When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks

In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes crucial. In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks. The performance of ICL on these tasks mostly cannot reach half of the state-of-the-art results. To explore the reasons behind this failure, we conduct comprehensive experiments on 18 specification-heavy tasks with various LLMs and identify three primary reasons: inability to specifically understand context, misalignment in task schema comprehension with humans, and inadequate long-text understanding ability. Furthermore, we demonstrate that through fine-tuning, LLMs can achieve decent performance on these tasks, indicating that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback of existing alignment methods that renders LLMs incapable of handling complicated specification-heavy tasks via ICL. To substantiate this, we perform dedicated instruction tuning on LLMs for these tasks and observe a notable improvement. We hope the analyses in this paper could facilitate advancements in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie

An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance

Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning results for fusion. In the offline stage, measurements of the Bluetooth and Wi-Fi received signal strength (RSS) were collected to construct Bluetooth and Wi-Fi fingerprint databases, respectively. Then, fingerprint positioning error prediction models were built with GPR and data from the fingerprint databases. In the online stage, online Bluetooth and Wi-Fi RSS readings were matched with the fingerprint databases to get a Bluetooth fingerprint positioning result (BFPR) and a Wi-Fi fingerprint positioning result (WFPR). Then, with the help of RD and fingerprint positioning error prediction models, whether the positioning results are trusted was determined. The trusted result is selected as the position estimation result when there is only one trusted positioning result among the BFPR and WFPR. The mean is chosen as the position estimation result when both the BFPR and WFPR results are trusted or untrusted. Experimental results showed that the proposed method was better than BFP and WFP, with a mean positioning error of 2.06 m and a root-mean-square error of 1.449 m

Research on HAR-Based Floor Positioning

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs). Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures. For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance. In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone built-in sensor data to classify pedestrian activities. We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis. Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position. A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities. The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality. It is more stable than methods based on wireless signals. Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs. In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions

Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration

WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m

An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods

Electromagnetic Vibration Energy Harvester with Tunable Resonance Frequency Based on Stress Modulation of Flexible Springs

This paper presents a compact electromagnetic vibrational energy harvester (EVEH) with tunable resonance frequency. The resonance frequency of the EVEH is tuned by adjusting the axial stress in the flexible polymeric springs, which is realized by physically pulling and pushing the springs. The stress tuning functionality is realized with a compact structure with small volume. The total frequency tuning range of the proposed EVEH is 56 Hz (74–130 Hz), which is 64% of the natural resonance frequency of the EVEH (88 Hz). It is found that the tensile stress increases the resonance frequency of the EVEH, while the compressive stress firstly reduces the resonance frequency and then increases the resonance frequency due to buckling

Coupling coal pyrolysis with char gasification in a multi-stage fluidized bed to co-produce high-quality tar and syngas

A multi-stage fluidized bed (MSFB) by configuring the distributor with an overflow standpipe between its neighboring stages was developed to couple the powder coal pyrolysis with its resultant char gasification for co-production of tar and syngas. This work succeeded in the smooth operation of MSFB for coal staged conversion. The three modes of coupling pyrolysis and gasification in terms of the one-stage, two-stage and three-stage bed characterized by temperature drop from the bottom up were investigated to evaluate the quality of the liquid and gas products. Coupling low- and mid-temperature tandem coal pyrolysis with high-temperature char gasification in the MSFB improved the quality of tar and syngas. The obtained tar yield was over 80% of the Gray King assay tar yield and its light tar fraction (boiling point &lt; 360 degrees C) was as high as 70-80% in the MSFB. Syngas with CH4 content of 5.2 vol.% was produced that was suitable for SNG production. Inside the reactor, the flow direction of pyrolysis volatiles toward the temperature drop avoided the deep secondary reaction of tar. Syngas and steam from the bottom gasification section could contribute to the formation of light tar and CH4 by affecting the top coal pyrolysis. A comparison with the typical pyrolysis processes suggested that the MSFB process had its own advantages in treating powder coal to produce the high-quality tar and syngas.</p

Molecular Mechanism of HSF1-Upregulated ALDH2 by PKC in Ameliorating Pressure Overload-Induced Heart Failure in Mice

Evidences abound that HSF1 and ALDH2 are of cardioprotective effect, yet there is still no report on whether HSF1 can regulate ALDH2 to delay the occurrence of heart failure. We first established the pressure overload-induced heart failure model of mice by transverse aortic constriction (TAC) and discovered that, in the forming period of heart failure, changes of HSF1 and ALDH2 expression recorded the consistent trend. When HSF1 was upregulated/downregulated to delay/promote the occurrence of heart failure, PKC and ALDH2 also showed increased/decreased expression. And when ALDH2 was upregulated/downregulated, the role of HSF1 in delaying the occurrence of heart failure strengthened/weakened. Next, we used mechanical stretch to establish a pressure-stimulated myocardial hypertrophy model and discovered an increased expression of both HSF1 and ALDH2. When HSF1 was upregulated/downregulated to increase/decrease the expression of myocardial hypertrophy gene beta-MHC, PKC and ALDH2 recorded an increased/decreased expression. When an inhibitor was used to downregulate the expression of PKC in cardiomyocytes, we found that the role of HSF1 in upregulating ALDH2 beta-MHC weakened. These findings suggest that HSF1 can upregulate the expression of ALDH2 via PKC to promote pressure-stimulated myocardial compensatory hypertrophy, which is an important molecular pathway for HSF1 to ameliorate heart failure