### Top 10 Arxiv Papers Today

##### #1. From Cyber-Security Deception To Manipulation and Gratification Through Gamification
###### Xavier Bellekens, Gayan Jayasekara, Hanan Hindy, Miroslav Bures, David Brosset, Christos Tachtatzis, Robert Atkinson
With the ever growing networking capabilities and services offered to users, attack surfaces have been increasing exponentially, additionally, the intricacy of network architectures has increased the complexity of cyber-defenses, to this end, the use of deception has recently been trending both in academia and industry. Deception enables to create proactive defense systems, luring attackers in order to better defend the systems at hand. Current applications of deception, only rely on static, or low interactive environments. In this paper we present a platform that combines human-computer-interaction, analytics, gamification and deception to lure malicious users into selected traps while piquing their interests. Furthermore we analyse the interactive deceptive aspects of the platform through the addition of a narrative, further engaging malicious users into following a predefined path and deflecting attacks from key network systems.
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###### Tweets
x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
davaatulga: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/iJ8qiWK3CW (PDF)
lobsters: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/f04YiUWVOC #pdf #security https://t.co/IxtVnnX6O1
noktec: Our new research on #cyber #Deception is up on @arxiv https://t.co/9bLHpVmJxC We developed a modular platform for #deception using #manipulation #gamification and #narrative for #cyber #security ! More to come very soon !
ishamad: Interesting; Manipulation and Gratification Through Gamification ; deception defenses in control systems. https://t.co/bRueki6ymB
ComputerPapers: From Cyber-Security Deception To Manipulation and Gratification Through Gamification. https://t.co/QC7S4Ze5FV
arxiv_cshc: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/ew0vjgm4gj
arxiv_cshc: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/ew0vjg4sRJ
arxiv_cshc: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/ew0vjgm4gj
arxiv_cshc: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/ew0vjgm4gj
arxiv_cshc: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/ew0vjgm4gj
MateoMartinezOK: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
SardImperium: RT @noktec: Our new research on #cyber #Deception is up on @arxiv https://t.co/JJZ0RY8WIT We developed a modular platform for #deception using #manipulation #gamification and #narrative for #cyber #security ! More to come very soon !
btreguier: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
offethhacker: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
noktec: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
PoleAI: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
arphanetx: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
frknozr: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
mohamedlinux: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
JAX_MASTERS: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
_langly: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
hanan_yousry: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
hanan_yousry: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
hanan_yousry: RT @noktec: Our new research on #cyber #Deception is up on @arxiv https://t.co/9bLHpVmJxC We developed a modular platform for #deception…
hanan_yousry: RT @ComputerPapers: From Cyber-Security Deception To Manipulation and Gratification Through Gamification. https://t.co/QC7S4Ze5FV
Atharvvashishth: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
j0rivet: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
StryderScreams: RT @noktec: Our new research on #cyber #Deception is up on @arxiv https://t.co/9bLHpVmJxC We developed a modular platform for #deception…
shoaiburehman: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
JMasanell: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
danielmcapella: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
jfmeee: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
jkamdjou: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
x0rzz: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
Eznix2: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
christationly: RT @noktec: Our new research on #cyber #Deception is up on @arxiv https://t.co/9bLHpVmJxC We developed a modular platform for #deception…
Mbahal1: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
YanivDa: RT @x0rz: From Cyber-Security Deception To Manipulation and Gratification Through Gamification https://t.co/QGhYsGHPyv (PDF)
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Sample Sizes : None.
Authors: 7
Total Words: 5268
Unqiue Words: 1835

##### #2. Recent advances in conversational NLP : Towards the standardization of Chatbot building
###### Maali Mnasri
Dialogue systems have become recently essential in our life. Their use is getting more and more fluid and easy throughout the time. This boils down to the improvements made in NLP and AI fields. In this paper, we try to provide an overview to the current state of the art of dialogue systems, their categories and the different approaches to build them. We end up with a discussion that compares all the techniques and analyzes the strengths and weaknesses of each. Finally, we present an opinion piece suggesting to orientate the research towards the standardization of dialogue systems building.
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###### Tweets
bigdata: A survey paper on the the diversity of the approaches for building chatbots by @maali_mnasri of @opla_ai https://t.co/nQ6QMR65yn
BrundageBot: Recent advances in conversational NLP : Towards the standardization of Chatbot building. Maali Mnasri https://t.co/Ghgh6e2ANg
krishnamrith12: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/6vmjsSsRiH #ml @b_santra https://t.co/fKLopxSh0f
arxivml: "Recent advances in conversational NLP : Towards the standardization of Chatbot building", Maali Mnasri https://t.co/yYvKGk5JeF
SciFi: Recent advances in conversational NLP : Towards the standardization of Chatbot building. https://t.co/au06RNCCco
arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikoDtq
arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikoDtq
arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
tkym1220: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/bdvPxD1SkI
sigitpurnomo: RT @arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
morioka: RT @arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
puneethmishra: RT @arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikoDtq
puneethmishra: RT @arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
sussenglish: RT @SciFi: Recent advances in conversational NLP : Towards the standardization of Chatbot building. https://t.co/au06RNCCco
thapraveensingh: RT @arxiv_cscl: Recent advances in conversational NLP : Towards the standardization of Chatbot building https://t.co/jl2SikGeS0
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Sample Sizes : None.
Authors: 1
Total Words: 4941
Unqiue Words: 1943

##### #3. Online continual learning with no task boundaries
###### Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio
Continual learning is the ability of an agent to learn online with a non-stationary and never-ending stream of data. A key component for such never-ending learning process is to overcome the catastrophic forgetting of previously seen data, a problem that neural networks are well known to suffer from. The solutions developed so far often relax the problem of continual learning to the easier task-incremental setting, where the stream of data is divided into tasks with clear boundaries. In this paper, we break the limits and move to the more challenging online setting where we assume no information of tasks in the data stream. We start from the idea that each learning step should not increase the losses of the previously learned examples through constraining the optimization process. This means that the number of constraints grows linearly with the number of examples, which is a serious limitation. We develop a solution to select a fixed number of constraints that we use to approximate the feasible region defined by the original...
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###### Tweets
arxiv_org: Online continual learning with no task boundaries. https://t.co/5gL1KQ0Azs https://t.co/nF458kMh8l
BrundageBot: Online continual learning with no task boundaries. Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio https://t.co/RSaCnDAh2p
arxiv_in_review: #ICML2019 Online continual learning with no task boundaries. (arXiv:1903.08671v1 [cs\.LG]) https://t.co/Aj4KA4Qw7J
arxivml: "Online continual learning with no task boundaries", Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio https://t.co/frVcqhAnxH
SciFi: Online continual learning with no task boundaries. https://t.co/GbS43JNeGK
arxiv_cs_LG: Online continual learning with no task boundaries. Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio https://t.co/W32fHXnZaL
arxiv_cscv: Online continual learning with no task boundaries https://t.co/DreJ6uzoL4
arxiv_cscv: Online continual learning with no task boundaries https://t.co/DreJ6uzoL4
arxiv_cscv: Online continual learning with no task boundaries https://t.co/DreJ6uzoL4
arxiv_cscv: Online continual learning with no task boundaries https://t.co/DreJ6uQZCC
arxiv_cscv: Online continual learning with no task boundaries https://t.co/DreJ6uQZCC
mvaldenegro: RT @arxiv_org: Online continual learning with no task boundaries. https://t.co/5gL1KQ0Azs https://t.co/nF458kMh8l
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Sample Sizes : None.
Authors: 4
Total Words: 6056
Unqiue Words: 1752

##### #4. Observation of $C\!P$ violation in charm decays
A search for charge-parity ($C\!P$) violation in $D^0 \to K^- K^+$ and $D^0 \to \pi^- \pi^+$ decays is reported, using $pp$ collision data corresponding to an integrated luminosity of 6 $\mathrm{fb}^{-1}$ collected at a center-of-mass energy of 13 TeV with the LHCb detector. The flavor of the charm meson is inferred from the charge of the pion in $D^*(2010)^+ \to D^0 \pi^+$ decays or from the charge of the muon in $\overline{B} \to D^0 \mu^-\bar{\nu}_\mu X$ decays. The difference between the $C\!P$ asymmetries in $D^0 \to K^- K^+$ and $D^0 \to \pi^- \pi^+$ decays is measured to be $\Delta A_{C\!P} = [ -18.2 \pm 3.2\,(\rm stat.) \pm 0.9\,(\rm syst.) ] \times 10^{-4}$ for $\pi$-tagged and $\Delta A_{C\!P} = [ -9 \pm 8\,(\rm stat.) \pm 5\,(\rm syst.) ] \times 10^{-4}$ for $\mu$-tagged $D^0$ mesons. Combining these with previous LHCb results leads to $$\Delta A_{C\!P} = ( -15.4 \pm 2.9) \times 10^{-4},$$ where the uncertainty includes both statistical and systematic contributions. The measured value differs from zero by more than...
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###### Tweets
arxiv_org: Observation of $C\!P$ violation in charm decays. https://t.co/mSDN3sXNEL https://t.co/yESZYc0ISe
LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→K⁺K⁻ and D⁰→π⁺π⁻ is found to be (−15.4±2.9)× 10⁻⁴., which differs from zero by more than five standard deviations. https://t.co/zm4ykefnKb
QuarkWilliams: LHCb Charm physics paper 5 of 2019. No hiding this one under the radar! This is also the first LHCb submission with 2018 data, so no update for a while, but plenty of other channels to investigate, including individual KK/pipi asymmetries. https://t.co/LCyp7bdSkv @LHCbPhysics https://t.co/O9z8GqC1l3
SmithPhys: Ok I am bad a writing and I am bad at drawing but here is a something anyway. A gif on the @LHCbExperiment new paper on CP violation in charm decay. https://t.co/SQVIi8PZTf https://t.co/TUTvxFAPgb
HEPExperPapers: Observation of $C\!P$ violation in charm decays. https://t.co/kI83598tHt
taylor_marika: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
feline_cannon: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
alefisico: RT @HEPExperPapers: Observation of $C\!P$ violation in charm decays. https://t.co/kI83598tHt
Niels_Tuning: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
q_the_ordinary: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
Person__X: RT @arxiv_org: Observation of $C\!P$ violation in charm decays. https://t.co/mSDN3sXNEL https://t.co/yESZYc0ISe
eva_gersabeck: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
salwamsKSU: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
MarcoGersabeck: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
Hadron_Sophia: RT @HEPExperPapers: Observation of $C\!P$ violation in charm decays. https://t.co/kI83598tHt
peteronyisi1: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
john_garg: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
innesbigaran: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
tamasgal: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
damilanesc: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
boxschrodinger: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
FBezrukov: RT @LHCbPhysics: Observation of CP violation in charm decays https://t.co/70xdTDVy0j - The difference of CP asymmetries in the channels D⁰→…
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Sample Sizes : None.
Authors: 853
Total Words: 9336
Unqiue Words: 3141

##### #5. Implicit Generation and Generalization in Energy-Based Models
###### Yilun Du, Igor Mordatch
Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training, on continuous neural networks, and show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving significantly better samples than other likelihood models and on par with contemporary GAN approaches, while covering all modes of the data. We highlight unique capabilities of implicit generation, such as energy compositionality and corrupt image reconstruction and completion. Finally, we show that EBMs generalize well and are able to achieve state-of-the-art out-of-distribution classification, exhibit adversarially robust classification, coherent long term predicted trajectory roll-outs, and generate zero-shot compositions of models.
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###### Tweets
arxiv_org: Implicit Generation and Generalization in Energy-Based Models. https://t.co/F7tAI9giIe https://t.co/NP0hy5u1Ak
BrundageBot: Implicit Generation and Generalization in Energy-Based Models. Yilun Du and Igor Mordatch https://t.co/7dXfxPuzU9
arxivml: "Implicit Generation and Generalization in Energy-Based Models", Yilun Du, Igor Mordatch https://t.co/56Rm3Xfxgl
arxiv_cs_LG: Implicit Generation and Generalization in Energy-Based Models. Yilun Du and Igor Mordatch https://t.co/3Oqn0LkoEJ
Memoirs: Implicit Generation and Generalization in Energy-Based Models. https://t.co/wLkDHYetLq
arxiv_cscv: Implicit Generation and Generalization in Energy-Based Models https://t.co/00l3rjnCyD
arxiv_cscv: Implicit Generation and Generalization in Energy-Based Models https://t.co/00l3rj61a3
arxiv_cscv: Implicit Generation and Generalization in Energy-Based Models https://t.co/00l3rjnCyD
arxiv_cscv: Implicit Generation and Generalization in Energy-Based Models https://t.co/00l3rj61a3
arxiv_cscv: Implicit Generation and Generalization in Energy-Based Models https://t.co/00l3rjnCyD
Miles_Brundage: RT @BrundageBot: Implicit Generation and Generalization in Energy-Based Models. Yilun Du and Igor Mordatch https://t.co/7dXfxPuzU9
shubh_300595: RT @arxiv_org: Implicit Generation and Generalization in Energy-Based Models. https://t.co/F7tAI9giIe https://t.co/NP0hy5u1Ak
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Sample Sizes : None.
Authors: 2
Total Words: 8907
Unqiue Words: 2653

##### #6. Probing the Need for Visual Context in Multimodal Machine Translation
###### Ozan Caglayan, Pranava Madhyastha, Lucia Specia, Loïc Barrault
Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence of the very simple, short and repetitive sentences used in the only available dataset for the task (Multi30K), rendering the source text sufficient as context. In the general case, however, we believe that it is possible to combine visual and textual information in order to ground translations. In this paper we probe the contribution of the visual modality to state-of-the-art MMT models by conducting a systematic analysis where we partially deprive the models from source-side textual context. Our results show that under limited textual context, models are capable of leveraging the visual input to generate better translations. This contradicts the current belief that MMT models disregard the visual modality because of either the quality of the image features or the way they are integrated into the model.
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###### Tweets
arxiv_org: Probing the Need for Visual Context in Multimodal Machine Translation. https://t.co/NR8OHv5KmG https://t.co/d7n9NftOez
BrundageBot: Probing the Need for Visual Context in Multimodal Machine Translation. Ozan Caglayan, Pranava Madhyastha, Lucia Specia, and Loïc Barrault https://t.co/wcAvJO76Gc
arxivml: "Probing the Need for Visual Context in Multimodal Machine Translation", Ozan Caglayan, Pranava Madhyastha, Lucia S… https://t.co/nNJ6scc4Kf
Ozan__Caglayan: The preprint version of our upcoming NAACL paper "Probing the Need for Visual Context in Multimodal Machine Translation" is now online! https://t.co/dKztB7MSFU
arxiv_cscl: Probing the Need for Visual Context in Multimodal Machine Translation https://t.co/j4ILDTNOVC
arxiv_cscl: Probing the Need for Visual Context in Multimodal Machine Translation https://t.co/j4ILDTNOVC
arxiv_cscl: Probing the Need for Visual Context in Multimodal Machine Translation https://t.co/j4ILDTNOVC
arxiv_cscl: Probing the Need for Visual Context in Multimodal Machine Translation https://t.co/j4ILDTwdx2
arxiv_cscl: Probing the Need for Visual Context in Multimodal Machine Translation https://t.co/j4ILDTNOVC
ComputerPapers: Probing the Need for Visual Context in Multimodal Machine Translation. https://t.co/BSvGP41MIP
shubh_300595: RT @arxiv_org: Probing the Need for Visual Context in Multimodal Machine Translation. https://t.co/NR8OHv5KmG https://t.co/d7n9NftOez
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Sample Sizes : None.
Authors: 4
Total Words: 5465
Unqiue Words: 1934

##### #7. Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
###### Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen
Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing...
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###### Tweets
BrundageBot: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning. Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, and Huajun Chen https://t.co/6wGViWDDBr
arxivml: "Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning", Wen Zhang, Bibek Paudel, Liang Wang, Jia… https://t.co/0U7sToBj5p
SciFi: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning. https://t.co/x90McAq94N
arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Y1CyB
arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Yjdq9
arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Y1CyB
arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Y1CyB
arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Yjdq9
arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Y1CyB
RexDouglass: RT @arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Yjdq9
puneethmishra: RT @arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Y1CyB
puneethmishra: RT @arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Yjdq9
chengjie: RT @arxiv_cscl: Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning https://t.co/IFfx9Y1CyB
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Sample Sizes : None.
Authors: 8
Total Words: 11210
Unqiue Words: 2839

##### #8. Evolution of Dark Energy Perturbations for Slotheon Field and Power Spectrum
Within the framework of modified gravity model namely Slotheon model, inspired by the theory of extra dimensions, we explore the behaviour of Dark Energy and the perturbations thereof. The Dark Energy and matter perturbations equations are then derived and solved numerically by defining certain dimensionless variables and properly chosen initial conditions. The results are compared with those for standard quintessence model and $\Lambda$CDM model. The matter power spectrum is obtained and also compared with that for $\Lambda$CDM model. It appears that Dark Energy in Slotheon model is more akin to that for $\Lambda$CDM model than the standard quintessence model.
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###### Tweets
arxiv_org: Evolution of Dark Energy Perturbations for Slotheon Field and Power Spectrum. https://t.co/RMwfLG2WBT https://t.co/LVD0r8YY65
dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA https://t.co/PnqxV7Nlsr
Mike_Banks: @dcastelvecchi The origin of the slotheon: https://t.co/4J51T9jAvn
RelativityPaper: Evolution of Dark Energy Perturbations for Slotheon Field and Power Spectrum. https://t.co/9Te32jHxir
Summer_Ash: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
bmaher: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
docfreeride: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
tvjrennie: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
martin_hamilton: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
JDRedding: RT @arxiv_org: Evolution of Dark Energy Perturbations for Slotheon Field and Power Spectrum. https://t.co/RMwfLG2WBT https://t.co/LVD0r8YY65
1cRebeca: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
odreissi: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
BillDortch: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
The_Heyoka: RT @dcastelvecchi: Would you like a new elementary particle called the slotheon? Oh yes you do. Oh yes you do. https://t.co/EuIiFZioDA htt…
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###### Other stats
Sample Sizes : None.
Authors: 3
Total Words: 7326
Unqiue Words: 1885

##### #9. LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
###### Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view involves well known challenges for learning, including occlusion and scale variation, but it also provides contextual information based on how the sensor data was captured. Our approach uses a fully convolutional network to predict a multimodal distribution over 3D boxes for each point and then it efficiently fuses these distributions to generate a prediction for each object. Experiments show that modeling each detection as a distribution rather than a single deterministic box leads to better overall detection performance. Benchmark results show that this approach has significantly lower runtime than other recent detectors and that it achieves state-of-the-art performance when compared on a large dataset that has enough data to overcome the...
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###### Tweets
arxiv_org: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. https://t.co/Z6pPEgeV2x https://t.co/7Ie0Hz55yX
BrundageBot: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, and Carl K. Wellington https://t.co/ZACxxas8Pu
arxivml: "LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving", Gregory P． Meyer, Ankit Laddha, E… https://t.co/7I96N8M5dT
arxiv_cs_LG: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, and Carl K. Wellington https://t.co/J6QOPlh459
Memoirs: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. https://t.co/YuPeYnSsQu
arxiv_cscv: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving https://t.co/ici4Z7nWRH
arxiv_cscv: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving https://t.co/ici4Z7nWRH
arxiv_cscv: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving https://t.co/ici4Z76lt7
arxiv_cscv: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving https://t.co/ici4Z7nWRH
guildai: RT @arxiv_org: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. https://t.co/Z6pPEgeV2x https://t.co/7Ie0Hz5…
Jotarun: RT @arxiv_org: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. https://t.co/Z6pPEgeV2x https://t.co/7Ie0Hz5…
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###### Other stats
Sample Sizes : None.
Authors: 5
Total Words: 0
Unqiue Words: 0

##### #10. Im2Pencil: Controllable Pencil Illustration from Photographs
###### Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style. This is a challenging task due to multiple stroke types (e.g., outline and shading), structural complexity of pencil shading (e.g., hatching), and the lack of aligned training data pairs. To address these challenges, we develop a two-branch model that learns separate filters for generating sketchy outlines and tonal shading from a collection of pencil drawings. We create training data pairs by extracting clean outlines and tonal illustrations from original pencil drawings using image filtering techniques, and we manually label the drawing styles. In addition, our model creates different pencil styles (e.g., line sketchiness and shading style) in a user-controllable manner. Experimental results on different types of pencil drawings show that the proposed algorithm performs favorably against existing methods in terms of quality, diversity and user evaluations.
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###### Tweets
arxiv_org: Im2Pencil: Controllable Pencil Illustration from Photographs. https://t.co/BkaKYXb0QL https://t.co/A68VI5bXrP
arxivml: "Im2Pencil: Controllable Pencil Illustration from Photographs", Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman… https://t.co/EiJJFfm5hw
drumato: ○Abstractまとめ 写真から線画を生成する上で 陰影法やタッチの複雑性がタスクを難化させていると述べた上で､ 概形の描き方と陰影をそれぞれ学習させるTwo-Branches-Modelを提唱している｡ https://t.co/2UeIy7Gunb
arxiv_cscv: Im2Pencil: Controllable Pencil Illustration from Photographs https://t.co/jokWMq2azY
arxiv_cscv: Im2Pencil: Controllable Pencil Illustration from Photographs https://t.co/jokWMq2azY
arxiv_cscv: Im2Pencil: Controllable Pencil Illustration from Photographs https://t.co/jokWMpKzbo
arxiv_cscv: Im2Pencil: Controllable Pencil Illustration from Photographs https://t.co/jokWMq2azY
ComputerPapers: Im2Pencil: Controllable Pencil Illustration from Photographs. https://t.co/yEabZlbAof
syoyo: RT @arxiv_org: Im2Pencil: Controllable Pencil Illustration from Photographs. https://t.co/BkaKYXb0QL https://t.co/A68VI5bXrP
xuetal: RT @arxiv_org: Im2Pencil: Controllable Pencil Illustration from Photographs. https://t.co/BkaKYXb0QL https://t.co/A68VI5bXrP
roadrunning01: RT @arxiv_org: Im2Pencil: Controllable Pencil Illustration from Photographs. https://t.co/BkaKYXb0QL https://t.co/A68VI5bXrP
kodai_nakashima: RT @arxiv_cscv: Im2Pencil: Controllable Pencil Illustration from Photographs https://t.co/jokWMpKzbo
shubh_300595: RT @arxiv_org: Im2Pencil: Controllable Pencil Illustration from Photographs. https://t.co/BkaKYXb0QL https://t.co/A68VI5bXrP
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###### Other stats
Sample Sizes : None.
Authors: 5
Total Words: 6148
Unqiue Words: 1856

Assert is a website where the best academic papers on arXiv (computer science, math, physics), bioRxiv (biology), BITSS (reproducibility), EarthArXiv (earth science), engrXiv (engineering), LawArXiv (law), PsyArXiv (psychology), SocArXiv (social science), and SportRxiv (sport research) bubble to the top each day.

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