Scene Graph Generation via Conditional Random Fields

Despite the great success object detection and segmentation models have achieved in recognizing individual objects in images, performance on cognitive tasks such as image caption, semantic image retrieval, and visual QA is far from satisfactory. To achieve better performance on these cognitive tasks, merely recognizing individual object instances is insufficient. Instead, the interactions between object instances need to be captured in order to facilitate reasoning and understanding of the visual scenes in an image. Scene graph, a graph representation of images that captures object instances and their relationships, offers a comprehensive understanding of an image. However, existing techniques on scene graph generation fail to distinguish subjects and objects in the visual scenes of images and thus do not perform well with real-world datasets where exist ambiguous object instances. In this work, we propose a novel scene graph generation model for predicting object instances and its corresponding relationships in an image. Our model, SG-CRF, learns the sequential order of subject and object in a relationship triplet, and the semantic compatibility of object instance nodes and relationship nodes in a scene graph efficiently. Experiments empirically show that SG-CRF outperforms the state-of-the-art methods, on three different datasets, i.e., CLEVR, VRD, and Visual Genome, raising the Recall@100 from 24.99% to 49.95%, from 41.92% to 50.47%, and from 54.69% to 54.77%, respectively

Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+

We extend probabilistic action language pBC+ with the notion of utility as in decision theory. The semantics of the extended pBC+ can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language LPMLN. Alternatively, the semantics of pBC+ can also be defined in terms of Markov Decision Process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as to leverage an MDP solver to compute pBC+. The idea led to the design of the system pbcplus2mdp, which can find an optimal policy of a pBC+ action description using an MDP solver. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 3 figures; Under consideration in Theory and Practice of Logic Programming (TPLP). arXiv admin note: text overlap with arXiv:1805.0063

Distributions of a particle's position and their asymptotics in the qq-deformed totally asymmetric zero range process with site dependent jumping rates

In this paper we study the probability distribution of the position of a tagged particle in the $q$-deformed Totally Asymmetric Zero Range Process ($q$-TAZRP) with site dependent jumping rates. For a finite particle system, it is derived from the transition probability previously obtained by Wang and Waugh. We also provide the probability distribution formula for a tagged particle in the $q$-TAZRP with the so-called step initial condition in which infinitely many particles occupy one single site and all other sites are unoccupied. For the $q$-TAZRP with step initial condition, we provide a Fredholm determinant representation for the probability distribution function of the position of a tagged particle, and moreover we obtain the limiting distribution function as the time goes to infinity. Our asymptotic result for $q$-TAZRP with step initial condition is comparable to the limiting distribution function obtained by Tracy and Widom for the $k$-th leftmost particle in the asymmetric simple exclusion process with step initial condition (Theorem 2 in Commun. Math. Phys. 290, 129--154 (2009)).Comment: 34 pages, 2 figure

On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic

Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widely applied formalisms in Statistical Relational Learning, an emerging area in Artificial Intelligence that is concerned with combining logical and statistical AI. Despite their resemblance, the relationship has not been formally stated. In this paper, we describe the precise semantic relationship between them from a logical perspective. This is facilitated by first extending fuzzy logic to allow weights, which can be also viewed as a generalization of PSL, and then relate that generalization to MLN. We observe that the relationship between PSL and MLN is analogous to the known relationship between fuzzy logic and Boolean logic, and furthermore the weight scheme of PSL is essentially a generalization of the weight scheme of MLN for the many-valued setting.Comment: In Working Notes of the 6th International Workshop on Statistical Relational A

A quantized physical framework for understanding the working mechanism of ion channels

A quantized physical framework, called the five-anchor model, is developed for a general understanding of the working mechanism of ion channels. According to the hypotheses of this model, the following two basic physical principles are assigned to each anchor: the polarity change induced by an electron transition and the mutual repulsion and attraction induced by an electrostatic force. Consequently, many unique phenomena, such as fast and slow inactivation, the stochastic gating pattern and constant conductance of a single ion channel, the difference between electrical and optical stimulation (optogenetics), nerve conduction block and the generation of an action potential, become intrinsic features of this physical model. Moreover, this model also provides a foundation for the probability equation used to calculate the results of electrical stimulation in our previous C-P theory

A Probabilistic Extension of Action Language BC+

We present a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LPMLN programs---a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LPMLN.Comment: Paper presented at the 34nd International Conference on Logic Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 18 pages, LaTeX, 1 PDF figures (arXiv:YYMM.NNNNN

The first principle of neural circuit and the general Circuit-Probability theory

A new neural circuit is proposed by considering the myelin as an inductor. This new neural circuit can explain why the lump-parameter circuit used in previous C-P theory is valid. Meanwhile, it provides a new explanation of the biological function of myelin for neural signal propagation. Furthermore, a new model for magnetic nerve stimulation is built and all phenomena in magnetic nerve stimulation can be well explained. Based on this model, the coil structure can be optimized

High TcT_c superconductivity at the FeSe/SrTiO3_3 Interface

In a recent experiment the superconducting gap of a single unit cell thick FeSe film on SrTiO$_3$ substrate is observed by scanning tunneling spectroscopy and angle-resolved photoemission spectroscopy. The value of the superconducting gap is much larger than that of the bulk FeSe under ambient pressure. In this paper we study the effects of screening due to the ferroelectric phonons on Cooper pairing. We conclude it can significantly enhance the energy scale of Cooper pairing and even change the pairing symmetry. Our results also raise some concerns on whether phonons can be completely ignored for bulk iron-based superconductors.Comment: 20 one-column pages with appendix, 9 figure

Growth index with the cosmological constant

We obtain the exact analytic form of the growth index at present epoch ($a=1$) in a flat universe with the cosmological constant ({\it i.e.} the dark energy with its equation of state $\omega_{de} = -1$). For the cosmological constant, we obtain the exact value of the current growth index parameter $\gamma = 0.5547$, which is very close to the well known value 6/11. We also obtain the exact analytic solution of the growth factor for $\omega_{de}$ = -1/3 or -1. We investigate the growth index and its parameter at any epoch with this exact solution. In addition to this, we are able to find the exact $\Omega_{m}^{0}$ dependence of those observable quantities. The growth index is quite sensitive to $\Omega_{m}^{0}$ at $z = 0.15$, where we are able to use 2dF observation. If we adopt 2dF value of growth index, then we obtain the constrain $0.11 \leq \Omega_{m}^{0} \leq 0.37$ for the cosmological constant model. Especially, the growth index is quite sensitive to $\Omega_{m}^{0}$ around $z \leq 1$. We might be able to obtain interesting observations around this epoch. Thus, the analytic solution for this growth factor provides the very useful tools for future observations to constrain the exact values of observational quantities at any epoch related to growth factor for $\omega_{de} = -1$ or -1/3.Comment: 10pages, 6 figures, Slightly changing in title. Add one more figure to the previous versio

The Bayesian process control with multiple assignable causes

We study an optimal process control problem with multiple assignable causes. The process is initially in-control but is subject to random transition to one of multiple out-of-control states due to assignable causes. The objective is to find an optimal stopping rule under partial observation that maximizes the total expected reward in infinite horizon. The problem is formulated as a partially observable Markov decision process (POMDP) with the belief space consisting of state probability vectors. New observations are obtained at fixed sampling interval to update the belief vector using Bayes' theorem. Under standard assumptions, we show that a conditional control limit policy is optimal and that there exists a convex, non-increasing control limit that partitions the belief space into two individually connected control regions: a stopping region and a continuation region. We further derive the analytical bounds for the control limit. An algorithm is devised based on structural results, which considerably reduces the computation. We also shed light on the selection of optimal fixed sampling interval.Comment: 23 pages, 4 figures, 4 tables, under review in Operations Researc