Pseudo-Relevance Feedback (PRF) is a key technique in information retrieval (IR). Traditional implementations rely on statistical information, such as term frequency, for precise matching and relevance assessment. However, these methods struggle to fully capture the deep semantic integrity of query terms, especially in handling polysemy, high semantic relevance, and long-document comprehension. To address these challenges, this paper innovatively proposes a large language model-assisted PRF probabilistic model. The model first employs a precise matching algorithm to evaluate and determine the term-level weights, and then uses a large language model to encode the contextual relationships within the query and feedback documents, thereby accurately acquiring the global semantic weights of terms relevant to the query at the document level. By adjusting a balancing factor to allocate weights between these two components, the model comprehensively selects expanded terms for constructing a new query representation and executing query expansion (QE). This model not only facilitates approximate matching through the integration of global semantic features of documents but also effectively combines with the precise matching information of traditional PRF models, enabling a comprehensive and accurate optimization of queries from a broader perspective. To validate effectiveness, extensive empirical analyses on five TREC datasets assess performance across key metrics such as MAP, P@10, NDCG, and MRR. Experimental results show significant improvements over baseline models. Comparative analyses and case studies confirm that the expanded terms maintain high semantic relevance and consistency with the original query while preserving diversity and effectively capturing global document semantics, establishing an efficient QE mechanism.

In the context of exoskeleton rehabilitation for patients in the later stages of recovery, reducing unnecessary interactive torque and enhancing exoskeleton transparency is important, where patients are encouraged to engage in voluntary movement. This study introduces a novel method known as Periodic Kernelized Movement Primitives (PerKMP) to address this issue. PerKMP is an advanced iteration of the original Kernelized Movement Primitives (KMP) that integrates periodic kernel functions for heightened adaptability. Two distinct PerKMP modules have been developed: the desired trajectory modulation PerKMP, which dynamically plans the desired trajectory according to the walking state, and the joint torque estimation PerKMP, which accurately estimates total joint torque in real-time and adjusts controller stiffness accordingly. A series of experiments with different walking speeds and different subjects were conducted to verify the validity of the method. Through the combined implementation of two PerKMP modules, the average absolute value of the interactive torque and energy per unit distance (EPUD) were reduced effectively. This research broadens the application of imitation learning methods in exoskeletons, and enables real-time control adjustments, thereby presenting a pioneering approach to enhancing the adaptability of exoskeleton rehabilitation systems.

High-speed on/off valves (HSVs) are recognized for their rapid response and high reliability, and are widely employed as pilot elements in proportional valves. However, their inherent switching behavior intensifies the nonlinear characteristics of the control system, thereby limiting fluid delivery precision. In addition, the main spool of the proportional valve is subjected to significantly time-varying load forces. To address these challenges, a nonlinear flow model of the HSV is established, and the dynamics of pressure pulsation propagation are systematically analyzed. Meanwhile, a dynamic model of the proportional valve main spool is developed, explicitly incorporating the effects of time-varying load forces. Based on these models, a load-adaptive sliding mode control (LASMC) strategy is proposed to improve the motion control performance of a high-speed on/off valve–piloted proportional valve (HSVPPV). Motion control experiments conducted on the HSVPPV and its valve-controlled system validated the proposed approach. Compared to conventional PI control, LASMC reduced the main spool’s maximum displacement error from 0.696 mm to 0.312 mm (a 55.2% reduction) and standard deviation from 0.213 mm to 0.127 mm (a 40.4% reduction). At the actuator level, the cylinder’s maximum error decreased from 14.996 mm to 11.527 mm (a 23.1% reduction) and standard deviation from 11.347 mm to 10.323 mm (a 9.0% reduction). These results demonstrate that the proposed controller significantly enhances both valve positioning accuracy and overall system tracking stability.

DNA methylation is a key epigenetic modification underlying cellular identity. Conventional methods based on CpG site-level data often lack sensitivity in detecting low-frequency methylation signals. Here, we present Alpha, a novel method combining unbiased segmentation with robust read-level identification of low frequency cell-type-specific methylation signals. Methylation markers identified by Alpha exhibited significant enrichment in regulatory genomic elements such as enhancers, active promoters, and transcription factor binding sites. In simulated cell-type admixtures, Alpha-derived markers demonstrated improved deconvolution performance, exhibiting lower error metrics compared to beta-value based methods (DSS), even with limited marker numbers (N < 50). We combined Alpha with a non-negative least squares approach (Alpha-NNLS) to enable sensitive detection of circulating tumor DNA (ctDNA) in simulated cell-free DNA from breast and colon cancers, outperforming existing read-level methylation-based tumor fraction estimation methods (CelFEER and UXM). We applied Alpha-NNLS to targeted bisulfite sequencing data from early-stage colon cancer plasma samples and demonstrated strong concordance with existing approaches (R2 = 0.98), supporting its potential for sensitive detection of ctDNA.

The electrophysiological findings have shown that epileptiform spikes triggering sleep spindles within 1[Formula: see text]s across multiple channels are commonly observed during sleep in focal epilepsy (FE). Such spatio-temporal couplings of spikes and spindles (STCSSs) are defined as a kind of pathological waves, and frequent emergence of them may cause the degradation of cognitive function for FE patients. However, the neural mechanisms underlying STCSSs are not well understood. To this end, this work first develops a neural mass network model for focal epilepsy (FE-NMNM) with multiple thalamocortical columns being its nodes and the long-range synaptic interactions of thalamocortical columns being its edges, where each thalamocortical column is extended on the basis of Costa model and then they are connected through excitatory synapses between pyramidal cells. Then, how the cortico-cortical connectivity affects the evolution of STCSSs across the network is especially discussed by simulations in two cases, where the inter-ictal state and the ictal state are considered separately. Simulation results demonstrate that: (1) the more STCSSs occur in a more extensive area when the cortico-cortical connectivity becomes stronger, and the significant increase of coupling discharges is attributed to the presence of abundant spikes; (2) when the connectivity is excessively strong, the cortical hyperexcitability will happen, thereby inducing massive spike discharges which may further inhibit the occurrence of spindles, and hence, resulting in the disappearance of STCSSs. The obtained results provide a mechanistic insight into STCSSs, and suggest that such coupling patterns could reflect widespread network dysfunction in FE, thereby potentially advancing therapeutic strategies for FE.

In the field of information Retrieval (IR), Pseudo-relevance feedback (PRF) and Query Expansion (QE) techniques have garnered significant attention for their efficacy in enhancing retrieval effectiveness. However, traditional PRF approaches predominantly concentrate solely on pseudo-relevant documents identified during the initial retrieval stage, neglecting the rich semantic information embedded within non-pseudo-relevant documents. This paper introduces an innovative PRF model that integrates soft negative samples and contrastive learning to address this limitation, aiming for a more comprehensive capture and representation of semantics. First, we employ the BM25 algorithm as the baseline retrieval mechanism to accurately pinpoint pseudo-relevant documents from the first stage retrieval and assign weights to their terms. Second, a contrastive learning strategy is introduced to distill semantic features from all documents globally, further refining the semantic weights of terms. To mitigate the risk of information loss associated with soft negative samples, we ingeniously leverage the statistical properties of kernel function to precisely gauge the co-occurrence frequencies between terms, ensuring the preservation of core information and thus obtaining kernel function term co-occurrence weights. Third, we select semantically related terms highly relevant to the query for creating an optimized query by balancing these three weight distributions. Extensive empirical analyses conducted on several TREC datasets demonstrate the practical feasibility of our proposed model. It outperforms baseline models and state-of-the-art technologies on core evaluation metrics such as MAP, P@10, NDCG, and MRR. Deeper comparative experiments and case studies reveal that the expansion terms generated by our model exhibits a deeper level of semantic coherence with the original query, underscoring the dual advantages of the model in both theory and practice. In summary, the model presented herein not only opens a new path at the technical level, but also provides a more accurate and efficient solution for real-world applications in IR.

Abstract Recently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence‐to‐sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these models can improve ranking effectiveness by enhancing input semantics. This article introduces SE‐BERT, a semantically enhanced bidirectional encoder representation from transformers (BERT) based ranking framework that captures more semantic information by modifying the input text. SE‐BERT utilizes a pretrained generative language model to summarize both sides of the candidate passage and concatenate them into a new input sequence, allowing BERT to acquire more semantic information within the constraints of the input sequence's length. Experimental results from two Text Retrieval Conference datasets demonstrate that our approach's effectiveness increasing as the length of the input text increases.

Abstract The pre‐trained language model (PLM) based on the Transformer encoder, namely BERT, has achieved state‐of‐the‐art results in the field of Information Retrieval. Existing BERT‐based ranking models divide documents into passages and aggregate passage‐level relevance to rank the document list. However, these common score aggregation strategies cannot capture important semantic information such as document structure and have not been extensively studied. In this article, we propose a novel kernel‐based score pooling system to capture document‐level relevance by aggregating passage‐level relevance. In particular, we propose and study several representative kernel pooling functions and several different document ranking strategies based on passage‐level relevance. Our proposed framework KnBERT naturally incorporates kernel functions from the passage level into the BERT‐based re‐ranking method, which provides a promising avenue for building universal retrieval‐then‐rerank information retrieval systems. Experiments conducted on two widely used TREC Robust04 and GOV2 test datasets show that the KnBERT has made significant improvements over other BERT‐based ranking approaches in terms of MAP, P@20, and NDCG@20 indicators with no extra or even less computations.

Pseudo‐relevance feedback is a well‐studied query expansion technique in which it is assumed that the top‐ranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co‐occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co‐occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co‐occurrence‐based framework to enhance retrieval performance by integrating term co‐occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co‐occurrence‐based Rocchio method (KRoc) and a kernel co‐occurrence‐based RM3 method (KRM3) are proposed. In our framework, co‐occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within‐document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state‐of‐the‐art approaches.

Factors such as environmental change during space flight have the widespread influences on physical and mental health, causing aerospace medical problems like cardiovascular dysfunction. One of its manifestations of cardiovascular disorders is orthostatic intolerance. At present, these measures to protect against the physiological effects of weightlessness provide a protective effect, but still cannot completely eliminate the adverse effects of weightlessness on cardiovascular system. Therefore, it is very necessary to find a convenient and effective protective countermeasures. Clinical practice and modern experimental research have revealed that acupuncture and moxibustion has very good regulating effect on cardiovascular system. However, until now, there is no unified stimulus standard (such as stimulus frequency, time etc), which seriously affects the repeatability of operation and the efficacy of acupuncture. Therefore it's very important to set a repeatable standard.

Copper (Cu) is an important enzyme co-factor that is also extremely toxic at high intracellular concentrations, making active efflux mechanisms essential for preventing Cu accumulation. Here, we have investigated the mechanistic role of metallochaperones in regulating Cu efflux. We have constructed a computational model of Cu trafficking and efflux based on systems analysis of the Cu stress response of Halobacterium salinarum. We have validated several model predictions via assays of transcriptional dynamics and intracellular Cu levels, discovering a completely novel function for metallochaperones. We demonstrate that in addition to trafficking Cu ions, metallochaperones also function as buffers to modulate the transcriptional responsiveness and efficacy of Cu efflux. This buffering function of metallochaperones ultimately sets the upper limit for intracellular Cu levels and provides a mechanistic explanation for previously observed Cu metallochaperone mutation phenotypes.

A new shunt STATCOM based on modular multilevel converter (MMC) is researched in this paper. The circuit topology, sub-module mathematical model and working principle are analyzed in detail. A dynamic reactive current detection method based on the trigonometric function is applied to the compensation of MMC inverter. Attention is paid on the control to keep voltage stable of the capacitor with combining the regulating error signals to the CPS-SPWM waves, which has overcome the shortcoming that dc-link capacitor voltage is influenced by load interference when tracking the reactive current. The feasibility and effectiveness of the MMC-based STATCOM is validated by test results.

Because of the increasing dominance of interconnect issues in advanced IC technology, it is desirable to incorporate global routing into early design stages to get accurate interconnect information. Hence, high-quality and fast global routers are in great demand. In this paper, we propose two major techniques to improve the extremely fast global router, FastRoute (Pan and Chu, 2006) in terms of solution quality : (1) monotonic routing, (2) multi-source multi-sink maze routing. The new router is called FastRoute 2.0. Experimental results show that FastRoute 2.0 can generate high-quality routing solutions with fast runtime compared with three state-of-the-art academic global routers FastRoute, Labyrinth (Kastner et al., 2000) and Chi Dispersion router (Hadsell and Madden, 2003). On the set of benchmarks used in Pan and Chu, 2006 and Hadsell and Madden (2003), the total overflow of FastRoute 2.0 is 98, compared to 1012 (FastRoute), 2846 (Labyrinth) and 1271 (Chi Dispersion Router). The runtime of FastRoute 2.0 is 73% slower than FastRoute, but 78times and 37times faster than Labyrinth and Chi Dispersion router. The promising results make it possible to integrate global routing into early design stages. This could dramatically improve the design solution quality.

This paper presents a very efficient algorithm for performance-driven topology design for interconnects. Given a net, it first generates A-tree topology using table lookup and net-breaking. Then a performance-driven post-processing heuristic not restricting to A-tree topology improves the obtained topology by considering the sink positions, required time and load capacitance to achieve better timing. Experimental results show that our new technique can produce topologies with better timing and is hundreds of times faster than traditional approach.

In this paper, we present FastPlace 3.0 - an efficient and scalable multilevel quadratic placement algorithm for large-scale mixed-size designs. The main contributions of our work are: (1) A multilevel global placement framework, by incorporating a two-level clustering scheme within the flat analytical placer FastPlace (Viswanathan and Chu, 2005) and Viswanathan et al., 2006), (2) An efficient and improved iterative local refinement technique that can handle placement blockages and placement congestion constraints. (3) A congestion aware standard-cell legalization technique in the presence of blockages. On the ISPD-2005 placement benchmarks (Nam et al., 2005), our algorithm is 5.12times, 11.52times and 16.92times faster than mPL6, Capo10.2 and APlace2.0 respectively. In terms of wirelength, we are on average, 2% higher as compared to mPL6 and 9% and 3% better as compared to Capo10.2 and APlace2.0 respectively. We also achieve competitive results compared to a number of academic placers on the placement congestion constrained ISPD-2006 placement benchmarks (Nam, 2006).

In nanometer-scale VLSI technologies, several interconnect issues like routing congestion and interconnect delay have become the main concerns in placement. However, all previous placement approaches optimize some very primitive interconnect models during placement. These models are far from the actual interconnect implementation in the routing stage. As a result, placement solution considered to be good by primitive interconnect models may turn out to be poor after routing. In addition, the placement may not even be routable and timing closure may not be achievable.

In this paper, we present FastPlace 2.0 - an extension to the efficient analytical standard-cell placer - FastPlace, to address the mixed-mode placement problem. The main contributions of our work are: (1) Extensions to the global placement framework of FastPlace to handle mixed-mode designs. (2) An efficient and optimal minimum perturbation macro legalization algorithm that is applied after global placement to resolve overlaps among the macros. (3) An efficient legalization scheme to legalize the standard cells among the placeable segments created after fixing the movable macros. On the ISPD 02 mixed-size placement benchmarks, our algorithm is 16.8times and 7.8times faster than state-of-the-art academic placers Capo 9.1 and Feng-shui 5.0 respectively. Correspondingly, we are on average, 12% and 3% better in terms of wirelength over the respective placers

Because of the increasing dominance of interconnect issues in advanced IC technology, placement has become a critical step in the IC design flow. To get accurate interconnect information during the placement process, it is desirable to incorporate global routing into it. However, previous global routers are computationally expensive. It is impractical to perform global routing repeatedly during placement.

In the past few years, there has been a lot of research in the area of global placement. In comparison, not much attention has been paid to the detailed placement problem. Existing detailed placers either fail to improve upon the excellent solution quality enabled by good global placers or are very slow. To handle the above problems, we focus on the detailed placement problem. We present an efficient and effective detailed placement algorithm to handle the wirelength minimization problem. The main contributions of our work are: (1) an efficient Global Swap technique to identify a pair of cells that can be swapped to reduce wirelength; (2) a flow that combines the Global Swap technique with other heuristics to produce very good wirelength; (3) an efficient single-segment clustering technique to optimally shift cells within a segment to minimize wirelength. On legalized mPL5 global placements on the IBM standard-cell benchmarks, our detailed placer can achieve 19.0%, 13.2% and 0.5% more wirelength reduction compared to Fengshui5.0, rowironing and Domino respectively. Correspondingly we are 3.6/spl times/ 2.8/spl times/ and 15/spl times/ faster. On the ISPD05 benchmarks (Gi-Joon Nam et al., 2005), we achieve 8.1% and 9.1% more wirelength reduction compared to Fengshui5.0 and rowironing respectively. Correspondingly we are 3.1/spl times/ and 2.3/spl times/ faster.

Modern designs often contain a combination of a large number of standard cells and macro blocks. Traditionally large macro blocks are handled at the floorplanning level, after which their positions are fixed. The standard cells are then handled during the placement level. Current designs can have hundreds of large and medium sized macro blocks and a large number of standard cells. As a result, traditional floorplanning techniques cannot scale to this problem, both in terms of runtime and solution quality. Hence a technique is required to simultaneously handle this combination of placeable objects.In this paper, we present a combined placement and floorplanning approach for mixed-mode placement. We extend the efficient analytical placement algorithm FastPlace by integrating a simulated annealing based floorplanner to solve the global placement problem for mixed-mode designs. We also present an efficient and effective detailed placement algorithm to improve the wirelength of the global placement solution based on a greedy swapping heuristic.