Learning to Rank in the Age of Muppets

The emergence of BERT in 2018 has brought a huge boon to retrieval effectiveness in many tasks across various domains and led the recent research landscape of IR to transformer-related technologies. While researchers are fascinated by the power of BERT, along with related transformer models, substantial computational costs incurred by transformers become an unavoidable problem. Meanwhile, under the light of BERT, there are ''out-of-date'' but fairly effective techniques forgotten by people. For example, learning to rank was one of the most popular technologies a decade ago. In this work, we aim to answer two research questions: RQ1 is whether using learning to rank as a filtering stage in a multi-stage reranking pipeline can improve the efficiency of reranking using transformers without sacrificing effectiveness. In addition, we are interested in if using transformer-based features in the traditional learning to rank framework can increase effectiveness as RQ2. To answer RQ1, we implement a multi-stage reranking pipeline which places learning to rank as a filter in the middle stage. This configuration allows the pipeline to only send the most promising candidates using cheap learning to rank module to expensive neural rerankers, hence a speedup in overall latency for transformer-based reranking can be obtained without a degradation in effectiveness. By applying the pipeline on MS MARCO passage and document ranking tasks, we can achieve up to 18 times increase in efficiency while maintaining the same level of effectiveness. Moreover, our method is orthogonal to other techniques that focus on neural models themselves to accelerate inference. Hence, our method can be combined with other accelerating works to further save computational costs and latency. For RQ2, since transformers generate relevance scores for different query-document pairs independently, it is possible to use transformer-based scores as learning to rank features, so that learning to rank can take advantage of transformers to increase retrieval effectiveness. Applied to the MS MARCO passage and document ranking tasks, we gain a maximal 52% increase in effectiveness by adding the BERT-based feature compared to the ''traditional'' learning to rank. Also, we obtain a result with a little bit higher effectiveness by adding transformer-based features with other traditional features in learning to rank, compared to the standard retrieve-and-rerank design with transformers. This work explores potential roles of learning to rank in the age of muppets. In a broader sense, this work illustrates that we should stand on the shoulder of giants, which is what we learned and discovered in history, to explore next unknowns

Semiparametric Quantitative-Trait-Locus Mapping: II. on Censored Age-at-Onset

In genetic studies, the variation in genotypes may not only affect different inheritance patterns in qualitative traits, but may also affect the age-at-onset as quantitative trait. In this article, we use standard cross designs, such as backcross or F2, to propose some hazard regression models, namely, the additive hazards model in quantitative trait loci mapping for age-at-onset, although the developed method can be extended to more complex designs. With additive invariance of the additive hazards models in mixture probabilities, we develop flexible semiparametric methodologies in interval regression mapping without heavy computing burden. A recently developed multiple comparison procedures is adapted to identify the QTL in dense maps. The proposed methodologies will be evaluated by simulation studies and demonstrated in an actual data analysis of forest tree growth

Gravity Effects on Information Filtering and Network Evolving

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model

Attributable Risk Function in the Proportional Hazards Model

As an epidemiological parameter, the population attributable fraction is an important measure to quantify the public health attributable risk of an exposure to morbidity and mortality. In this article, we extend this parameter to the attributable fraction function in survival analysis of time-to-event outcomes, and further establish its estimation and inference procedures based on the widely used proportional hazards models. Numerical examples and simulations studies are presented to validate and demonstrate the proposed methods

Method of constructing braid group representation and entanglement in a Yang-Baxter sysytem

In this paper we present reducible representation of the $n^{2}$ braid group representation which is constructed on the tensor product of n-dimensional spaces. By some combining methods we can construct more arbitrary $n^{2}$ dimensional braiding matrix S which satisfy the braid relations, and we get some useful braiding matrix S. By Yang-Baxteraition approach, we derive a $9\times9$ unitary $\breve{R}$ according to a $9\times9$ braiding S-matrix we have constructed. The entanglement properties of $\breve{R}$-matrix is investigated, and the arbitrary degree of entanglement for two-qutrit entangled states can be generated via $\breve{R}(\theta, \phi_{1},\phi_{2})$-matrix acting on the standard basis.Comment: 9 page

Landslides Caused by Climate Change and Groundwater Movement in Permafrost Mountain

Climate change induced warming results in permafrost degradation. Melting permafrost subsequently leads to an increased incidence of landslides. The study area was within the northwest section of the Lesser Khingan Range in northern China along the Bei\u27an-Heihe Highway. We analyzed the impact of climate change on landslide movement in the permafrost zone via a combination of geological survey and meteorological data. The average annual temperature of the study area has increased by 3.2°C in last 60 years, and permafrost degradation is severe. Loose soil on the hillside surface provides appropriate conditions for the infiltration of atmospheric precipitation and snowmelt, and seepage from thawing permafrost. As it infiltrates downwards, water is blocked by the underlying permafrost or dense soil, and infiltrates along this barrier layer toward lower positions, forming a potential sliding zone. The combination of high density resistivity (HDR) methods based on soil resistivity values, ground-penetrating radar (GPR) methods based on characteristics of radar wave reflection, respectively, and geological drilling can be utilized to determine the regional stratigraphic distribution. This will allow the exact location of the landslide sliding surface to be precisely determined. Field test results indicate that radar reflectivity characteristics and the resistivity values of the soil in the landslide mass is significantly different from surrounding soil. There are sudden decreases in the apparent resistivity values at the sliding surface location. In addition, the radar exhibits strong reflection at the sliding surface position, with a sudden increase in the amplitude of the radar wave. Drilling results indicate that the soil has high water content at the location of the sliding surface of the landslide mass in the study area, which is entirely consistent with the GPR and HDR results. Thus, abnormal radar wave reflection and abrupt changes in apparent resistivity values can be used in practice to identify the location of landslide sliding surfaces in this region. We produce a detailed analysis of a representative landslide within the study area. Displacement monitoring locations were positioned at the trailing edge of the landslide mass and on the landslide mass surface. We then used this data to determine the relationships of landslide movement with both ground temperature and the trailing edge pore water pressure. The results suggest seasonal variation in the landslide movement process and characteristics of an annual cyclical trend. Landslide movement can be described by intermittence and low angles. The slip rate and the timing of slide occurrence exhibit relationships with the trailing edge pore water pressure of the landslide mass. The seepage of thaw water into the landslide mass will impact the trailing edge pore water pressure of the landslide mass. This phenomenon is identified as the primary cause of landslide movement

Resistivity Model of Frozen Soil and High‐Density Resistivity Method for Exploration Discontinuous Permafrost

In permafrost‐degraded areas, “islands” of permafrost can be buried in the unfrozen soil. When permafrost is arranged in this discontinuous pattern, it is more difficult to analyze from an engineering or geological perspective. The degree of resistivity of unfrozen soil is determined by the dry density, temperature, moisture content, and pore water resistivity of the soil, as well as by the mineral composition, size, and cementing state of the soil particles. Part of the water in the soil pores experiences a phase change as the soil freezes, so permafrost has different resistivity than unfrozen soil. In this chapter, we explore the conduction characteristics of permafrost. First, we established a theoretical model to analyze the factors affecting the resistivity of permafrost. Next, we used an experimental study to analyze how unfrozen water content, initial moisture content, soil temperature, and dry density influence the resistivity of frozen soil. These experimental study results served to validate the rationality of the model of permafrost resistivity. To analyze differences in conductivity between underground media, we used a high‐density resistivity (HDR) method, which infers the storage of underground geologic bodies with different resistivity based on the distribution of a conduction current under the electric field action. In this chapter, the WGMD‐9 super HDR measurement system produced by the Chongqing Benteng Numerical Control Technique Research Institute was used to obtain the resistivity profile. The study region was the road area from Bei’an Expressway to Heihe Expressway in the permafrost degeneration area in Northeast China. A permafrost profile map was drawn based on data from engineering drilling and an analysis of factors that influence permafrost resistivity. The reliability of the permafrost profile map was verified by an analysis of temperature data taken at measured points at different depths of the soil profile