Miracle-dz/UniFAR-FTUs huggingface dataset:
Exit, Voice, and Response on Digital Platforms: An Empirical Investigation of Online Management Response Strategies

In the past decade, we have witnessed the growing importance of management responses to online reviews on digital platforms. In this study, we examine the impact of online management responses on business performance and their spillover effect on nearby businesses. By adopting multiple causal identification strategies to address the issue of self-selected responses, we find that the responses by a business owner play a significant role in the performance of the focal business as well as in the performance of the nearby businesses. We observe that, in general, the launch of the new management response feature benefits businesses. What is more interesting is that the benefit is not observed in a consistent manner across all businesses. Only the businesses that choose to use the management response feature observe increases in check-ins. On the other hand, the businesses that are unaware of the management response feature launch on digital platforms, or are aware but choose not to use the management response feature, tend to remain at a disadvantage. Interestingly, we also uncover that the spillover effect externality of online management responses on the nearby businesses crucially depends on whether the focal business and the nearby businesses are in direct competition. Furthermore, we identify conditions under which business owners are more likely to respond to consumer comments. Our findings have direct implications for both business owners and digital platforms: Digital platforms can help businesses develop the right engagement strategies by taking the online response strategies of the nearby businesses into account. The online appendix is available at https://doi.org/10.1287/isre.2017. 0749 .
Towards User Recognition by Shallow Web Traffic Inspection

We consider the problem of web user recognition, or web traffic de-anonymization: given a set of users, is it possible to identify which user has generated a given web traffic with only a shallow packet inspection (that is, without looking inside the packet payloads)? We propose to address this problem by means of machine learning (ML) techniques, in particular clustering and supervised classification. The basic idea is that each user can be identified by their browsing habits, and these habits can be described by a suitable set of features: click frequency, permanence time on web pages, amount of downloaded data, etc. In this paper we introduce these features, and show how these can be derived from the data obtained only from packet headers and their arrival time. Finally, we show the effectiveness of this approach with some preliminary tests and experiments.
GmDAD1, a Conserved Defender Against Cell Death 1 (DAD1) From Soybean, Positively Regulates Plant Resistance Against Phytophthora Pathogens

Initially identified as a mammalian apoptosis suppressor, defender against apoptotic death 1 (DAD1) protein has conserved plant orthologs acting as negative regulators of cell death. The potential roles and action mechanisms of plant DADs in resistance against Phytophthora pathogens are still unknown. Here, we cloned GmDAD1 from soybean and performed functional dissection. GmDAD1 expression can be induced by Phytophthora sojae infection in both compatible and incompatible soybean varieties. By manipulating GmDAD1 expression in soybean hairy roots, we showed that GmDAD1 transcript accumulations are positively correlated with plant resistance levels against P. sojae. Heterologous expression of GmDAD1 in Nicotiana benthamiana enhanced its resistance to Phytophthora parasitica. NbDAD1 from N. benthamiana was shown to have similar role in conferring Phytophthora resistance. As an endoplasmic reticulum (ER)-localized protein, GmDAD1 was demonstrated to be involved in ER stress signaling and to affect the expression of multiple defense-related genes. Taken together, our findings reveal that GmDAD1 plays a critical role in defense against Phytophthora pathogens and might participate in the ER stress signaling pathway. The defense-associated characteristic of GmDAD1 makes it a valuable working target for breeding Phytophthora resistant soybean varieties.
Some Evidence for an Association Between Early Life Adversity and Decision Urgency

The relationship between early life adversity and adult outcomes is traditionally investigated relative to risk and protective factors (e.g., resilience, cognitive appraisal), and poor self-control or decision-making. However, life history theory suggests this relationship may be adaptive—underpinned by mechanisms that use early environmental cues to alter the developmental trajectory toward more short-term strategies. These short-term strategies have some theoretical overlap with the most common process models of decision-making—evidence accumulation models—which model decision urgency as a decision threshold. The current study examined the relationship between decision urgency (through the linear ballistic accumulator) and early life adversity. A mixture of analysis methods, including a joint model analysis designed to explicitly account for uncertainty in estimated decision urgency values, revealed weak-to-strong evidence in favor of a relationship between decision urgency and early life adversity, suggesting a possible effect of life history strategy on even the most basic decisions.
Neurophysiological Activations of Predictive and Non-predictive Exogenous Cues: A Cue-Elicited EEG Study on the Generation of Inhibition of Return

In cueing tasks, predictive and non-predictive exogenous spatial cues produce distinct patterns of behavioral effects. Although both cues initially attract attention, only non-predictive cues lead to inhibitory effects (worse performance at the cued location as compared to the uncued location) if the time elapsed between the cue and the target is long enough. However, the process/processes leading to the later inhibitory effect, named inhibition of return (IOR), are still under debate. In the present study, we used cue-elicited EEG activations from predictive and non-predictive exogenous spatial cues to further investigate the neural processes involved in IOR. Unlike previous similar studies, we intermixed both types of cues in a block of trials, in an attempt to identify the unique neurophysiological activations associated with the generation of IOR. We found that predictive and non-predictive cues significantly differed in activation just at 400–470 ms post-cue window. Activation was greater for non-predictive cues in the intraparietal sulcus (IPS), and this activation correlated significantly with IOR effects. These findings support the hypothesis that the posterior parietal cortex plays a crucial role in the generation of IOR.
Incidence of Mild Cognitive Impairment and Dementia in Parkinson’s Disease: The Parkinson’s Disease Cognitive Impairment Study

Background: Cognitive impairment in Parkinson’s disease (PD) includes a spectrum varying from Mild Cognitive Impairment (PD-MCI) to PD Dementia (PDD). The main aim of the present study is to evaluate the incidence of PD-MCI, its rate of progression to dementia, and to identify demographic and clinical characteristics which predict cognitive impairment in PD patients. Methods: PD patients from a large hospital-based cohort who underwent at least two comprehensive neuropsychological evaluations were retrospectively enrolled in the study. PD-MCI and PDD were diagnosed according to the Movement Disorder Society criteria. Incidence rates of PD-MCI and PDD were estimated. Clinical and demographic factors predicting PD-MCI and dementia were evaluated using Cox proportional hazard model. Results: Out of 139 enrolled PD patients, 84 were classified with normal cognition (PD-NC), while 55 (39.6%) fulfilled the diagnosis of PD-MCI at baseline. At follow-up (mean follow-up 23.5 ± 10.3 months) 28 (33.3%) of the 84 PD-NC at baseline developed MCI and 4 (4.8%) converted to PDD. The incidence rate of PD-MCI was 184.0/1000 pyar (95% CI 124.7–262.3). At multivariate analysis a negative association between education and MCI development at follow-up was observed (HR 0.37, 95% CI 0.15–0.89; p = 0.03). The incidence rate of dementia was 24.3/1000 pyar (95% CI 7.7–58.5). Out of 55 PD-MCI patients at baseline, 14 (25.4%) converted to PDD, giving an incidence rate of 123.5/1000 pyar (95% CI 70.3–202.2). A five time increased risk of PDD was found in PD patients with MCI at baseline (RR 5.09, 95% CI 1.60–21.4). Conclusion: Our study supports the relevant role of PD-MCI in predicting PDD and underlines the importance of education in reducing the risk of cognitive impairment.
Steering motion control of a snake robot via a biomimetic approach

We propose a biomimetic approach for steering motion control of a snake robot. Inspired by a vertebrate biological motor system paradigm, a hierarchical control scheme is adopted. In the control scheme, an artificial central pattern generator (CPG) is employed to generate serpentine locomotion in the robot. This generator outputs the coordinated desired joint angle commands to each lower-level effector controller, while the locomotion can be controlled through CPG modulation by a higher-level motion controller. The motion controller consists of a cerebellar model articulation controller (CMAC) and a proportional-derivative (PD) controller. Because of the fast learning ability of the CMAC, the proposed motion controller can drive the robot to track the desired orientation and adapt to unexpected perturbations. The PD controller is employed to expedite the convergence speed of the motion controller. Finally, both numerical studies and experiments proved that the proposed approach can help the snake robot achieve good tracking performance and adaptability in a varying environment.
Data-flow/dependence profiling for structured transformations

Profiling feedback is an important technique used by developers for performance debugging, where it is usually used to pinpoint performance bottlenecks and also to find optimization opportunities. Assessing the validity and potential benefit of a program transformation requires accurate knowledge of the data flow and dependencies, which can be uncovered by profiling a particular execution of the program. In this work we develop poly-prof, an end-to-end infrastructure for dynamic binary analysis, which produces feedback about the potential to apply complex program rescheduling. Our tool can handle both inter- and intraprocedural aspects of the program in a unified way, thus providing interprocedural transformation feedback.
Adaptive sparse matrix-matrix multiplication on the GPU

In the ongoing efforts targeting the vectorization of linear algebra primitives, sparse matrix-matrix multiplication (SpGEMM) has received considerably less attention than sparse Matrix-Vector multiplication (SpMV). While both are equally important, this disparity can be attributed mainly to the additional formidable challenges raised by SpGEMM. In this paper, we present a dynamic approach for addressing SpGEMM on the GPU. Our approach works directly on the standard compressed sparse rows (CSR) data format. In comparison to previous SpGEMM implementations, our approach guarantees a homogeneous, load-balanced access pattern to the first input matrix and improves memory access to the second input matrix. It adaptively re-purposes GPU threads during execution and maximizes the time efficient on-chip scratchpad memory can be used. Adhering to a completely deterministic scheduling pattern guarantees bit-stable results during repetitive execution, a property missing from other approaches. Evaluation on an extensive sparse matrix benchmark suggests our approach being the fastest SpGEMM implementation for highly sparse matrices (80% of the set). When bit-stable results are sought, our approach is the fastest across the entire test set.
SEP-graph: finding shortest execution paths for graph processing under a hybrid framework on GPU

In general, the performance of parallel graph processing is determined by three pairs of critical parameters, namely synchronous or asynchronous execution mode (Sync or Async), Push or Pull communication mechanism (Push or Pull), and Data-driven or Topology-driven traversing scheme (DD or TD), which increases the complexity and sophistication of programming and system implementation of GPU. Existing graph-processing frameworks mainly use a single combination in the entire execution for a given application, but we have observed their variable and suboptimal performance. In this paper, we present SEP-Graph, a highly efficient software framework for graph-processing on GPU. The hybrid execution mode is automatically switched among three pairs of parameters, with an objective to achieve the shortest execution time in each iteration. We also apply a set of optimizations to SEP-Graph, considering the characteristics of graph algorithms and underlying GPU architectures. We show the effectiveness of SEP-Graph based on our intensive and comparative performance evaluation on NVIDIA 1080, P100, and V100 GPUs. Compared with existing and representative GPU graph-processing framework Groute and Gunrock, SEP-Graph can reduce execution time up to 45.8 times and 39.4 times.
Provably and practically efficient granularity control

Over the past decade, many programming languages and systems for parallel-computing have been developed, e.g., Fork/Join and Habanero Java, Parallel Haskell, Parallel ML, and X10. Although these systems raise the level of abstraction for writing parallel codes, performance continues to require labor-intensive optimizations for coarsening the granularity of parallel executions. In this paper, we present provably and practically efficient techniques for controlling granularity within the run-time system of the language. Our starting point is "oracle-guided scheduling", a result from the functional-programming community that shows that granularity can be controlled by an "oracle" that can predict the execution time of parallel codes. We give an algorithm for implementing such an oracle and prove that it has the desired theoretical properties under the nested-parallel programming model. We implement the oracle in C++ by extending Cilk and evaluate its practical performance. The results show that our techniques can essentially eliminate hand tuning while closely matching the performance of hand tuned codes.
Making concurrent algorithms detectable: poster

Non-volatile memory (NVM) promises persistent main memory that remains correct despite loss of power. Since caches are expected to remain volatile, concurrent algorithms must be redesigned to ensure a consistent state after a system crash, and to continue the execution upon recovery. We give the first general construction to make any concurrent program persistent, and show that the persistent version is guaranteed to have at most a constant factor blow-up in both steps and contention. We also provide an optimized transformation for normalized lock-free data structures. We experimentally evaluate our transformation by comparing it to a persistent transactional memory, as well as a hand-tuned persistent algorithm. We show that our transformation's performance is reasonable given its generality.
Multiband Faster Than Nyquist Techniques in Multipath Channel for Spectrum Efficiency

—For improving bandwidth efficiency, FTN (FasterThan Nyquist) method which transmits faster than the Nyquist rate is applied in wireless communications. However, performance degrade arising from ISI(Inter-Symbol Interference) induced by increasing the interference rate. To overcome this problem, we propose the multiband OFDM(Orthogonal Frequency Division Multiplexing)-FTN method which is uniformly dividing and allocating the encoded bits into multiband, and transmits the encoded bits using the frequency allocated to each band by applying the FTN transmission method to each band. Through simulation results by setting the interference ratio to 25%, we show that the performance of the proposed multiband FTN method is improved about 0.3 dB ~ 0.5 dB than that of single band FTN method. In multipath channels, only multiband FTN method was vulnerable to multipath whereas the multiband OFTM-FTN method was efficient for multipath. In addition, we confirmed that the performance improved as the subcarrier number of the proposed multiband OFDM-FTN method increased.
T-thinker: a task-centric distributed framework for compute-intensive divide-and-conquer algorithms

Many computationally expensive problems are solved by a divide-and-conquer algorithm: a problem over a big dataset can be recursively divided into independent tasks over smaller subsets of the dataset. We present a distributed general-purpose framework called T-thinker which effectively utilizes the CPU cores in a cluster by properly decomposing an expensive problem into smaller independent tasks for parallel computation. T-thinker well overlaps CPU processing with network communication, and its superior performance is verified over a re-engineered graph mining system G-thinker available at http://cs.uab. edu/yanda/gthinker/.
Encapsulated open nesting for STM: fine-grained higher-level conflict detection

Open nesting allows replacing the automatic detection of conflicting memory accesses used in transactional memory (TM) with programmer-specified higher-level conflict detection. Higher-level conflict detection allows removing conflicts of commuting operations where the automatic conflict detection is too conservative. Different conflict detection schemes are incompatible with each other and thus must operate on separate memory locations to prevent inconsistencies. Using open nesting, a programmer implements this separation manually using source code modifications possibly assisted by static checks. Encapsulated open nesting extends open nesting by automatically preventing concurrent accesses to the same memory location using different conflict detection schemes unless all accesses are reads. This approach uses an additional synchronization layer with the same granularity as the existing synchronization of TM. This automatism reduces programming effort by allowing fine-grained use of open nesting and extends the scope of open nesting to abstractions with unknown implementation. We extend the TLRW algorithm to support encapsulated open nesting. An evaluation using an implementation of this algorithm within the Deuce framework shows an average overhead of 21.7% for encapsulated open nesting (compared to regular open nesting) for nine benchmarks while simplifying programming.
A distributed hypervisor for resource aggregation: poster

Scale-out has become the standard answer to data analysis, machine learning and many other fields. Contrary to common belief, scale-up machines can outperform scale-out clusters for a considerable portion of tasks. However, those scale-up machines are not economical and may not be affordable for small businesses. This paper presents GiantVM, a distributed hypervisor that aggregates resources from multiple physical machines, providing the guest OS with a uniform hardware abstraction. We propose techniques to deal with the challenges of CPU, Memory, and I/O virtualization in distributed environments.
Exploiting the input sparsity to accelerate deep neural networks: poster

Efficient inference of deep learning models are challenging and of great value in both academic and industrial community. In this paper, we focus on exploiting the sparsity in input data to improve the performance of deep learning models. We propose an end-to-end optimization pipeline to generate programs for the inference with sparse input. The optimization pipeline contains both domain-specific and general optimization techniques and is capable of generating efficient code without relying on the off-the-shelf libraries. Evaluations show that we achieve significant speedups over the state-of-the-art frameworks and libraries on a real-world application, e.g., 9.8× over TensorFlow and 3.6× over Intel MKL on the detection in autonomous driving.
Political memes and the politics of memes: A methodological proposal for content analysis of online political memes

This paper discusses preliminary results of a comprehensive research on the uses and appropriations of political-electoral memes. Our main goal at this stage is to develop, following former propositions from other scholars, a taxonomical matrix able to assist researchers interested in dealing with online political memes with greater objectivity. Therefore, we performed a content analysis of memes that circulated on Twitter during Brazilian 2014 presidential elections.
Incremental flattening for nested data parallelism

Compilation techniques for nested-parallel applications that can adapt to hardware and dataset characteristics are vital for unlocking the power of modern hardware. This paper proposes such a technique, which builds on flattening and is applied in the context of a functional data-parallel language. Our solution uses the degree of utilized parallelism as the driver for generating a multitude of code versions, which together cover all possible mappings of the application's regular nested parallelism to the levels of parallelism supported by the hardware. These code versions are then combined into one program by guarding them with predicates, whose threshold values are automatically tuned to hardware and dataset characteristics. Our unsupervised method---of statically clustering datasets to code versions---is different from autotuning work that typically searches for the combination of code transformations producing a single version, best suited for a specific dataset or on average for all datasets. We demonstrate---by fully integrating our technique in the repertoire of a compiler for the Futhark programming language---significant performance gains on two GPUs for three real-world applications, from the financial domain, and for six Rodinia benchmarks.
CircFunBase: a database for functional circular RNAs

Abstract Increasing evidence reveals that circular RNAs (circRNAs) are widespread in eukaryotes and play important roles in diverse biological processes. However, a comprehensive functionally annotated circRNA database is still lacking. CircFunBase is a web-accessible database that aims to provide a high-quality functional circRNA resource including experimentally validated and computationally predicted functions. The current version of CircFunBase documents more than 7000 manually curated functional circRNA entries, mainly including Homo sapiens, Mus musculus etc. CircFunBase provides visualized circRNA-miRNA interaction networks. In addition, a genome browser is provided to visualize the genome context of circRNAs. As a biological information platform for circRNAs, CircFunBase will contribute for circRNA studies and bridge the gap between circRNAs and their functions.
Hallucinations in Children and Adolescents: An Updated Review and Practical Recommendations for Clinicians

Abstract Hallucinations in children and adolescents are now known to occur on a continuum from healthy to psychopathology-related phenomena. Although hallucinations in young populations are mostly transient, they can cause substantial distress. Despite hallucinations being widely investigated, research so far has had limited implications for clinical practice. The present article has 3 main aims: (1) to review research findings since 2014 (when the last major review of the area was published); (2) to present assessment tools validated to measure hallucinations in children and adolescents; and (3) to discuss therapeutic strategies and clinical issues. We conclude by presenting a tailored care model for clinicians and outline future challenges for research.