraction of extracted entropy decreases with $i$. The first extractor is a function computable in polynomial time in~$n$ (for $ε=o(1)$, but not too small); the second one is computable in polynomial time when $k\leqα\log\log n/\log\log\log n$, where $α$ is a positive constant. The subject studied in this paper is closely related to Ramsey theory. We use methods developed in Ramsey theory and our results can also be interpreted as a contribution to this field.
On the Resource Utilization of Multi-Connectivity Transmission for URLLC Services in 5G New Radio
Venue: arXiv cs.NI 2019
Year: 2019

Multi-connectivity with packet duplication, where the same data packet is duplicated and transmitted from multiple transmitters, is proposed in 5G New Radio as a reliability enhancement feature. This paper presents an analytical study of the outage probability enhancement with multi-connectivity, and analyses its cost in terms of resource usage. The performance analysis is further compared against conventional single-connectivity transmission. Our analysis shows that, for transmission with a given block error rate target, multi-connectivity results in more than an order of magnitude outage probability improvement over the baseline single-connectivity scheme. However, such gains are achieved at the cost of almost doubling the amount of radio resources used. Multi-connectivity should thus be selectively used such that its benefits can be harnessed for critical users, while the price to pay in terms of resource utilization is simultaneously minimized.
Subframe resource optimization for massive machine device access in LTE networks
Venue: arXiv cs.NI 2019
Year: 2019

Synchronous massive machine device access can lead to severe congestion in the random access channel (RACH) of LTE networks. With scarce frequency resources, effective means must be developed to combat this key challenge. In this letter, the efficient allocation of frequency resources is considered as an optimization problem to be solved with a utility function. Based on this and a method of estimating the number of machine devices, an adaptive subframe allocation scheme is proposed. Numerical and simulation results verify the effectiveness of the proposed frame adaptation scheme in combating RACH congestion.
Discovering Episodes with Compact Minimal Windows
Venue: arXiv cs.LG 2019
Year: 2019

Discovering the most interesting patterns is the key problem in the field of pattern mining. While ranking or selecting patterns is well-studied for itemsets it is surprisingly under-researched for other, more complex, pattern types. In this paper we propose a new quality measure for episodes. An episode is essentially a set of events with possible restrictions on the order of events. We say that an episode is significant if its occurrence is abnormally compact, that is, only few gap events occur between the actual episode events, when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step by first discovering frequent episodes and then rank them according to this measure. In order to compute the score we will need to compute the mean and the variance according to the independence model. As a main technical contribution we introduce a technique that allows us to compute these values. Such a task is surprisingly complex and in order to solve it we develop intricate finite state machines that allow us to compute the needed statistics. We also show that asymptotically our score can be interpreted as a P-value. In our experiments we demonstrate that despite its intricacy our ranking is fast: we can rank tens of thousands episodes in seconds. Our experiments with text data demonstrate that our measure ranks interpretable episodes high.
Beyond Technical Motives: Perceived User Behavior in Abandoning Wearable Health & Wellness Trackers
Venue: arXiv cs.HC 2019
Year: 2019

Health trackers are widely adopted to support individuals with daily health and wellness activity tracking. They can help increase steps taken, enhance sleeping pattern, improve healthy diet, and promote the overall health. Despite the growth in wearable adoption, their real-life use is still questionable. While some users derive long-term values from their trackers, others face barriers to integrate it into their daily routine. Studies have analysed technical aspects of these barriers. In this paper, we analyse the behavioural factors of discouragement and wearable abandonment strictly tied to user habits and lifestyle circumstances. A data analysis was conducted on 8 of the highly rated wearables for 2017. The analysis collected sale posts on Kijiji and Gumtree, the second sales online retailers for both the Italian and UK market, respectively. We extracted insights from the posts about user motives, highlighted technology condition and limitations, and timeframe before the abandonment. The findings revealed certain user behavioural patterns when abandoning their wearables. In addition, analysing the posts showed other motives for the posts and not strictly related to wearable abandonment.
Accessibility of Virtual Reality Locomotion Modalities to Adults and Minors
Venue: arXiv cs.HC 2019
Year: 2019

Virtual reality (VR) is an important new technology that is fun-damentally changing the way people experience entertainment and education content. Due to the fact that most currently available VR products are one size fits all, the accessibility of the content design and user interface design, even for healthy children is not well understood. It requires more research to ensure that children can have equally good user compared to adults in VR. In our study, we seek to explore accessibility of locomotion in VR between healthy adults and minors along both objective and subjective dimensions. We performed a user experience experiment where subjects completed a simple task of moving and touching underwater animals in VR using one of four different locomotion modalities, as well as real-world walking without wearing VR headsets as the baseline. Our results show that physical body movement that mirrors real-world movement exclusively is the least preferred by both adults and minors. However, within the different modalities of controller assisted locomotion there are variations between adults and minors for preference and challenge levels.
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
Venue: arXiv cs.NE 2019
Year: 2019

We analyze the performance of the 2-rate $(1+λ)$ Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a $(1+λ)$~EA variant using multiplicative update rules on the OneMax problem. We compare their efficiency for offspring population sizes ranging up to $λ=3,200$ and problem sizes up to $n=100,000$. Our empirical results show that the ranking of the algorithms is very consistent across all tested dimensions, but strongly depends on the population size. While for small values of $λ$ the 2-rate EA performs best, the multiplicative updates become superior for starting for some threshold value of $λ$ between 50 and 100. Interestingly, for population sizes around 50, the $(1+λ)$~EA with static mutation rates performs on par with the best of the self-adjusting algorithms. We also consider how the lower bound $p_{\min}$ for the mutation rate influences the efficiency of the algorithms. We observe that for the 2-rate EA and the EA with multiplicative update rules the more generous bound $p_{\min}=1/n^2$ gives better results than $p_{\min}=1/n$ when $λ$ is small. For both algorithms the situation reverses for large~$λ$.
Remarks on the Rényi Entropy of a sum of IID random variables
Venue: arXiv cs.IT 2019
Year: 2019

In this note we study a conjecture of Madiman and Wang which predicted that the generalized Gaussian distribution minimizes the Rényi entropy of the sum of independent random variables. Through a variational analysis, we show that the generalized Gaussian fails to be a minimizer for the problem.
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition
Venue: arXiv cs.SD 2019
Year: 2019

Recently, data-driven based Automatic Speech Recognition (ASR) systems have achieved state-of-the-art results. And transfer learning is often used when those existing systems are adapted to the target domain, e.g., fine-tuning, retraining. However, in the processes, the system parameters may well deviate too much from the previously learned parameters. Thus, it is difficult for the system training process to learn knowledge from target domains meanwhile not forgetting knowledge from the previous learning process, which is called as catastrophic forgetting (CF). In this paper, we attempt to solve the CF problem with the lifelong learning and propose a novel multi-task learning (MTL) training framework for ASR. It considers reserving original knowledge and learning new knowledge as two independent tasks, respectively. On the one hand, we constrain the new parameters not to deviate too far from the original parameters and punish the new system when forgetting original knowledge. On the other hand, we force the new system to solve new knowledge quickly. Then, a MTL mechanism is employed to get the balance between the two tasks. We applied our method to an End2End ASR task and obtained the best performance in both target and original datasets.
Deep Fusion Network for Image Completion
Venue: arXiv cs.CV 2019
Year: 2019

Deep image completion usually fails to harmonically blend the restored image into existing content, especially in the boundary area. This paper handles with this problem from a new perspective of creating a smooth transition and proposes a concise Deep Fusion Network (DFNet). Firstly, a fusion block is introduced to generate a flexible alpha composition map for combining known and unknown regions. The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels. In this way, it builds a bridge for structural and texture information, so that information can be naturally propagated from known region into completion. Furthermore, fusion blocks are embedded into several decoder layers of the network. Accompanied by the adjustable loss constraints on each layer, more accurate structure information are achieved. We qualitatively and quantitatively compare our method with other state-of-the-art methods on Places2 and CelebA datasets. The results show the superior performance of DFNet, especially in the aspects of harmonious texture transition, texture detail and semantic structural consistency. Our source code will be avaiable at: \url{https://github.com/hughplay/DFNet}
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation
Venue: arXiv cs.CL 2019
Year: 2019

In recent years, the generation of conversation content based on deep neural networks has attracted many researchers. However, traditional neural language models tend to generate general replies, lacking logical and emotional factors. This paper proposes a conversation content generation model that combines reinforcement learning with emotional editing constraints to generate more meaningful and customizable emotional replies. The model divides the replies into three clauses based on pre-generated keywords and uses the emotional editor to further optimize the final reply. The model combines multi-task learning with multiple indicator rewards to comprehensively optimize the quality of replies. Experiments shows that our model can not only improve the fluency of the replies, but also significantly enhance the logical relevance and emotional relevance of the replies.
Approximating Cumulative Pebbling Cost is Unique Games Hard
Venue: arXiv cs.CC 2019
Year: 2019

The cumulative pebbling complexity of a directed acyclic graph $G$ is defined as $\mathsf{cc}(G) = \min_P \sum_i |P_i|$, where the minimum is taken over all legal (parallel) black pebblings of $G$ and $|P_i|$ denotes the number of pebbles on the graph during round $i$. Intuitively, $\mathsf{cc}(G)$ captures the amortized Space-Time complexity of pebbling $m$ copies of $G$ in parallel. The cumulative pebbling complexity of a graph $G$ is of particular interest in the field of cryptography as $\mathsf{cc}(G)$ is tightly related to the amortized Area-Time complexity of the Data-Independent Memory-Hard Function (iMHF) $f_{G,H}$ [AS15] defined using a constant indegree directed acyclic graph (DAG) $G$ and a random oracle $H(\cdot)$. A secure iMHF should have amortized Space-Time complexity as high as possible, e.g., to deter brute-force password attacker who wants to find $x$ such that $f_{G,H}(x) = h$. Thus, to analyze the (in)security of a candidate iMHF $f_{G,H}$, it is crucial to estimate the value $\mathsf{cc}(G)$ but currently, upper and lower bounds for leading iMHF candidates differ by several orders of magnitude. Blocki and Zhou recently showed that it is $\mathsf{NP}$-Hard to compute $\mathsf{cc}(G)$, but their techniques do not even rule out an efficient $(1+\varepsilon)$-approximation algorithm for any constant $\varepsilon>0$. We show that for any constant $c > 0$, it is Unique Games hard to approximate $\mathsf{cc}(G)$ to within a factor of $c$. (See the paper for the full abstract.)
An Online Learning Approach for Dengue Fever Classification
Venue: arXiv cs.LG 2019
Year: 2019

This paper introduces a novel approach for dengue fever classification based on online learning paradigms. The proposed approach is suitable for practical implementation as it enables learning using only a few training samples. With time, the proposed approach is capable of learning incrementally from the data collected without need for retraining the model or redeployment of the prediction engine. Additionally, we also provide a comprehensive evaluation of machine learning methods for prediction of dengue fever. The input to the proposed pipeline comprises of recorded patient symptoms and diagnostic investigations. Offline classifier models have been employed to obtain baseline scores to establish that the feature set is optimal for classification of dengue. The primary benefit of the online detection model presented in the paper is that it has been established to effectively identify patients with high likelihood of dengue disease, and experiments on scalability in terms of number of training and test samples validate the use of the proposed model.
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Venue: arXiv cs.LG 2019
Year: 2019

In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are more cost effective than one-by-one sequential queries. Furthermore, it is often not possible to directly choose items to query. Instead, the experimenter specifies a set of constraints that generates a library of possible items, which are then selected stochastically. Motivated by these considerations, we investigate \emph{Batched Stochastic Bayesian Optimization} (BSBO), a novel Bayesian optimization scheme for choosing the constraints in order to guide exploration towards items with greater utility. We focus on \emph{site-saturation mutagenesis}, a prototypical setting of BSBO in biochemical engineering, and propose a natural objective function for this problem. Importantly, we show that our objective function can be efficiently decomposed as a difference of submodular functions (DS), which allows us to employ DS optimization tools to greedily identify sets of constraints that increase the likelihood of finding items with high utility. Our experimental results show that our algorithm outperforms common heuristics on both synthetic and two real protein datasets.
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Venue: arXiv cs.CL 2019
Year: 2019

Sentiment analysis, mostly based on text, has been rapidly developing in the last decade and has attracted widespread attention in both academia and industry. However, the information in the real world usually comes from multiple modalities, such as audio and text. Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis. We call it the DFF-ATMF (Deep Feature Fusion - Audio and Text Modality Fusion) model, which consists of two parallel branches, the audio modality based branch and the text modality based branch. Its core mechanisms are the fusion of multiple feature vectors and multiple modality attention. Experiments on the CMU-MOSI dataset and the recently released CMU-MOSEI dataset, both collected from YouTube for sentiment analysis, show the very competitive results of our DFF-ATMF model. Furthermore, by virtue of attention weight distribution heatmaps, we also demonstrate the deep features learned by using DFF-ATMF are complementary to each other and robust. Surprisingly, DFF-ATMF also achieves new state-of-the-art results on the IEMOCAP dataset, indicating that the proposed fusion strategy also has a good generalization ability for multimodal emotion recognition.