Existing probabilistic retrieval models do not restrict the domain of the random variables that they deal with. In this article, we show that the upper bound of the normalized term frequency (
) from the relevant documents is much smaller than the upper bound of the normalized
from the whole collection. As a result, the existing models suffer from two major problems: (i) the domain mismatch causes data modeling error, (ii) since the outliers have very large magnitude and the retrieval models follow
hypothesis, the combination of these two factors tends to overestimate the relevance score. In an attempt to address these problems, we propose novel weighted probabilistic models based on truncated distributions. We evaluate our models on a set of large document collections. Significant performance improvement over six existing probabilistic models is demonstrated.
This article considers the task of text style transfer: transforming a specific style of sentence into another while preserving its style-independent content. A dominate approach to text style transfer is to learn a good content factor of text, define a fixed vector for every style and recombine them to generate text in the required style. In fact, there are a large number of different words to convey the same style from different aspects. Thus, using a fixed vector to represent one style is very inefficient, which causes the weak representation power of the style vector and limits text diversity of the same style. To address this problem, we propose a novel neural generative model called Adversarial Separation Network (ASN), which can learn the content and style vector jointly and the learnt vectors have strong representation power and good interpretabilities. In our method, adversarial learning is implemented to enhance our model’s capability of disentangling the two factors. To evaluate our method, we conduct experiments on two benchmark datasets. Experimental results show our method can perform style transfer better than strong comparison systems. We also demonstrate the strong interpretability of the learnt latent vectors.
) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the
In-phase and Quadrature
) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the
packet reception rate
) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.
A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.
Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge.
Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors:
—whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and
—whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks.
In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study.
Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data.
It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.
Laban Movement Analysis (LMA) and its Effort element provide a conceptual framework through which we can observe, describe, and interpret the intention of movement. Effort attributes provide a link between how people move and how their movement communicates to others. It is crucial to investigate the perceptual characteristics of Effort to validate whether it can serve as an effective framework to support a wide range of applications in animation and robotics that require a system for creating or perceiving expressive variation in motion. To this end, we first constructed an Effort motion database of short video clips of five different motions:
walk, sit down, pass, put, wave
performed in eight ways corresponding to the extremes of the Effort elements. We then performed a perceptual evaluation to examine the perceptual
among Effort elements:
Space (Indirect/Direct), Time (Sustained/Sudden), Weight (Light/Strong),
that appeared in the motion stimuli. The results of the perceptual consistency evaluation indicate that although the observers do not perceive the LMA Effort element 100% as intended, true response rates of seven Effort elements are higher than false response rates except for
Effort. The perceptual consistency results showed varying tendencies by motion. The perceptual association between LMA Effort elements showed that a single LMA Effort element tends to co-occur with the elements of other factors, showing significant correlation with one or two factors (e.g., indirect and free, light and free).
CRISPR-Cas has revolutionized genome editing and has a great potential for applications, such as correcting human genetic disorders. To increase the safety of genome editing applications, CRISPR-Cas may benefit from strict control over Cas enzyme activity. Previously, anti-CRISPR proteins and designed oligonucleotides have been proposed to modulate CRISPR-Cas activity. Here we report on the potential of guide-complementary DNA oligonucleotides as controlled inhibitors of Cas9 ribonucleoprotein complexes. First, we show that DNA oligonucleotides down-regulate Cas9 activity in human cells, reducing both on and off-target cleavage. We then used in vitro assays to better understand how inhibition is achieved and under which conditions. Two factors were found to be important for robust inhibition: the length of the complementary region, and the presence of a PAM-loop on the inhibitor. We conclude that DNA oligonucleotides can be used to effectively inhibit Cas9 activity both ex vivo and in vitro.
This study aims to determine the factors that lead to the occurrence of dynastic politics in village leadership in Wantiworo Village. The research method used is descriptive qualitative research. Data collection techniques were carried out through in-depth interviews (interviews), observation and document studies. Analysis of the data and informants that have been obtained was carried out qualitatively.The results of research on dynastic politics in village leadership (Study of Wantiworo Village, Kabawo District, Muna Regency) where the most influencing the occurrence of dynastic politics in Wantiworo Village are only two factors, including capital strength (economic), the economic ability of a village head is also a consideration in the nomination . Wealth owned by the village head is the basic capital to achieve a goal or victory. Then the power of the network (the family), the family of the village head does have quite a big influence in the community. For example, the former village head of La Ode Gafar is a religious figure and La Ode Kiji is a person who is quite respected in the community besides other important positions in the village that have kinship relations.
Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance.The one-window algorithm of Kim et al. Uses Landsat satellite imagery to model the earth's surface temperature.These trends are validated using meteorological data. Two main and basic factors play a major role in the temporal and spatial trend of the thermal islands of Rasht. These two factors of climate change that have occurred in the last two decades in the region of Gilan province and the city of Rasht. The second factor that has greatly enhanced the effect of the first factor is the human factor that has greatly included other urban factors in Rasht, including urban management and proper urban planning in the province and the city of Rasht. These two factors in the temporal and spatial trend of urban thermal islands have caused thermal islands to rapidly increase the growth of the city and urban population from the urban center to the western and southwestern regions and have very negative effects on land use changes and human areas. It has caused the construction of Rasht city.
Plasmonics in two-dimensional materials, an emerging direction of nano-optics, has attracted great attention recently, which exhibits unique properties than that in noble metals. Extending its advanced features by different manipulations is very beneficial for its promotion. In this paper, we study plasmonic excitations in graphene and black phosphorus (BP) nanostructures, where the effects of structural symmetry and material anisotropy are discussed. We show that the two factors are crucial to mode excitations, e.g. the extinction can be dominated by higher order modes rather than dipole resonance. The behavior occurs only in the direction hosting larger resonance frequencies, e.g. armchair (AC) direction of BP and shorter side of graphene rectangles. In BP rectangles along AC direction, the two factors are competing, and thus can be applied cooperatively to tune plasmonic resonance, from dipole to higher order excitations. Besides, the manipulation can also be achieved by designing BP square rings, in which the interaction between outer and inner edges show great impact on mode excitations. Our studies further promote the understanding of plasmonics in two-dimensional materials, and will pave the way for particular plasmonic applications.