OpenRaiser/Intern-Atlas huggingface dataset:
Study on 3GPP Rural Macrocell Path Loss Models for Millimeter Wave Wireless Communications
Venue: arXiv cs.IT 2017
Year: 2017

Little research has been done to reliably model millimeter wave (mmWave) path loss in rural macrocell settings, yet, models have been hastily adopted without substantial empirical evidence. This paper studies past rural macrocell (RMa) path loss models and exposes concerns with the current 3rd Generation Partnership Project (3GPP) TR 38.900 (Release 14) RMa path loss models adopted from the International Telecommunications Union - Radiocommunications (ITU-R) Sector. This paper shows how the 3GPP RMa large-scale path loss models were derived for frequencies below 6 GHz, yet they are being asserted for use up to 30 GHz, even though there has not been sufficient work or published data to support their validity at frequencies above 6 GHz or in the mmWave bands. We present the background of the 3GPP RMa path loss models and their use of odd correction factors not suitable for rural scenarios, and show that the multi-frequency close-in free space reference distance (CI) path loss model is more accurate and reliable than current 3GPP and ITU-R RMa models. Using field data and simulations, we introduce a new close-in free space reference distance with height dependent path loss exponent model (CIH), that predicts rural macrocell path loss using an effective path loss exponent that is a function of base station antenna height. This work shows the CI and CIH models can be used from 500 MHz to 100 GHz for rural mmWave coverage and interference analysis, without any discontinuity at 6 GHz as exists in today's 3GPP and ITU-R RMa models.
High Efficiency Power Side-Channel Attack Immunity using Noise Injection in Attenuated Signature Domain
Venue: arXiv cs.CR 2017
Year: 2017

With the advancement of technology in the last few decades, leading to the widespread availability of miniaturized sensors and internet-connected things (IoT), security of electronic devices has become a top priority. Side-channel attack (SCA) is one of the prominent methods to break the security of an encryption system by exploiting the information leaked from the physical devices. Correlational power attack (CPA) is an efficient power side-channel attack technique, which analyses the correlation between the estimated and measured supply current traces to extract the secret key. The existing countermeasures to the power attacks are mainly based on reducing the SNR of the leaked data, or introducing large overhead using techniques like power balancing. This paper presents an attenuated signature AES (AS-AES), which resists SCA with minimal noise current overhead. AS-AES uses a shunt low-drop-out (LDO) regulator to suppress the AES current signature by 400x in the supply current traces. The shunt LDO has been fabricated and validated in 130 nm CMOS technology. System-level implementation of the AS-AES along with noise injection, shows that the system remains secure even after 50K encryptions, with 10x reduction in power overhead compared to that of noise addition alone.
Hybrid Precoding for Multi-Group Physical Layer Multicasting
Venue: arXiv cs.IT 2017
Year: 2017

Next generation of wireless networks will likely rely on large-scale antenna systems, either in the form of massive multi-input-multi-output (MIMO) or millimeter wave (mmWave) systems. Therefore, the conventional fully-digital precoders are not suitable for physical layer multicasting as they require a dedicated radio frequency chain per antenna element. In this paper, we show that in a multi-group multicasting system with an arbitrary number of transmit antennas, $G$ multicasting groups, and an arbitrary number of users in each group, one can achieve the performance of any fully-digital precoder with just $G$ radio frequency chains using the proposed hybrid multi-group multicasting structure.
Dynamic Computational Time for Visual Attention
Venue: arXiv cs.CV 2017
Year: 2017

We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM). Rather than attention with a fixed number of steps for each input image, the model learns to decide when to stop on the fly. To achieve this, we add an additional continue/stop action per time step to RAM and use reinforcement learning to learn both the optimal attention policy and stopping policy. The modification is simple but could dramatically save the average computational time while keeping the same recognition performance as RAM. Experimental results on CUB-200-2011 and Stanford Cars dataset demonstrate the dynamic computational model can work effectively for fine-grained image recognition.The source code of this paper can be obtained from https://github.com/baidu-research/DT-RAM
Does Outside-In Teaching Improve the Learning of Object-Oriented Programming?
Venue: arXiv cs.SE 2017
Year: 2017

Object-oriented programming (OOP) is widely used in the software industry and university introductory courses today. Following the structure of most textbooks, such courses frequently are organised starting with the concepts of imperative and structured programming and only later introducing OOP. An alternative approach is to begin directly with OOP following the Outside-In teaching method as proposed by Meyer. Empirical results for the effects of Outside-In teaching on students and lecturers are sparse, however. We describe the conceptual design and empirical evaluation of two OOP introductory courses from different universities based on Outside-In teaching. The evaluation results are compared to those from a third course serving as the control group, which was taught OOP the "traditional" way. We evaluate the initial motivation and knowledge of the participants and the learning outcomes. In addition, we analyse results of the end-term exams and qualitatively analyse the results of interviews with the lecturers and tutors. Regarding the learning outcomes, the results show no signif- icant differences between the Outside-In and the "traditional" teaching method. In general, students found it harder to solve and implement algorithmic problems than to understand object oriented (OO) concepts. Students taught OOP by the Outside-In method, however, were less afraid that they would not pass the exam at the end of term and understood the OO paradigm more quickly. Therefore, the Outside-In method is no silver bullet for teaching OOP regarding the learning outcomes but has positive effects on motivation and interest.
From Deep to Shallow: Transformations of Deep Rectifier Networks
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we introduce transformations of deep rectifier networks, enabling the conversion of deep rectifier networks into shallow rectifier networks. We subsequently prove that any rectifier net of any depth can be represented by a maximum of a number of functions that can be realized by a shallow network with a single hidden layer. The transformations of both deep rectifier nets and deep residual nets are conducted to demonstrate the advantages of the residual nets over the conventional neural nets and the advantages of the deep neural nets over the shallow neural nets. In summary, for two rectifier nets with different depths but with same total number of hidden units, the corresponding single hidden layer representation of the deeper net is much more complex than the corresponding single hidden representation of the shallower net. Similarly, for a residual net and a conventional rectifier net with the same structure except for the skip connections in the residual net, the corresponding single hidden layer representation of the residual net is much more complex than the corresponding single hidden layer representation of the conventional net.
A Polynomial-time Algorithm to Achieve Extended Justified Representation
Venue: arXiv cs.GT 2017
Year: 2017

We consider a committee voting setting in which each voter approves of a subset of candidates and based on the approvals, a target number of candidates are to be selected. In particular we focus on the axiomatic property called extended justified representation (EJR). Although a committee satisfying EJR is guaranteed to exist, the computational complexity of finding such a committee has been an open problem and explicitly mentioned in multiple recent papers. We settle the complexity of finding a committee satisfying EJR by presenting a polynomial-time algorithm for the problem. Our algorithmic approach may be useful for constructing other voting rules in multi-winner voting.
An Empirical Approach for Modeling Fuzzy Geographical Descriptors
Venue: arXiv cs.AI 2017
Year: 2017

We present a novel heuristic approach that defines fuzzy geographical descriptors using data gathered from a survey with human subjects. The participants were asked to provide graphical interpretations of the descriptors `north' and `south' for the Galician region (Spain). Based on these interpretations, our approach builds fuzzy descriptors that are able to compute membership degrees for geographical locations. We evaluated our approach in terms of efficiency and precision. The fuzzy descriptors are meant to be used as the cornerstones of a geographical referring expression generation algorithm that is able to linguistically characterize geographical locations and regions. This work is also part of a general research effort that intends to establish a methodology which reunites the empirical studies traditionally practiced in data-to-text and the use of fuzzy sets to model imprecision and vagueness in words and expressions for text generation purposes.
Application of a Shallow Neural Network to Short-Term Stock Trading
Venue: arXiv cs.NE 2017
Year: 2017

Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock's year-long historical data. A chi-squared analysis found that the neural network can accurately and appropriately decide whether to buy or sell shares for a given stock, showing that a neural network can make simple decisions about the stock market.
A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients
Venue: arXiv cs.CV 2017
Year: 2017

Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity. Nevertheless, delineation task is usually still manually performed, which is inefficient and operator-dependent. Several attempts of automatizing this process have reported. however, marginal results when analyzing organs in the optic region. In this work we present a deep learning classification scheme based on augmented-enhanced features to automatically segment organs at risk (OARs) in the optic region -optic nerves, optic chiasm, pituitary gland and pituitary stalk-. Fifteen MR images with various types of brain tumors were retrospectively collected to undergo manual and automatic segmentation. Mean Dice Similarity coefficients around 0.80 were reported. Incorporation of proposed features yielded to improvements on the segmentation. Compared with support vector machines, our method achieved better performance with less variation on the results, as well as a considerably reduction on the classification time. Performance of the proposed approach was also evaluated with respect to manual contours. In this case, results obtained from the automatic contours mostly lie on the variability of the observers, showing no significant differences with respect to them. These results suggest therefore that the proposed system is more accurate than other presented approaches, up to date, to segment these structures. The speed, reproducibility, and robustness of the process make the proposed deep learning-based classification system a valuable tool for assisting in the delineation task of small OARs in brain cancer.
A Paradigm Shift: Detecting Human Rights Violations Through Web Images
Venue: arXiv cs.CV 2017
Year: 2017

The growing presence of devices carrying digital cameras, such as mobile phones and tablets, combined with ever improving internet networks have enabled ordinary citizens, victims of human rights abuse, and participants in armed conflicts, protests, and disaster situations to capture and share via social media networks images and videos of specific events. This paper discusses the potential of images in human rights context including the opportunities and challenges they present. This study demonstrates that real-world images have the capacity to contribute complementary data to operational human rights monitoring efforts when combined with novel computer vision approaches. The analysis is concluded by arguing that if images are to be used effectively to detect and identify human rights violations by rights advocates, greater attention to gathering task-specific visual concepts from large-scale web images is required.
The Impact of Crowds on News Engagement: A Reddit Case Study
Venue: arXiv cs.SI 2017
Year: 2017

Today, users are reading the news through social platforms. These platforms are built to facilitate crowd engagement, but not necessarily disseminate useful news to inform the masses. Hence, the news that is highly engaged with may not be the news that best informs. While predicting news popularity has been well studied, it has not been studied in the context of crowd manipulations. In this paper, we provide some preliminary results to a longer term project on crowd and platform manipulations of news and news popularity. In particular, we choose to study known features for predicting news popularity and how those features may change on reddit.com, a social platform used commonly for news aggregation. Along with this, we explore ways in which users can alter the perception of news through changing the title of an article. We find that news on reddit is predictable using previously studied sentiment and content features and that posts with titles changed by reddit users tend to be more popular than posts with the original article title.
Delay versus Stickiness Violation Trade-offs for Load Balancing in Large-Scale Data Centers
Venue: arXiv cs.NI 2017
Year: 2017

Most load balancing techniques implemented in current data centers tend to rely on a mapping from packets to server IP addresses through a hash value calculated from the flow five-tuple. The hash calculation allows extremely fast packet forwarding and provides flow `stickiness', meaning that all packets belonging to the same flow get dispatched to the same server. Unfortunately, such static hashing may not yield an optimal degree of load balancing, e.g., due to variations in server processing speeds or traffic patterns. On the other hand, dynamic schemes, such as the Join-the-Shortest-Queue (JSQ) scheme, provide a natural way to mitigate load imbalances, but at the expense of stickiness violation. In the present paper we examine the fundamental trade-off between stickiness violation and packet-level latency performance in large-scale data centers. We establish that stringent flow stickiness carries a significant performance penalty in terms of packet-level delay. Moreover, relaxing the stickiness requirement by a minuscule amount is highly effective in clipping the tail of the latency distribution. We further propose a bin-based load balancing scheme that achieves a good balance among scalability, stickiness violation and packet-level delay performance. Extensive simulation experiments corroborate the analytical results and validate the effectiveness of the bin-based load balancing scheme.
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Venue: arXiv cs.LG 2017
Year: 2017

Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction.
Neutral evolution and turnover over centuries of English word popularity
Venue: arXiv cs.CL 2017
Year: 2017

Here we test Neutral models against the evolution of English word frequency and vocabulary at the population scale, as recorded in annual word frequencies from three centuries of English language books. Against these data, we test both static and dynamic predictions of two neutral models, including the relation between corpus size and vocabulary size, frequency distributions, and turnover within those frequency distributions. Although a commonly used Neutral model fails to replicate all these emergent properties at once, we find that modified two-stage Neutral model does replicate the static and dynamic properties of the corpus data. This two-stage model is meant to represent a relatively small corpus (population) of English books, analogous to a `canon', sampled by an exponentially increasing corpus of books in the wider population of authors. More broadly, this mode -- a smaller neutral model within a larger neutral model -- could represent more broadly those situations where mass attention is focused on a small subset of the cultural variants.
Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Besides, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation. The data set is manually annotated with Universal Dependencies.
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
Venue: arXiv cs.CV 2017
Year: 2017

We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our system exploits visual saliency and semantics to non-uniformly sample in space and time for generating hyperlapses. In addition, users can optionally choose objects of interest for customizing the hyperlapses. We first stabilize an input $360^\circ$ video by smoothing the rotation between adjacent frames and then compute regions of interest and saliency scores. An initial hyperlapse is generated by optimizing the saliency and motion smoothness followed by the saliency-aware frame selection. We further smooth the result using an efficient 2D video stabilization approach that adaptively selects the motion model to generate the final hyperlapse. We validate the design of our system by showing results for a variety of scenes and comparing against the state-of-the-art method through a user study.
Transition Watchpoints: Teaching Old Debuggers New Tricks
Venue: arXiv cs.PL 2017
Year: 2017

Reversible debuggers and process replay have been developed at least since 1970. This vision enables one to execute backwards in time under a debugger. Two important problems in practice are that, first, current reversible debuggers are slow when reversing over long time periods, and, second, after building one reversible debugger, it is difficult to transfer that achievement to a new programming environment. The user observes a bug when arriving at an error. Searching backwards for the corresponding fault may require many reverse steps. Ultimately, the user prefers to write an expression that will transition to false upon arriving at the fault. The solution is an expression-transition watchpoint facility based on top of snapshots and record/replay. Expression-transition watch- points are implemented as binary search through the timeline of a program execution, while using the snapshots as landmarks within that timeline. This allows for debugging of subtle bugs that appear only after minutes or more of program execution. When a bug occurs within seconds of program startup, repeated debugging sessions suffice. Reversible debugging is preferred for bugs seen only after minutes. This architecture allows for an efficient and easy-to-write snapshot-based reversibe debugger on top of a conventional debugger. The validity of this approach was tested by developing four personalities (for GDB, MATLAB, Perl, and Python), with each personality typically requiring just 100 lines of code.
Single Image Super Resolution - When Model Adaptation Matters
Venue: arXiv cs.CV 2017
Year: 2017

In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external priors modeling are the key ingredients. The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors. In this paper we propose a novel deep convolutional neural network carefully designed for robustness and efficiency at both learning and testing. Moreover, we propose a couple of model adaptation strategies to the internal contents of the low resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors we achieve 0.1 up to 0.3dB PSNR improvements over best reported results on standard datasets. Our adaptation especially favors images with repetitive structures or under large resolutions. Moreover, it can be combined with other simple techniques, such as back-projection or enhanced prediction, for further improvements.