Query: DeepLogic: End-to-End Logical Reasoning

Relevant passages:
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing <=SEP=> Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a unified neural network framework which processes input sequences and questions, forms semantic and episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN can be trained end-to-end and obtains state of the art results on several types of tasks and datasets: question answering (Facebook’s bAbI dataset), sequence modeling for part of speech tagging (WSJ-PTB), coreference resolution (Quizbowl dataset) and text classification for sentiment analysis (Stanford Sentiment Treebank). The model relies exclusively on trained word vector representations and requires no string matching or manually engineered features.

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks <=SEP=> One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.

Lifted Relational Neural Networks <=SEP=> We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method.

Adam: A Method for Stochastic Optimization <=SEP=> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

TensorLog: A Differentiable Deductive Database <=SEP=> Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this sort of knowledge into “deep” gradient-based learning systems. To address this problem, we describe a probabilistic deductive database, called TensorLog, in which reasoning uses a differentiable process. In TensorLog, each clause in a logical theory is first converted into certain type of factor graph. Then, for each type of query to the factor graph, the message-passing steps required to perform belief propagation (BP) are “unrolled” into a function, which is differentiable. We show that these functions can be composed recursively to perform inference in non-trivial logical theories containing multiple interrelated clauses and predicates. Both compilation and inference in TensorLog are efficient: compilation is linear in theory size and proof depth, and inference is linear in database size and the number of message-passing steps used in BP. We also present experimental results with TensorLog and discuss its relationship to other first-order probabilistic logics.

Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition <=SEP=> This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

Full virtualization based ARINC 653 partitioning <=SEP=> As the number of electronic components of avionics systems is significantly increasing, it is desirable to run several avionics software on a single computing device. In such system, providing a seamless way to integrate separate applications on a computing device is a very critical issue as the Integrated Modular Avionics (IMA) concept addresses. In this context, the ARINC 653 standard defines resource partitioning of avionics application software. The virtualization technology has very high potential of providing an optimal implementation of the partition concept. In this paper, we study supports for full virtualization based ARINC 653 partitioning. The supports include extension of XML-based configuration file format and hierarchical scheduler for temporal partitioning. We show that our implementation can support well-known VMMs, such as VirtualBox and VMware and present basic performance numbers.

Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks <=SEP=> Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.

Predictors of loneliness among older women and men in Sweden: A national longitudinal study. <=SEP=> OBJECTIVES
Longitudinal research on loneliness in old age has rarely considered loneliness separately for men and women, despite gender differences in life experiences. The objective of this study was to examine the extent to which older women and men (70+) report feelings of loneliness with a focus on: (a) changes in reported loneliness as people age, and (b) which factors predict loneliness.


METHOD
Data from the 2004 and 2011 waves of SWEOLD, a longitudinal national survey, was used (n = 587). The prediction of loneliness in 2011 by variables measured in 2004 and 2004-2011 variable change scores was examined in three logistic regression models: total sample, women and men. Variables in the models included: gender, age, education, mobility problems, depression, widowhood and social contacts.


RESULTS
Older people moved into and out of frequent loneliness over time, although there was a general increase in loneliness with age. Loneliness at baseline, depression increment and recent widowhood were significant predictors of loneliness in all three multivariable models. Widowhood, depression, mobility problems and mobility reduction predicted loneliness uniquely in the model for women; while low level of social contacts and social contact reduction predicted loneliness uniquely in the model for men.


CONCLUSION
This study challenges the notion that feelings of loneliness in old age are stable. It also identifies important gender differences in prevalence and predictors of loneliness. Knowledge about such differences is crucial for the development of effective policy and interventions to combat loneliness in later life.

Research on high-frequency induction heating load-matched based on LLC resonant circuit <=SEP=> Modern high-frequency induction heating technology puts forward higher requirements for load matching of the induction heating power supply. LLC resonant load circuit can replace the traditional induction heating load matching transformer, and increase power density. This paper analyzes the load matching characteristics of LLC load resonant circuit in high frequency induction heating power supply, and gives the parameter selection method of matching components. At last, combining with phase-shifting pulse-width modulation (PWM) control method, the phase-shifting induction heating experiment platform based on LLC load resonant circuit is established. Experimental results show that the performance of LLC circuit in high frequency induction heating power supply load matching is excellent.

An 800-MHz–6-GHz Software-Defined Wireless Receiver in 90-nm CMOS <=SEP=> A software-defined radio receiver is designed from a low-power ADC perspective, exploiting programmability of windowed integration sampler and clock-programmable discrete-time analog filters. To cover the major frequency bands in use today, a wideband RF front-end, including the low-noise amplifier (LNA) and a wide tuning-range synthesizer, spanning over 800 MHz to 6 GHz is designed. The wideband LNA provides 18-20 dB of maximum gain and 3-3.5 dB of noise figure over 800 MHz to 6 GHz. A low 1/f noise and high-linearity mixer is designed which utilizes the passive mixer core properties and provides around +70 dBm IIP2 over the bandwidth of operation. The entire receiver circuits are implemented in 90-nm CMOS technology. Programmability of the receiver is tested for GSM and 802.11g standards

Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey <=SEP=> Swarm systems consist of large numbers of robots that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from search-and-rescue situations to Cyber defence. The two decision making cycles of swarms and humans operate on two different time-scales, where the former is normally orders of magnitude faster than the latter. Closing the loop at the intersection of these two cycles will create fast and adaptive humanswarm teaming networks. This paper brings desperate pieces of the ground work in this research area together to review this multidisciplinary literature. We conclude with a framework to synthesize the findings and summarize the multi-modal indicators needed for closed-loop human-swarm adaptive systems.

Opportunities and advances in ultra-wideband electronically scanned arrays <=SEP=> This paper discusses bandwidth and polarization challenges in modern dual-polarized ultra-wideband (UWB) electronically scanned array (ESA) aperture design and two recent solution advances aiming to remedy such issues: a 6:1 Planar Ultrawideband Modular Antenna (PUMA) array and a 10:1 Sliced Notch Antenna (SNA) array. The proposed arrays enable wide bandwidth, low cross-polarization, and high efficiency from both low-profile and high-profile array research directions to appeal to a wider variety of applications and design embodiments. The proposed UWB-ESAs are analyzed alongside a conventional 10:1 all-metal Vivaldi array to shed light upon modern design trends and implications.

Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information <=SEP=> Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it’s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. * Corresponding author

Incremental dense multi-modal 3D scene reconstruction <=SEP=> Aquiring reliable depth maps is an essential prerequisite for accurate and incremental 3D reconstruction used in a variety of robotics applications. Depth maps produced by affordable Kinect-like cameras have become a de-facto standard for indoor reconstruction and the driving force behind the success of many algorithms. However, Kinect-like cameras are less effective outdoors where one should rely on other sensors. Often, we use a combination of a stereo camera and lidar, however, process the acquired data in independent pipelines which generally leads to sub-optimal performance since both sensors suffer from different drawbacks. In this paper, we propose a probabilistic model that efficiently exploits complementarity between different depth-sensing modalities for incremental dense scene reconstruction. Our model uses a piecewise planarity prior assumption which is common in both the indoor and outdoor scenes. We demonstrate the effectiveness of our approach on the KITTI dataset, and provide qualitative and quantitative results showing high-quality dense reconstruction of a number of scenes.

An Algebraic Theory of Graph Reduction <=SEP=> Wc show how membership in classes of graphs definable m monwhc second-order ]oglc and of bounded treewldth can be decided by finite sets of terminating reduction rules. The method is constructive in the sense that wc describe an algorlthm that wdl produce, from J formula in monxhc second-order Ioglc and an mleger k such that the class dcfmed by the formul~ IS of treewidth s k, a set of rewrite rules that rcducxs any member of the elms to one of’ firrltely many graphs, in a number of steps bounded by the size c~f the graph. This rcductmn syjtem ymlds an algorlthm that runs m time linear m the size of the graph. We illustrate our results with rcductlon systems that recognux some families of outerplanar and planar graphs. Categories .ind Subject Descriptors F.2 2 [Analysis of Algorithms and Problem Complexity], Nonnurnerical Algorithms and Pr[>blems—cotyz/] ~trczt~o/zs O;Zdzscrctc strut tztrcs; F.-1 3 [Mathematical Logic and Formal Languages]; Formal Languages-ckmscs defu~ed by ~runzn~ars c~ruutomafa. G 2.2 [Discrete Mathematics]; Graph Thco~–grup/~ alcqortrhms, trees General Terms Algorithm\, Languages, Perform~nce. Theory Additlonai

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models <=SEP=> MOTIVATION
Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively.


RESULTS
We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others.


AVAILABILITY
The specification of SBML Level 1 is freely available from http://www.sbml.org/

Children's engagement with educational iPad apps: Insights from a Spanish classroom <=SEP=> This study investigates the effects of a story-making app called Our Story and a selection of other educational apps on the learning engagement of forty-one Spanish 4–5-year-olds. Children were observed interacting in small groups with the story-making app and this was compared to their engagement with a selection of construction and drawing apps. Children’s engagement was analysed in two ways: it was categorised using Bangert-Drowns and Pyke’s taxonomy for individual hands-on engagement with educational software, and using the concept of exploratory talk as developed by Mercer et al. to analyse peer engagement. For both approaches, quantitative and qualitative indices of children’s engagement were considered. The overall findings suggested that in terms of the BangertDrowns and Pyke taxonomy, the quality of children’s individual engagement was higher with the OS app in contrast to their engagement with other app software. The frequency of children’s use of exploratory talk was similar with the OS and colouring and drawing apps, and a detailed qualitative analysis of the interaction transcripts revealed several instances of the OS and drawing apps supporting joint problem-solving and collaborative engagement. We suggest that critical indices of an app’s educational value are the extent to which the app supports opportunities for open-ended content and children’s independent use of increasingly difficult features. 2013 Elsevier Ltd. All rights reserved.

A lightweight symbolic virtual machine for solver-aided host languages <=SEP=> Solver-aided domain-specific languages (SDSLs) are an emerging class of computer-aided programming systems. They ease the construction of programs by using satisfiability solvers to automate tasks such as verification, debugging, synthesis, and non-deterministic execution. But reducing programming tasks to satisfiability problems involves translating programs to logical constraints, which is an engineering challenge even for domain-specific languages.
 We have previously shown that translation to constraints can be avoided if SDSLs are implemented by (traditional) embedding into a host language that is itself solver-aided. This paper describes how to implement a symbolic virtual machine (SVM) for such a host language. Our symbolic virtual machine is lightweight because it compiles to constraints only a small subset of the host's constructs, while allowing SDSL designers to use the entire language, including constructs for DSL embedding. This lightweight compilation employs a novel symbolic execution technique with two key properties: it produces compact encodings, and it enables concrete evaluation to strip away host constructs that are outside the subset compilable to constraints. Our symbolic virtual machine architecture is at the heart of Rosette, a solver-aided language that is host to several new SDSLs.

Learning to Collaborate for Question Answering and Asking <=SEP=> Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In this paper, we give a systematic study that seeks to leverage the connection to improve both QA and QG. We present a training algorithm that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario. The two key ideas are improving the QG model with QA through incorporating additional QA-specific signal as the loss function, and improving the QA model with QG through adding artificially generated training instances. We conduct experiments on both document based and knowledge based question answering tasks. We have two main findings. Firstly, the performance of a QG model (e.g in terms of BLEU score) could be easily improved by a QA model via policy gradient. Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Learning when to regard generated questions as positive instances could bring performance boost.

Developing Citizens ’ Trust towards Successful Adoption of E-Government : an Empirical Study from Saudi Arabia <=SEP=> Despite the significant influence of citizens’ trust towards successful adoption of e-government, the majority of research studies investigating trust in e-government focus only on technology and government agencies. This research provides a deep understanding of the concept of trust in e-government by investigating the influence of technology, government agencies, risk, citizens’ characteristic. Structure Equation Modelling PLS-SEM is utilized to analyse the collected data and to test the proposed hypotheses. The findings of the study reveals the positive and significant impacts of both technical factors and citizens’ characteristic while factors related to government agencies and risk provide negative impacts in trust in egovernment. Also, this paper identifies effective strategies that government need to develop citizens’ trust on their online services. Research contributions and limitations are also discussed in the end of the paper.

A ground level causal learning algorithm <=SEP=> Open domain causal learning involves learning and establishing causal connections between events directly from sensory experiences. It has been established in psychology that this often requires background knowledge. However, background knowledge has to be built from first experiences, which we term ground level causal learning, which basically involves observing temporal correlations. Subsequent knowledge level causal learning can then be based on this ground level causal knowledge. The causal connections between events, such as between lightning and thunder, are often hard to discern based on simple temporal correlations because there might be noise - e.g., wind, headlights, sounds of vehicles, etc. - that intervene between lightning and thunder. In this paper, we adopt the position that causal learning is inductive and pragmatic, and causal connections exist on a scale of graded strength. We describe a method that is able to filter away noise in the environment to obtain likely causal connections between events.

A study on the effects of personalization and task information on implicit feedback performance <=SEP=> While Implicit Relevance Feedback (IRF) algorithms exploit users' interactions with information to customize support offered to users of search systems, it is unclear how individual and task differences impact the effectiveness of such algorithms. In this paper we describe a study on the effect on retrieval performance of using additional information about the user and their search tasks when developing IRF algorithms. We tested four algorithms that use document display time to estimate relevance, and tailored the threshold times (i.e., the time distinguishing relevance from non-relevance) to the task, the user, a combination of both, or neither. Interaction logs gathered during a longitudinal naturalistic study of online information-seeking behavior are used as stimuli for the algorithms. The findings show that tailoring display time thresholds based on task information improves IRF algorithm performance, but doing so based on user information worsens performance. This has implications for the development of effective IRF algorithms.

Neural Program Synthesis from Diverse Demonstration Videos <=SEP=> Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task objective to encourage the model to learn meaningful intermediate representations for end-to-end training. We show that our model is able to reliably synthesize underlying programs as well as capture diverse behaviors exhibited in demonstrations. The code is available at https://shaohua0116.github.io/demo2program.

Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke? <=SEP=> BACKGROUND AND PURPOSE
Motor dysfunction after unilateral deafferentation in primates can be overcome by restraining the unaffected limb. We asked whether a constraint-induced movement (CIM) program could be implemented within 2 weeks after stroke and whether CIM is more effective than traditional upper-extremity (UE) therapies during this period.


METHODS
Twenty-three persons were enrolled in a pilot randomized, controlled trial that compared CIM with traditional therapies. A blinded observer rated the primary end point, the Action Research Arm Test (ARA). Inclusion criteria were the following: ischemic stroke within 14 days, persistent hemiparesis, evidence of preserved cognitive function, and presence of a protective motor response. Differences between the groups were compared by using Student's t tests, ANCOVA, and Mann-Whitney U: tests.


RESULTS
Twenty subjects completed the 14-day treatment. Two adverse outcomes, a recurrent stroke and a death, occurred in the traditional group; 1 CIM subject met rehabilitation goals and was discharged before completing 14 inpatient days. The CIM treatment group had significantly higher scores on total ARA and pinch subscale scores (P:<0.05). Differences in the mean ARA grip, grasp, and gross movement subscale scores did not reach statistical significance. UE activities of daily living performance was not significantly different between groups, and no subject withdrew because of pain or frustration.


CONCLUSIONS
A clinical trial of CIM therapy during acute rehabilitation is feasible. CIM was associated with less arm impairment at the end of treatment. Long-term studies are needed to determine whether CIM early after stroke is superior to traditional therapies.

Controllability of complex networks <=SEP=> The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.

Evaluation of alternative glyph designs for time series data in a small multiple setting <=SEP=> We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.

Learning to Segment Instances in Videos with Spatial Propagation Network <=SEP=> We propose a deep learning-based framework for instance-level object segmentation. Our method mainly consists of three steps. First, We train a generic model based on ResNet-101 for foreground/background segmentations. Second, based on this generic model, we fine-tune it to learn instance-level models and segment individual objects by using augmented object annotations in first frames of test videos. To distinguish different instances in the same video, we compute a pixel-level score map for each object from these instance-level models. Each score map indicates the objectness likelihood and is only computed within the foreground mask obtained in the first step. To further refine this per frame score map, we learn a spatial propagation network. This network aims to learn how to propagate a coarse segmentation mask spatially based on the pairwise similarities in each frame. In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video. Finally, we decide the instance-level object segmentation in each video by comparing score maps of different instances.

A Longitudinal Assessment of the Persistence of Twitter Datasets <=SEP=> With social media datasets being increasingly shared by researchers, it also presents the caveat that those datasets are not always completely replicable. Having to adhere to requirements of platforms like Twitter, researchers cannot release the raw data and instead have to release a list of unique identifiers, which others can then use to recollect the data from the platform themselves. This leads to the problem that subsets of the data may no longer be available, as content can be deleted or user accounts deactivated. To quantify the impact of content deletion in the replicability of datasets in a long term, we perform a longitudinal analysis of the persistence of 30 Twitter datasets, which include over 147 million tweets. Having the original datasets collected between 2012 and 2016, and recollecting them later by using the tweet IDs, we look at four different factors that quantify the extent to which recollected datasets resemble original ones: completeness, representativity, similarity and changingness. Even though the ratio of available tweets keeps decreasing as the dataset gets older, we find that the textual content of the recollected subset is still largely representative of the whole dataset that was originally collected. The representativity of the metadata, however, keeps decreasing over time, both because the dataset shrinks and because certain metadata, such as the users’ number of followers, keeps changing. Our study has important implications for researchers sharing and using publicly shared Twitter datasets in their research.

Transaction Repair for Multi-Version Concurrency Control <=SEP=> The optimistic variants of Multi-Version Concurrency Control (MVCC) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even more conflicts.
 In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, when encountering conflicts among transactions, our mechanism quickly detects the conflict locations in the program and partially re-executes the conflicting transactions. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput.