Query: Using machine learning to optimize parallelism in big data applications

Relevant passages:
MapReduce: Simplified Data Processing on Large Clusters <=SEP=> MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing <=SEP=> We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection <=SEP=> We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report on a large scale experiment over half a million runs of C and a Naive Bayes algorithm to estimate the e ects of di erent parameters on these al gorithms on real world datasets For cross validation we vary the number of folds and whether the folds are strati ed or not for boot strap we vary the number of bootstrap sam ples Our results indicate that for real word datasets similar to ours the best method to use for model selection is ten fold strati ed cross validation even if computation power allows using more folds

Paragon: QoS-aware scheduling for heterogeneous datacenters <=SEP=> Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications.
 We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state.
 We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11% and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.

Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations <=SEP=> As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem.
 In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.

Factored recurrent neural network language model in TED lecture transcription <=SEP=> In this study, we extend recurrent neural network-based language models (RNNLMs) by explicitly integrating morphological and syntactic factors (or features). Our proposed RNNLM is called a factored RNNLM that is expected to enhance RNNLMs. A number of experiments are carried out on top of state-of-the-art LVCSR system that show the factored RNNLM improves the performance measured by perplexity and word error rate. In the IWSLT TED test data sets, absolute word error rate reductions over RNNLM and n-gram LM are 0.4∼0.8 points.

Feature selection for high-dimensional data <=SEP=> This paper offers a comprehensive approach to feature selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection in the context of high-dimensional data. First, we focus on the basis of feature selection, providing a review of its history and basic concepts. Then, we address different topics in which feature selection plays a crucial role, such as microarray data, intrusion detection, or medical applications. Finally, we delve into the open challenges that researchers in the field have to deal with if they are interested to confront the advent of “Big Data” and, more specifically, the “Big Dimensionality”.

Discriminative Multi-View Interactive Image Re-Ranking <=SEP=> Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users’ intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud <=SEP=> Biometric identification has played an important role in achieving user authentication. For efficiency and economic savings, biometric data owners are motivated to outsource the biometric data and identification tasks to a third party, which however introduces potential threats to user’s privacy. In this paper, we propose a new privacy-preserving biometric identification scheme which can release the database owner from heavy computation burden. In the proposed scheme, we design concrete biometric data encryption and matching algorithms, and introduce perturb terms in each biometric data. A thorough analysis indicates that our schemes are secure, and the ultimate scheme offers a high level of privacy protection. In addition, the performance evaluations via extensive simulations demonstrate our schemes’ efficiency. © 2017 Elsevier Inc. All rights reserved.

An Energy-Efficient Architecture for the Internet of Things (IoT) <=SEP=> Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.

Tight bounds for rumor spreading in graphs of a given conductance <=SEP=> We study the connection between the rate at which a rumor spreads throughout a graph and the conductance of the graph—a standard measure of a graph’s expansion properties. We show that for any n-node graph with conductance φ, the classical PUSH-PULL algorithm distributes a rumor to all nodes of the graph in O(φ log n) rounds with high probability (w.h.p.). This bound improves a recent result of Chierichetti, Lattanzi, and Panconesi [6], and it is tight in the sense that there exist graphs where Ω(φ log n) rounds of the PUSH-PULL algorithm are required to distribute a rumor w.h.p. We also explore the PUSH and the PULL algorithms, and derive conditions that are both necessary and sufficient for the above upper bound to hold for those algorithms as well. An interesting finding is that every graph contains a node such that the PULL algorithm takes O(φ log n) rounds w.h.p. to distribute a rumor started at that node. In contrast, there are graphs where the PUSH algorithm requires significantly more rounds for any start node. 1998 ACM Subject Classification G.3 [Mathematics of Computing]: Probability and Statistics

Coccydynia: an overview of the anatomy, etiology, and treatment of coccyx pain. <=SEP=> BACKGROUND
Despite its small size, the coccyx has several important functions. Along with being the insertion site for multiple muscles, ligaments, and tendons, it also serves as one leg of the tripod-along with the ischial tuberosities-that provides weight-bearing support to a person in the seated position. The incidence of coccydynia (pain in the region of the coccyx) has not been reported, but factors associated with increased risk of developing coccydynia include obesity and female gender.


METHODS
This article provides an overview of the anatomy, physiology, and treatment of coccydynia.


RESULTS
Conservative treatment is successful in 90% of cases, and many cases resolve without medical treatment. Treatments for refractory cases include pelvic floor rehabilitation, manual manipulation and massage, transcutaneous electrical nerve stimulation, psychotherapy, steroid injections, nerve block, spinal cord stimulation, and surgical procedures.


CONCLUSION
A multidisciplinary approach employing physical therapy, ergonomic adaptations, medications, injections, and, possibly, psychotherapy leads to the greatest chance of success in patients with refractory coccyx pain. Although new surgical techniques are emerging, more research is needed before their efficacy can be established.

Passive ultrasonic irrigation of the root canal: a review of the literature. <=SEP=> Ultrasonic irrigation of the root canal can be performed with or without simultaneous ultrasonic instrumentation. When canal shaping is not undertaken the term passive ultrasonic irrigation (PUI) can be used to describe the technique. In this paper the relevant literature on PUI is reviewed from a MEDLINE database search. Passive ultrasonic irrigation can be performed with a small file or smooth wire (size 10-20) oscillating freely in the root canal to induce powerful acoustic microstreaming. PUI can be an important supplement for cleaning the root canal system and, compared with traditional syringe irrigation, it removes more organic tissue, planktonic bacteria and dentine debris from the root canal. PUI is more efficient in cleaning canals than ultrasonic irrigation with simultaneous ultrasonic instrumentation. PUI can be effective in curved canals and a smooth wire can be as effective as a cutting K-file. The taper and the diameter of the root canal were found to be important parameters in determining the efficacies of dentine debris removal. Irrigation with sodium hypochlorite is more effective than with water and ultrasonic irrigation is more effective than sonic irrigation in the removal of dentine debris from the root canal. The role of cavitation during PUI remains inconclusive. No detailed information is available on the influence of the irrigation time, the volume of the irrigant, the penetration depth of the instrument and the shape and material properties of the instrument. The influence of irrigation frequency and intensity on the streaming pattern as well as the complicated interaction of acoustic streaming with the adherent biofilm needs to be clarified to reveal the underlying physical mechanisms of PUI.

Web-based expert systems: benefits and challenges <=SEP=> Convergence of technologies in the Internet and the field of expert systems has offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-Advisor – for e-business strategy development, Fish-Expert – For fish disease diagnosis, and IMIS – to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.

Identification, characterization, and grounding of gradable terms in clinical text <=SEP=> Gradable adjectives are inherently vague and are used by clinicians to document medical interpretations (e.g., severe reaction, mild symptoms). We present a comprehensive study of gradable adjectives used in the clinical domain. We automatically identify gradable adjectives and demonstrate that they have a substantial presence in clinical text. Further, we show that there is a specific pattern associated with their usage, where certain medical concepts are more likely to be described using these adjectives than others. Interpretation of statements using such adjectives is a barrier in medical decision making. Therefore, we use a simple probabilistic model to ground their meaning based on their usage in context.

A compact printed wide-slot UWB antenna with band-notched characteristics <=SEP=> In this paper, we present an offset microstrip-fed ultrawideband antenna with band notched characteristics. The antenna structure consists of rectangular radiating patch and ground plane with rectangular shaped slot, which increases impedance bandwidth upto 123.52%(2.6–11GHz). A new modified U slot is etched in the radiating patch to create band-notched properties in the WiMAX (3.3–3.7GHz) and C-band satellite communication (3.7–4.15GHz). Furthermore, parametric studies have been conducted using EM simulation software CADFEKO suite(7.0). A prototype of antenna is fabricated on 1.6mm thick FR-4 substrate with dielectric constant of 4.4 and loss tangent of 0.02. The proposed antenna exhibits directional and omnidirectional radiation patterns along E and H-plane with stable efficiency over the frequency band from 2.6GHz to 11GHz with VSWR less than 2, except 3.3–4.15GHz notched frequency band. The proposed antenna shows good time domain analysis.

Causes and consequences of microRNA dysregulation in cancer <=SEP=> Over the past several years it has become clear that alterations in the expression of microRNA (miRNA) genes contribute to the pathogenesis of most — if not all — human malignancies. These alterations can be caused by various mechanisms, including deletions, amplifications or mutations involving miRNA loci, epigenetic silencing or the dysregulation of transcription factors that target specific miRNAs. Because malignant cells show dependence on the dysregulated expression of miRNA genes, which in turn control or are controlled by the dysregulation of multiple protein-coding oncogenes or tumour suppressor genes, these small RNAs provide important opportunities for the development of future miRNA-based therapies.

A review on memristor applications <=SEP=> This article presents a review on the main applications of the fourth fundamental circuit element, named "memristor", which had been proposed for the first time by Leon Chua and has recently been developed by a team at HP Laboratories led by Stanley Williams. In particular, after a brief analysis of memristor theory with a description of the first memristor, manufactured at HP Laboratories, we present its main applications in the circuit design and computer technology, together with future developments.

A Customer Loyalty Model for E-Service Context <=SEP=> While the importance of customer loyalty has been recognized in the marketing literature for at least three decades, the conceptualization and empirical validation of a customer loyalty model for e-service context has not been addressed. This paper describes a theoretical model for investigating the three main antecedent influences on loyalty (attitudinal commitment and behavioral loyalty) for e-service context: trust, customer satisfaction, and perceived value. Based on the theoretical model, a comprehensive set of hypotheses were formulated and a methodology for testing them was outlined. These hypotheses were tested empirically to demonstrate the applicability of the theoretical model. The results indicate that trust, customer satisfaction, perceived value, and commitment are separate constructs that combine to determine the loyalty, with commitment exerting a stronger influence than trust, customer satisfaction, and perceived value. Customer satisfaction and perceived value were also indirectly related to loyalty through commitment. Finally, the authors discuss the managerial and theoretical implications of these results.

A multi-scale target detection method for optical remote sensing images <=SEP=> Faster RCNN is a region proposal based object detection approach. It integrates the region proposal stage and classification stage into a single pipeline, which has both rapid speed and high detection accuracy. However, when the model is applied to the target detection of remote sensing imagery, faced with multi-scale targets, its performance is degraded. We analyze the influences of pooling operation and target size on region proposal, then a modified solution for region proposal is introduced to improve recall rate of multi-scale targets. To speed up the convergence of the region proposal networks, an improved generation strategy of foreground samples is proposed, which could suppresses the generation of non-effective foreground samples. Extensive evaluations on the remote sensing image dataset show that the proposed model can obviously improve detection accuracy for multi-scale targets, moreover the training of the model is rapid and high-efficient.

Semi-Supervised Neural Machine Translation with Language Models <=SEP=> Training neural machine translation models is notoriously slow and requires abundant parallel corpora and computational power. In this work we propose an approach of transferring knowledge from separately trained language models to translation systems, also we investigate several techniques to improve translation quality when there is a lack of parallel data and computational resources. Our method is based on fusion between translation system and language model, and initialization of translation system with weights from pretrained language models. We show that this technique gives +2.2 BLEU score on En–Fr pair of WMT europarl-7 dataset and allows us to reach 20.7 BLEU on 20k parallel sentences in less than 8 hours of training on a single NVIDIA GeForce GTX 1080 Ti. We specifically note, that for this advance we use nothing but monolingual corpora for source and target languages. Significant part of presented results was obtained during DeepHack.Babel hackathon on low-resource machine translation organized by iPavlov Lab.

A Linearly Relaxed Approximate Linear Program for Markov Decision Processes <=SEP=> Approximate linear programming (ALP) and its variants have been widely applied to Markov decision processes (MDPs) with a large number of states. A serious limitation of ALP is that it has an intractable number of constraints, as a result of which constraint approximations are of interest. In this paper, we define a linearly relaxed approximation linear program (LRALP) that has a tractable number of constraints, obtained as positive linear combinations of the original constraints of the ALP. The main contribution is a novel performance bound for LRALP.

Memristor Model Comparison <=SEP=> Since the 2008-dated discovery of memristor behavior at the nano-scale, Hewlett Packard is credited for, a large deal of efforts have been spent in the research community to derive a suitable model able to capture the nonlinear dynamics of the nano-scale structures. Despite a considerable number of models of different complexity have been proposed in the literature, there is an ongoing debate over which model should be universally adopted for the investigation of the unique opportunities memristors may offer in integrated circuit design. In order to shed some light into this passionate discussion, this paper compares some of the most noteworthy memristor models present in the literature. The strength of the Pickett?s model stands in its experiment-based development and in its ability to describe some physical mechanism at the origin of memristor dynamics. Since its parameter values depend on the excitation of the memristor and/or on the circuit employing the memristor, it may be assumed as a reference for comparison only in those scenarios for which its parameters were reported in the literature. In this work various noteworthy memristor models are fitted to the Pickett's model under one of such scenarios. This study shows how three models, Biolek's model, the Boundary Condition Memristor model and the Threshold Adaptive Memristor model, outperform the others in the replica of the dynamics observed in the Pickett's model. In the second part of this work the models are used in a couple of basic circuits to study the variance between the dynamical behaviors they give rise to. This analysis intends to make the circuit designers aware of the different behaviors which may occur in memristor-based circuits according to the memristor model under use.

Data science is science's second chance to get causal inference right: A classification of data science tasks <=SEP=> 1. Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 2. Harvard-MIT Division of Health Sciences and Technology, Boston, MA 3. Mongan Institute, Massachusetts General Hospital, Boston, MA 4. Department of Health Care Policy, Harvard Medical School, Boston, MA 5. Department of Neurology, Harvard Medical School, Partners MS Center, Brigham and Women’s Hospital, Boston, MA 6. Biostatistics Center, Massachusetts General Hospital, Boston, MA

Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls <=SEP=> Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%.

Attention Alignment Multimodal LSTM for Fine-Gained Common Space Learning <=SEP=> We address the problem common space learning approach that maps all related multimodal information into a common space for multimodal data. To establish a fine-grained common space, the aligned relevant local information of different modalities is used to learn a common subspace where the projected fragmented information is further integrated according to intra-modal semantic relationships. Specifically, we propose a novel multimodal LSTM with an attention alignment mechanism, namely attention alignment multimodal LSTM (AAM-LSTM), which mainly includes attentional alignment recurrent network (AA-R) and hierarchical multimodal LSTM (HM-LSTM). Different from the traditional methods which operate on the full modal data directly, the proposed model exploits the inter-modal and intra-modal semantic relationships of local information, to jointly establish a uniform representation of multi-modal data. Specifically, AA-R automatically captures semantic-aligned local information to learn common subspace without the need of supervised labels, then HM-LSTM leverages the potential relationships of these local information to learn a fine-grained common space. The experimental results on Filker30K, Filker8K, and Filker30K entities verify the performance and effectiveness of our model, which compares favorably with the state-of-the-art methods. In particular, the experiment of phrase localization on AA-R with Filker30K entities shows the expected accurate attention alignment. Moreover, from the experiment results of image-sentence retrieval tasks, it can be concluded that the proposed AAM-LSTM outperforms benchmark algorithms by a large margin.

Chip-level and board-level CDM ESD tests on IC products <=SEP=> The electrostatic discharge (ESD) transient currents and failure analysis (FA) between chip-level and board-level charged-device-model (CDM) ESD tests are investigated in this work. The discharging current waveforms of three different printed circuit boards (PCBs) are characterized first. Then, the chip-level and board-level CDM ESD tests are performed to an ESD-protected dummy NMOS and a high-speed receiver front-end circuit, respectively. Scanning electron microscope (SEM) failure pictures show that the board-level CDM ESD test causes much severer failure than that caused by the chip-level CDM ESD test.

Classifying Objectionable Websites Based on Image Content <=SEP=> This paper describes IBCOW (Image-based Classi cation of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPETM (Wavelet Image Pornography Elimination) and statistics to provide robust classi cation of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as "objectionable" and "benign" in a training set can be performed efciently and e ectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% speci city in classifying a Web site based solely on images. Both the sensitivity and the speci city in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classi cation process.

Knowledge-Based Distant Regularization in Learning Probabilistic Models <=SEP=> Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization of such domain knowledge in popular regularization methods. In this paper, we propose the knowledge-based distant regularization framework, in which we utilize the distant information encoded in a knowledge graph for regularization of probabilistic model estimation. In particular, we propose to impose prior distributions on model parameters specified by knowledge graph embeddings. As an instance of the proposed framework, we present the factor analysis model with the knowledge-based distant regularization. We show the results of preliminary experiments on the improvement of the generalization capability of such model.

Representation Learning for Grounded Spatial Reasoning <=SEP=> The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.