Why closure at the end of a relationship is overrated - Secure In Love
The ending of a significant piece of one's life -- a relationship, a job, a stage Finding closure allows you to move into your future, unencumbered and optimistic. with particular emphasis on their structure–activity relationship. .. Gold- Catalyzed Divergent Ring-Closing Modes of Indole-Tethered Amino Allenynes . Jieru Yang, Xiaofan Zhou, Yu Zeng, Chaoqian Huang, Yuanjing. Chao-qian Li's 8 research works with 21 citations and 23 reads, including: [Role of Furthermore, current-voltage relationship(I-V) curve exhibited an upward shift, and The closure of BK(Ca) or Kv increased the contracting tone induced by.
Results show that the use of enhanced minimax for computing node priors results in the strongest MCTS-minimax hybrid in the three test domains of Othello, Breakthrough, and Catch the Lion. This hybrid also outperforms enhanced minimax as a standalone player in Breakthrough, demonstrating that at least in this domain, MCTS and minimax can be combined to an algorithm stronger than its parts.
While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets.
A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks.
Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning OGTLto cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability.
With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.
Generating Sentimental Texts via Mixture Adversarial Networks Ke Wang, Xiaojun Wan Natural Language Generation Generating texts of different sentiment labels is getting more and more attention in the area of natural language generation.
However, the texts generated by GAN usually suffer from the problems of poor quality, lack of diversity and mode collapse. In this paper, we propose a novel framework - SentiGAN, which has multiple generators and one multi-class discriminator, to address the above problems. In our framework, multiple generators are trained simultaneously, aiming at generating texts of different sentiment labels without supervision.
We propose a penalty based objective in the generators to force each of them to generate diversified examples of a specific sentiment label. Moreover, the use of multiple generators and one multi-class discriminator can make each generator focus on generating its own examples of a specific sentiment label accurately. Experimental results on four datasets demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the sentiment accuracy and quality of generated texts.
Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables.
This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry.
Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and Automatic poetry generation is an essential step towards computer creativity.
In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge.
In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line.
After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: Both automatic and human evaluation results show that our model outperforms current state-of-the-art methods.
Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training and evaluation resources. In this model, we maintain a novel multi-topic coverage vector, which learns the weight of each topic and is sequentially updated during the decoding process. Afterwards this vector is fed to an attention model to guide the generator. Moreover, we automatically construct two paragraph-level Chinese essay corpora,essay paragraphs and 55, question-and-answer pairs.
Empirical results show that our approach obtains much better BLEU score compared to various baselines. Furthermore, human judgment shows that MTA-LSTM has the ability to generate essays that are not only coherent but also closely related to the input topics.
Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse.
In order to address these two problems, in this paper, we employ inverse reinforcement learning IRL for text generation.
How to Get Closure from a Relationship: 15 Steps (with Pictures)
Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximum the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately.
Our method has two advantages: Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods. This paper presents a CNN-based method that is unsupervised and end-to-end trainable to better solve this task. Our method is unsupervised in the sense that it does not require any training data in the form of object masks but merely a set of images jointly covering objects of a specific class.
Our method comprises two collaborative CNN modules, a feature extractor and a co-attention map generator. The former module extracts the features of the estimated objects and backgrounds, and is derived based on the proposed co-attention loss which minimizes inter-image object discrepancy while maximizing intra-image figure-ground separation.
The latter module is learned to generated co-attention maps by which the estimated figure-ground segmentation can better fit the former module. Besides, the co-attention loss, the mask loss is developed to retain the whole objects and remove noises. Experiments show that our method achieves superior results, even outperforming the state-of-the-art, supervised methods.
However, existing hash based multi-indexing methods suffer from the heavy redundancy, without strong table complementarity and effective hash code learning. To address the problems, this paper proposes a complementary binary quantization CBQ method to jointly learning multiple hash tables. It exploits the power of incomplete binary coding based on prototypes to align the original space and the Hamming space, and further utilizes the nature of multi-indexing search to jointly reduce the quantization loss based on the prototype based hash function.
Our alternating optimization adaptively discovers the complementary prototype sets and the corresponding code sets of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes. Extensive experiments carried out on two popular large-scale tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed CBQ method enjoys the strong table complementarity and significantly outperforms the state-of-the-art, with up to However, it is just a trade-off between these two constraints.
In this paper, we propose a novel Cascaded Low Rank and Sparse Representation CLRSR method for subspace clustering, which seeks the sparse expression on the former learned low rank latent representation. An effective solution and its convergence analysis are also provided. The excellent experimental results demonstrate the proposed method is more robust than other state-of-the-art clustering methods on imageset data.
With the recent development in generative models, image generation has achieved great progress and has been applied to various computer vision tasks. However, multi-domain image generation may not achieve the desired performance due to the difficulty of learning the correspondence of different domain images, especially when the information of paired samples is not given. RegCGAN is based on the conditional GAN, and we introduce two regularizers to guide the model to learn the corresponding semantics of different domains.
We evaluate the proposed model on several tasks for which paired training data is not given, including the generation of edges and photos, the generation of faces with different attributes, etc. The experimental results show that our model can successfully generate corresponding images for all these tasks, while outperforms the baseline methods. In recent years, various graph extensions of CF and NMF have been proposed to explore intrinsic geometrical structure of data for the purpose of better clustering performance.
However, many methods build the affinity matrix used in the manifold structure directly based on the input data. Therefore, the clustering results are highly sensitive to the input data.
distribution fiber management
To further improve the clustering performance, we propose a novel manifold concept factorization model with adaptive neighbor structure to learn a better affinity matrix and clustering indicator matrix at the same time. Technically, the proposed model constructs the affinity matrix by assigning the adaptive and optimal neighbors to each point based on the local distance of the learned new representation of the original data with itself as a dictionary.
Our experimental results present superior performance over the state-of-the-art alternatives on numerous datasets. Given a query variable and a large graphical model, we define a much smaller model in a local region around the query variable in the target model so that the marginal distribution of the query variable can be accurately approximated. We verify our theoretical bounds on various datasets and demonstrate that our localized inference algorithm can provide fast and accurate approximation for large graphical models.
To handle multiple queries efficiently, the lifted junction tree algorithm LJT employs a first-order cluster representation of a model and LVE as a subroutine.
Both algorithms answer conjunctive queries of propositional random variables, shattering the model on the query, which causes unnecessary groundings for conjunctive queries of interchangeable variables. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Combining multisets with lifting makes it possible to simultaneously exploit multiple strategies for reducing inference complexity when compared to list-based grounded state representations.Attachment in Adulthood 3 Tips for Finding Closure When an Ex Won’t Give it to You
The core idea is to borrow the concept of Maximally Parallel Multiset Rewriting Systems and to enhance it by concepts from Rao-Blackwellization and Lifted Inference, giving a representation of state distributions that enables efficient inference. In worlds where the random variables that define the system state are exchangeable -- where the identity of entities does not matter -- it automatically uses a representation that abstracts from ordering achieving an exponential reduction in complexity -- and it automatically adapts when observations or system dynamics destroy exchangeability by breaking symmetry.
It has shown tremendous promise for solving inference problems in graphical models and probabilistic programs. Yet, state-of-the-art tools for WMI are generally limited either by the range of amenable theories, or in terms of performance.
To overcome the main roadblock of XADDs -- the computational cost of integration -- we formulate a novel and powerful exact symbolic dynamic programming SDP algorithm that seamlessly handles Boolean, integer-valued and real variables, and is able to effectively cache partial computations, unlike its predecessor. Our empirical results demonstrate that these contributions can lead to a significant computational reduction over existing probabilistic inference algorithms.
MF faces major challenges of handling very sparse and large data. Poisson Factorization PF as an MF variant addresses these challenges with high efficiency by only computing on those non-missing elements. However, ignoring the missing elements in computation makes PF weak or incapable for dealing with columns or rows with very few observations corresponding to sparse items or users.
Zhejiang Chaoqian Communication Equipment Co., Ltd. Reviews & Products -- Qing Xu
MPF adds the metadata-based observed entries to the factorized PF matrices. In addition, similar to MF, choosing the suitable number of latent components for PF is very expensive on very large datasets.
MIPF also effectively estimates the number of latent components. Since transitions depend on physical parameters, when the environment changes, a roboticist has to painstakingly readjust the parameters to work in the new environment. An automated analysis of the transition function 1 identifies adjustable parameters, 2 converts the transition function into a system of logical constraints, and 3 formulates the constraints and user-supplied corrections as a MaxSMT problem that yields new parameter values.
We show that SRTR finds new parameters 1 quickly, 2 with few corrections, and 3 that the parameters generalize to new scenarios. We also show that a SRTR-corrected state machine can outperform a more complex, expert-tuned state machine. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints.
In this paper, we consider the challenging problem of learning unmanned aerial vehicle UAV control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative PID controller to enable stable learning without catastrophic failure.
The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user.
In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal.
We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafted domain-dependent heuristics.
Zhejiang Chaoqian Communication Equipment Co., Ltd.
Bowman, Kostas Daniilidis, George J. Pappas Robotics Traditional approaches for simultaneous localization and mapping SLAM rely on geometric features such as points, lines, and planes to infer the environment structure. They make hard decisions about the data association between observed features and mapped landmarks to update the environment model. This paper makes two contributions to the state of the art in SLAM. First, it generalizes the purely geometric model by introducing semantically meaningful objects, represented as structured models of mid-level part features.
Second, instead of making hard, potentially wrong associations between semantic features and objects, it shows that SLAM inference can be performed efficiently with probabilistic data association. Most of us hate ambiguity. You believe getting closure will be the answer to moving on, and it will help you learn from past mistakes. You comb over every email, text, and message searching for clues to the mystery of what went wrong.
However, despite your best sleuthing, often the evidence you do find may not warrant the ending. You begin to question your own sanity —Did my relationship actually happen, did I make it all up? Why did it have to end? An ending that makes sense. Validation that the relationship was significant and, Information about what really happened.
That said, the type of closure you seek will actually depend more on your love style than any other personal trait. Ideally, closure for emotional types is sharing feelings of sadness with your partner. You want to make sense of the ending; was I deceived? Did I misread the situation? You want to know what changed and when? Do you imagine your ex will be able to give you anything that is helpful? Will you be less wonderful the next time? Are you picking emotionally unavailable partners?
Are you projecting your needs and fears onto others? Are you afraid of being alone?