Revisiting the Knowledge Injection Frameworks

In recent years, large language models (LLMs), such as GPTs, have attained great impact worldwide. However, how to adapt these LLMs to better suit the vertical domain-specific tasks by utilizing external knowledge remains not completely solved. Indeed, there have emerged a few works on this line where most of them rely on an alignment heuristic that is built to inject the corresponding knowledge tuple into the associated text sample. However, despite the promise, we identify a pivotal problem in this work ubiquitously. Simply put, we find that injecting unaligned (i.e., random) knowledge tuple into the LLMs achieves comparable (and sometimes better) results than the aligned knowledge being injected. We therefore take a thorough investigation of this frustrating finding on a variety of related prior work and further provide a chain of potential interpretations for the phenomenon. Based on all that, we offer a simple remediated technique. Briefly, the core of this technique is rooted in an ideological emphasis on the pruning and purification of the external knowledge base to be injected into LLMs. At last, we show that by integrating this technique into most (if not all) knowledge injection frameworks and recent LLMs, it manages to overcome the aforementioned sanity problem and further pushes the boundary of the performance of the domain-adaptive LLMs.Comment: 9 pages, 6 figures, accepted by EMNLP 2023 Mai

A Distributed Game Theoretic Approach for Blockchain-based Offloading Strategy

Keeping patients’ sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge Computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium

Functional connectivity of the anterior cingulate cortex in depression and in health

The first voxel-level resting-state functional connectivity (FC) neuroimaging analysis of depression of the anterior cingulate cortex (ACC) showed in 282 patients with major depressive disorder compared with 254 controls, some higher, and some lower FCs. However, in 125 unmedicated patients, primarily increases of FC were found: of the subcallosal anterior cingulate with the lateral orbitofrontal cortex, of the pregenual/supracallosal anterior cingulate with the medial orbitofrontal cortex, and of parts of the anterior cingulate with the inferior frontal gyrus, superior parietal lobule, and with early cortical visual areas. In the 157 medicated patients, these and other FCs were lower than in the unmedicated group. Parcellation was performed based on the FC of individual ACC voxels in healthy controls. A pregenual subdivision had high FC with medial orbitofrontal cortex areas, and a supracallosal subdivision had high FC with lateral orbitofrontal cortex and inferior frontal gyrus. The high FC in depression between the lateral orbitofrontal cortex and the subcallosal parts of the ACC provides a mechanism for more non-reward information transmission to the ACC, contributing to depression. The high FC between the medial orbitofrontal cortex and supracallosal ACC in depression may also contribute to depressive symptoms

The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression

Objective: The male preponderance in prevalence of autism is among the most pronounced sex ratios across neurodevelopmental conditions. The authors sought to elucidate the relationship between autism and typical sex-differential neuroanatomy, cognition, and related gene expression. Methods: Using a novel deep learning framework trained to predict biological sex based on T1-weighted structural brain images, the authors compared sex prediction model performance across neurotypical and autistic males and females. Multiple large-scale data sets comprising T1-weighted MRI data were employed at four stages of the analysis pipeline: 1) pretraining, with the UK Biobank sample (>10,000 individuals); 2) transfer learning and validation, with the ABIDE data sets (1,412 individuals, 5–56 years of age); 3) test and discovery, with the EU-AIMS/AIMS-2-TRIALS LEAP data set (681 individuals, 6–30 years of age); and 4) specificity, with the NeuroIMAGE and ADHD200 data sets (887 individuals, 7–26 years of age). Results: Across both ABIDE and LEAP, features positively predictive of neurotypical males were on average significantly more predictive of autistic males (ABIDE: Cohen’s d=0.48; LEAP: Cohen’s d=1.34). Features positively predictive of neurotypical females were on average significantly less predictive of autistic females (ABIDE: Cohen’s d=1.25; LEAP: Cohen’s d=1.29). These differences in sex prediction accuracy in autism were not observed in individuals with ADHD. In autistic females, the male-shifted neurophenotype was further associated with poorer social sensitivity and emotional face processing while also associated with gene expression patterns of midgestational cell types. Conclusions: The results demonstrate an increased resemblance in both autistic male and female individuals’ neuroanatomy with male-characteristic patterns associated with typically sex-differential social cognitive features and related gene expression patterns. The findings hold promise for future research aimed at refining the quest for biological mechanisms underpinning the etiology of autism

Extension Limit of a Straight-Swirling Mixed Jet Bit and Its Influential Factors in Radial Jet Drilling

Radial jet drilling (RJD) is applied to low-permeability and unconventional natural gas. A self-propelled bit chiefly affects the borehole length and drilling efficiency. Herein, for a straight-swirling mixed jet (SSMJ) bit, a prediction model of the drilling extension limit LEL was established by analysing the forces and pressure loss of RJD system. LEL of a specific project was calculated and the pressure loss and forces distribution were obtained. Additionally, the influence laws of the main factors on LEL were studied using the established model. The results indicated that the high-pressure hose contributes the main system pressure loss, and the recoil force of backward nozzles is the sole driving force of RJD. The recoil force of forward nozzle and friction between the hose and borehole constitute the main resistances. Increasing the pump pressure pb, backward nozzle diameter db, and jet diffusion angle θx and reducing forward nozzle diameter df, impeller central hole diameter dm, and backward nozzle inclination angle θb are conducive to improving LEL. However, larger pb and db increase the hydraulic consumption, besides larger θx or smaller df, and dm will reduce the rock-breaking capability. From a sensitivity analysis, pb and dm have the maximum and minimum influence on LEL, respectively. Finally, prediction value LEL of an oil well in Eastern Sichuan can be up to 63.7 m. The results provide a guidance for the hydraulic parameters and key component selection, additionally bit structure optimization of RJD

Effect of System Pressure on the Surface Microstructure of Spherical Porous Basic Magnesium Carbonate

spherical porous basic magnesium carbonate (Mg5(CO3)4(OH)2.4H2O) has been prepared by using ammonium bicarbonate and magnesium chloride hexahydrate as raw materials. A novel CO2 bubble template was constructed to explain the formation of pore structure of porous Mg5(CO3)4(OH)2.4H2O. System pressure was found to be important in controlling the final pore size of spherical porous Mg5(CO3)4(OH)2.4H2O. By the investigation of the particles from pressure-dependent experiments, scanning electron microscopy observations, X-ray diffraction, and Fourier transform infrared spectra were used to follow the reactions and identify the products. The results show that the pore size of spherical porous Mg5(CO3)4(OH)2.4H2O gradually decreased via increasing system pressure

Effect of System Pressure on the Surface Microstructure of Spherical Porous Basic Magnesium Carbonate

spherical porous basic magnesium carbonate (Mg5(CO3)4(OH)2.4H2O) has been prepared by using ammonium bicarbonate and magnesium chloride hexahydrate as raw materials. A novel CO2 bubble template was constructed to explain the formation of pore structure of porous Mg5(CO3)4(OH)2.4H2O. System pressure was found to be important in controlling the final pore size of spherical porous Mg5(CO3)4(OH)2.4H2O. By the investigation of the particles from pressure-dependent experiments, scanning electron microscopy observations, X-ray diffraction, and Fourier transform infrared spectra were used to follow the reactions and identify the products. The results show that the pore size of spherical porous Mg5(CO3)4(OH)2.4H2O gradually decreased via increasing system pressure