bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facility Reallocation on the Line
Authors: de Keijzer Bart , Wojtczak Dominik 
Published: July 2018

We consider a multi-stage facility reallocation problems on the real line, where a facility is being moved between stages based on the locations reported by n agents. The aim of the reallocation mechanism is to minimize the social cost, i.e., the sum over the total distance between the facility and all agents at all stages, plus the cost incurred for moving the facility. We also study this problem both in the offline setting and online setting. In the offline case the mechanism has full knowledge of the agent locations in all future stages, and in the online setting the mechanism does not know these future locations and must decide the location of the facility on a stage-per-stage basis. For both cases, we derive the optimal mechanism, where for the online setting we show that its competitive ratio is (n+2)/(n+1). As neither of these mechanisms turns out to be strategyproof, we propose another strategyproof mechanism which has a competitive ratio of (n+3)/(n+1) for odd n and (n+4)/n for even n, which we conjecture to be the best possible. We also consider a generalization with multiple facilities and weighted agents, for which we show that the optimum can be computed in polynomial time for a fixed number of facilities.
Abducing Relations in Continuous Spaces
Authors: Sato Taisuke , Inoue Katsumi , Sakama Chiaki 
Published: July 2018

We propose a new approach to abduction, i.e., non-deductive inference to find a hypothesis H for an observation O such that H,KB |- O where KB is background knowledge. We reformulate it linear algebraically in vector spaces to abduce ``relations'', not logical formulas, to realize approximate but scalable abduction that can deal with web-scale knowledge bases. More specifically we consider the problem of abducing relations for Datalog programs with binary predicates. We treat two cases, the non-recursive case and the recursive case. In the non-recursive case, given r1(X,Y) and r3(X,Z), we abduce r2(Y,Z) so that r3(X,Z) <= r1(X,Y)&r2(Y,Z) approximately holds, by computing a matrix R2 that approximately satisfies a matrix equation R3 = min1(R1R2) containing a nonlinear function min1(x). Here R1, R2 andR3 encode as adjacency matrix r1(X,Y), r2(Y,Z) and r3(Y,Z) respectively. We apply this matrix-based abduction to rule discovery and relation discovery in a knowledge graph. The recursive case is mathematically more involved and computationally more difficult but solvable by deriving a recursive matrix equation and solving it. We illustrate concrete recursive cases including a transitive closure relation.
Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization
Authors: Du Yuxuan , Liu Tongliang , Li Yinan , Duan Runyao , Tao Dacheng 
Published: July 2018

It is NP-complete to find non-negative factors W and H with fixed rank r from a non-negative matrix X by minimizing ||X-WH^Τ ||^2. Although the separability assumption (all data points are in the conical hull of the extreme rows) enables polynomial-time algorithms, the computational cost is not affordable for big data. This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013]. The design of quantum DCA (QDCA) is challenging. In the divide step, the random projections in DCA is completed by a quantum algorithm for linear operations, which achieves the exponential speedup. We then devise a heuristic post-selection procedure which extracts the information of anchors stored in the quantum states efficiently. Under a plausible assumption, QDCA performs efficiently, achieves the quantum speedup, and is beneficial for high dimensional problems.
Faster Training Algorithms for Structured Sparsity-Inducing Norm
Authors: Gu Bin , Ju Xingwang , Li Xiang , Zheng Guansheng 
Published: July 2018

Structured-sparsity regularization is popular for sparse learning because of its flexibility of encoding the feature structures. This paper considers a generalized version of structured-sparsity regularization (especially for $l_1/l_{\infty}$ norm) with arbitrary group overlap. Due to the group overlap, it is time-consuming to solve the associated proximal operator. Although Mairal~\shortcite{mairal2010network} have proposed a network-flow algorithm to solve the proximal operator, it is still time-consuming especially in the high-dimensional setting. To address this challenge, in this paper, we have developed a more efficient solution for $l_1/l_{\infty}$ group lasso with arbitrary group overlap using an Inexact Proximal-Gradient method. In each iteration, our algorithm only requires to calculate an inexact solution to the proximal sub-problem, which can be done efficiently. On the theoretic side, the proposed algorithm enjoys the same global convergence rate as the exact proximal methods. Experiments demonstrate that our algorithm is much more efficient than network-flow algorithm, while retaining the similar generalization performance.
Refine or Represent: Residual Networks with Explicit Channel-wise Configuration
Authors: Shen Yanyan , Gao Jinyang 
Published: July 2018

The successes of deep residual learning are mainly based on one key insight: instead of learning a completely new representation y = H(x), it is much easier to learn and optimize its residual mapping F(x)= H(x)-x, as F(x) could be generally closer to zero than the non-residual function H(x). In this paper, we further exploit this insight by explicitly configuring each feature channel with a fine-grained learning style. We define two types of channel-wise learning styles: Refine and Represent. A Refine channel is learnt via the residual function yi= Fi(x) + xi with a regularization term on the channel response ||Fi(x)||, aiming to refine the input feature channel xi of the layer. A Represent channel directly learns a new representation yi = Hi(x) without calculating the residual function with reference to xi. We apply random channel-wise configuration to each residual learning block. Experimental results on the CIFAR10, CIFAR100 and ImageNet datasets demonstrate that our proposed method can substantially improve the performance of conventional residual networks including ResNet, ResNeXt and SENet.
MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling
Authors: Xu Jianpeng , Liu Xi , Wilson Tyler , Tan Pang-Ning , Hatami Pouyan , Luo Lifeng 
Published: July 2018

In climate and environmental sciences, vast amount of spatio-temporal data have been generated at varying spatial resolutions from satellite observations and computer models. Integrating such diverse sources of data has proven to be useful for building prediction models as the multi-scale data may capture different aspects of the Earth system. In this paper, we present a novel framework called MUSCAT for predictive modeling of multi-scale, spatio-temporal data. MUSCAT performs a joint decomposition of multiple tensors from different spatial scales, taking into account the relationships between the variables. The latent factors derived from the joint tensor decomposition are used to train the spatial and temporal prediction models at different scales for each location. The outputs from these ensemble of spatial and temporal models will be aggregated to generate future predictions. An incremental learning algorithm is also proposed to handle the massive size of the tensors. Experimental results on real-world data from the United States Historical Climate Network (USHCN) showed that MUSCAT outperformed other competing methods in more than 70\% of the locations.
Distributed Self-Paced Learning in Alternating Direction Method of Multipliers
Authors: Zhang Xuchao , Zhao Liang , Chen Zhiqian , Lu Chang-Tien 
Published: July 2018

Self-paced learning (SPL) mimics the cognitive process of humans, who generally learn from easy samples to hard ones. One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset. In this paper, we reformulate the self-paced learning problem into a distributed setting and propose a novel Distributed Self-Paced Learning method (DSPL) to handle large scale datasets. Specifically, both the model and instance weights can be optimized in parallel for each batch based on a consensus alternating direction method of multipliers. We also prove the convergence of our algorithm under mild conditions. Extensive experiments on both synthetic and real datasets demonstrate that our approach is superior to those of existing methods.
Adversarial Task Assignment
Authors: Hajaj Chen , Vorobeychik Yevgeniy 
Published: July 2018

The problem of task assignment to workers is of

long-standing fundamental importance. Examples of this include the classical

problem of assigning computing tasks to nodes in a distributed computing

environment, assigning jobs to robots, and crowdsourcing. Extensive research

into this problem generally addresses important issues such as uncertainty and

incentives. However, the problem of adversarial tampering with the task

assignment process has not received as much attention. We are concerned with a particular adversarial setting in

task assignment where an attacker may target a set of workers in order to

prevent the tasks assigned to these workers from being completed. For the case when all tasks are homogeneous, we provide an

efficient algorithm for computing the optimal assignment. When tasks are

heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an

algorithm for solving it approximately. Our theoretical results are accompanied

by extensive simulation results showing the effectiveness of our algorithms.
Joint Extraction of Entities and Relations Based on a Novel Graph Scheme
Authors: Wang Shaolei , Zhang Yue , Che Wanxiang , Liu Ting 
Published: July 2018

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. Most existing neural joint methods extract entities and relations separately and achieve joint learning through parameter sharing, leading to a drawback that information between output entities and relations cannot be fully exploited. In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations. Experiments on NewYork Times (NYT) corpora show that our approach outperforms the state-of-the-art methods.
Enhancing Semantic Representations of Bilingual Word Embeddings with Syntactic Dependencies
Authors: Xu Linli , Ouyang Wenjun , Ren Xiaoying , Wang Yang , Jiang Liang 
Published: July 2018

Cross-lingual representation is a technique that can both represent different languages in the same latent vector space and enable the knowledge transfer across languages. To learn such representations, most of existing works require parallel sentences with word-level alignments and assume that aligned words have similar Bag-of-Words (BoW) contexts. However, due to differences in grammar structures among different languages, the contexts of aligned words in different languages may appear at different positions of the sentence. To address this issue of different syntactics across different languages, we propose a model of bilingual word embeddings integrating syntactic dependencies (DepBiWE) by producing dependency parse-trees which encode the accurate relative positions for the contexts of aligned words. In addition, a new method is proposed to learn bilingual word embeddings from dependency-based contexts and BoW contexts jointly. Extensive experimental results on a real world dataset clearly validate the superiority of the proposed model DepBiWE on various natural language processing (NLP) tasks.
Learning Tag Dependencies for Sequence Tagging
Authors: Zhang Yuan , Chen Hongshen , Zhao Yihong , Liu Qun , Yin Dawei 
Published: July 2018

Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.
Commonsense Knowledge Aware Conversation Generation with Graph Attention
Authors: Zhou Hao , Young Tom , Huang Minlie , Zhao Haizhou , Xu Jingfang , Zhu Xiaoyan 
Published: July 2018

Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than state-of-the-art baselines.
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
Authors: Wang Zhihua , Rosa Stefano , Yang Bo , Wang Sen , Trigoni Niki , Markham Andrew 
Published: July 2018

The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality. In particular, predicting how deformable objects will react to applied forces in real time is a significant challenge. This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e.g. a robot manipulating an object will only be able to observe a partial view of the entire solid. In this work we present a framework, 3D-PhysNet, which is able to predict how a three-dimensional solid will deform under an applied force using intuitive physics modelling. In particular, we propose a new method to encode the physical properties of the material and the applied force, enabling generalisation over materials. The key is to combine deep variational autoencoders with adversarial training, conditioned on the applied force and the material properties.We further propose a cascaded architecture that takes a single 2.5D depth view of the object and predicts its deformation. Training data is provided by a physics simulator. The network is fast enough to be used in real-time applications from partial views. Experimental results show the viability and the generalisation properties of the proposed architecture.
Budget-feasible Procurement Mechanisms in Two-sided Markets
Authors: Wu Weiwei , Liu Xiang , Li Minming 
Published: July 2018

This paper considers the mechanism design problem in two-sided markets where multiple strategic buyers come with budgets to procure as much value of items as possible from the strategic sellers. Each seller holds an item with public value and is allowed to bid its private cost. Buyers could claim their budgets, not necessarily the true ones. The goal is to seek budget-feasible mechanisms that ensure sellers are rewarded enough payment and buyers' budgets are not exceeded. Our main contribution is a random mechanism that guarantees various desired theoretical guarantees like the budget feasibility, the truthfulness on the sellers' side and the buyers' side simultaneously, and constant approximation to the optimal total procured value of buyers.
Optimal Multi-Attribute Decision Making in Social Choice Problems
Authors: Sikdar Sujoy 
Published: July 2018

My thesis solves problems of decision making when alternatives are characterized by multiple attributes, under natural restrictions on agents’ preferences that are motivated by practical and cognitive considerations. Computing optimal decisions in these settings is often hard in general. Fortunately, agents’ preferences often have some natural structure, which have been studied in cognitive psychology literature. This makes several important problems tractable. I identify cases where such structure accurately models preferences in real world data, and provide efficient mechanisms to compute optimal outcomes for important social choice problems with theoretical guarantees.
KONSTRUKSI INTEROGATIF POLAR DALAM BAHASA JEPANG
Authors: Gapur Abdul 
Published: July 2018

Each language has its own characteristics, which can be seen by the interrogative construction themselves. Polar interrogative construction in Japanese will be discussed in this research. The research is kind of qualitative reasearch by using descriptive method. The data of the research is the polar interrogative construction consisted in the textbooks minnano nihongo shokyuu I and nameraka nihongo kaiwa. The theory of interrogative construction by Seimund (2011) was used as the theory in this research. The results of the research showed that the finding of polar interrogative construction was formed by the intonation, the addition of interrogative particle, and the interrogative tag. Meanwhile, the polar interrogative construction formed by the change of constituent order, verbal inflection, and disjunction was not found. The finding of polar interrogative construction in the textbook minna no nihongo shokyuu I is dominated by the polar interrogative construction which is formed by the adding of interrogative particle, while in the textbook nameraka nihongo kaiwa is dominated by the polar interrogative construction which is formed by the intonation. Abstrak Setiap bahasa memiliki karakteristik tersendiri. Karakteristik tersebut dapat dilihat dari konstruksi interogatifnya. Pada penelitian ini akan dibahas mengenai konstruksi interogatif polar dalam bahasa Jepang. Penelitian ini merupakan penelitian kualitatif dengan metode deskriptif. Data dalam penelitian ini adalah konstruksi interogatif polar yang terdapat dalam bukuMinna No Nihongo Shokyuu I, Nameraka Nihongo Kaiwa,dan komikOremonogatariChapter1karangan Kazune Kawahara danAruko.Teori yang digunakan dalam penelitian ini adalah teori konstruksi interogatif Siemund (2001). Hasil dari penelitian ini adalah konstruksi interogatif polar yang wujud terbentuk dari intonasi, penambahan partikel interogatif,dan taginterogatif. Sementarakonstruksi interogatif polar yang terbentuk dengan perubahan urutan konstituen, infleksi verbal dan disjungsi tidak ditemukan. Konstruksi interogatif polar yang wujud dalam buku Minna No Nihongo Shokyuu I dankomikOremonogatariChapter 1 didominasi oleh konstruksi interogatif polar yang terbentuk dari penambahan partikel interogatif,sementara pada buku Nameraka Nihongo Kaiwadidominasi oleh konstruksi interogatif polar yang terbentuk dari intonasi.
Two Manuscripts, One by Routley, One by Meyer: The Origins of the Routley-Meyer Semantics for Relevance Logics
Authors: Bimbo Katalin , Dunn Jon Michael , Ferenz Nicholas 
Published: July 2018

A ternary relation is often used nowadays to interpret an implication connective of a logic, a practice that became dominant in the semantics of relevance logics. This paper examines two early manuscripts --- one by Routley, another by Meyer --- in which they were developing set-theoretic semantics for various relevance logics. A standard presentation of a ternary relational semantics for, let us say, the logic of relevant implication R is quite illuminating, yet the invention of this semantics was fraught with false starts. Meyer's manuscript, in which he builds on some ideas from Routley's manuscript, essentially contains a relational semantics for which R^{ot} is sound and complete.
Counterpressure dan Efek terhadap Nyeri Persalinan Kala I Fase Aktif pada Ibu Primigravida
Authors: Harini Ririn 
Published: July 2018

Metode Counterpressure merupakan pijatan atau tekanan pada sacrum atau tulang belakang untuk menekan atau mengurangi nyeri. Counterpressure dapat dilakukan dengan menekan bagian punggung tepatnya pada sacrum seperti tekanan dengan menggunakan tangan, dengan cara telapak tangan melakukan tekanan dalam, kuat pada sacrum dengan tumit tangan dan menggerakkan melingkar kecil. Penelitian ini bertujuan untuk mengetahui Efektifitas nyeri pada ibu inpartu kala I fase aktif pada kelompok intervensi Counterpressure maupum kelompok kontrol. Metode penelitian ini menggunakandesain penelitian pra eksperimentyang dilaksanakan di Rumah Sakit Kanjuruhan Kabupaten Malang selama 4 bulan. Teknik observasi dilakukan untuk melihat intensitas nyeri sebelum dan sesudah dilakukan intervensi yang ditentukan dengan rumus uji-t. Instrument yang digunakan dengan menggunakan skala nyeri Bourbonais dan lembar observasi teknik Counterpressure. Berdasarkan uji analisis t-test dengan tingkat kemaknaan p < 0,05 menggunakan SPSS versi 17 didapatkan nilai p = 0,002 < 0,05 sehingga dapat diartikan bahwa ada perbedaan penurunan nyeri persalinan kala I fase aktif pada Ibu Primigravida sebelum dan sesudah dilakukan teknik Counterpressure. Dari hasil penelitian ini didapatkan kesimpulan bahwa ibu primigravida yang melakukan teknik CounterpressureÂ Â secara teratur dapat menurunkan nyeri persalinan secara berkala. Diharapkan metode counterpressure dapat diaplikasikan dalam asuhan keperawatan pada nyeri persalinan kala I fase aktif pada ibu primigravida.
Faces of nostalgia. Restorative and reflective nostalgia in the fine arts
Authors: Horvath Gizela 
Published: July 2018

In the present article I use the terminology introduced by Svetlana Boym of restorative nostalgia and reflective nostalgia regarding works of contemporary fine art.
Restorative nostalgia implies an effort to revive the past – but without acknowledging that the desired and idealized past never existed, therefore it cannot be restored, either. I illustrate the application of this concept through the “new-academic” direction in today’s contemporary Hungarian fine arts. The reflective nostalgia is aware of the idealizing momentum of the desired past, it reflects critically upon its own desires, and it highlights possibilities in the past regarding the present – often playfully or with irony. I illustrate the latter through the works of the Chinese Ai Wei Wei and the Romanian Mircea Cantor. I argue that reflective nostalgia is not a fruitless burial into the past, but a resource for processing the passing of time in a creative manner.
Total charges for a consumption of 100m³ of water in 2015
Authors: Meyer Chloé 
Published: July 2018

Total charges is expressed in US dollars. The amount of total charges is derived from the addition of total charges for drinking water, environmental charges, and VAT and/or other taxes.
The full data are available online. For more information, visit: www.waterstatistics.org

Cost
Drinking water
Use/Reuse