Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction

Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pretrained Language Models. In this paper, we observe that the optimized prompts, including discrete prompts and continuous prompts, exhibit undesirable object bias. To handle this problem, we propose a novel prompt tuning method called MeCoD. consisting of three modules: Prompt Encoder, Object Equalization and Biased Object Obstruction. Experimental results show that MeCoD can significantly reduce the object bias and at the same time improve accuracy of factual knowledge extraction

An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucination, which may lead to harmful consequences. Therefore, evaluating MLLMs' hallucinations is becoming increasingly important in model improvement and practical application deployment. Previous works are limited in high evaluation costs (e.g., relying on humans or advanced LLMs) and insufficient evaluation dimensions (e.g., types of hallucination and task). In this paper, we propose an LLM-free multi-dimensional benchmark AMBER, which can be used to evaluate both generative task and discriminative task including object existence, object attribute and object relation hallucination. Based on AMBER, we design a low-cost and efficient evaluation pipeline. Additionally, we conduct a comprehensive evaluation and detailed analysis of mainstream MLLMs including GPT-4V(ision), and also give guideline suggestions for mitigating hallucinations. The data and code of AMBER are available at https://github.com/junyangwang0410/AMBER.Comment: 11 pages, 4 figure

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers

The present study was conducted to clarify the differences in the multi-element, volatile organic compound, fatty acid, and metabolite fingerprints between black and white sesame seeds. A total of 53 chemical elements, 32 volatile flavor compounds, 40 fatty acids, and 283 metabolites were identified and evaluated in the two groups of sesame seeds. Univariate and multivariate statistics indicated a distinct separation between the two groups of sesame seeds. A panel of 16 chemical elements, 3 volatile compounds, 8 individual fatty acids, and 54 metabolites with p value < 0.05 and variable importance in projection score > 1 were selected as the most important discriminants for the two types of sesame seeds. Overall, these data reveal the influence of genotype on the chemical composition of sesame seeds. Our findings also demonstrate that the hybrid model of instrumental analysis and chemometrics is feasible for the discrimination of black and white sesame seeds

Subgrouping breast cancer patients based on immune evasion mechanisms unravels a high involvement of transforming growth factor-beta and decoy receptor 3.

In the era of immunotherapy and personalized medicine, there is an urgent need for advancing the knowledge of immune evasion in different cancer types and identifying reliable biomarkers that guide both therapy selection and patient inclusion in clinical trials. Given the differential immune responses and evasion mechanisms in breast cancer, we expect to identify different breast cancer groups based on their expression of immune-related genes. For that, we used the sequential biclustering method on The Cancer Genome Atlas RNA-seq breast cancer data and identified 7 clusters. We found that 77.4% of the clustered tumor specimens evade through transforming growth factor-beta (TGF-β) immunosuppression, 57.7% through decoy receptor 3 (DcR3) counterattack, 48.0% through cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and 34.3% through programmed cell death-1 (PD-1). TGF-β and DcR3 are potential novel drug targets for breast cancer immunotherapy. Targeting TGF-β and DcR3 may provide a powerful approach for treating breast cancer because 57.7% of patients overexpressed these two molecules. Furthermore, triple-negative breast cancer (TNBC) patients clustered equally into two subgroups: one with impaired antigen presentation and another with high leukocyte recruitment but four different evasion mechanisms. Thus, different TNBC patients may be treated with different immunotherapy approaches. We identified biomarkers to cluster patients into subgroups based on immune evasion mechanisms and guide the choice of immunotherapy. These findings provide a better understanding of patients' response to immunotherapies and shed light on the rational design of novel combination therapies

Preparation and Properties of Organically Modified Na-Montmorillonite

This study investigates the montmorillonite (MMT) content, rotational viscosity, and colloidal index of sodium montmorillonite (Na-MMT) as a function of the sodium agent dosage, reaction time, reaction temperature, and stirring time. Na-MMT was modified using different octadecyl trimethyl ammonium chloride (OTAC) dosages under optimal sodification conditions. The organically modified MMT products were characterized via infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy. The results show that the Na-MMT with good properties (i.e., the maximum rotational viscosity and highest Na-MMT content with no decrease in the colloid index) was obtained at a 2.8% sodium carbonate dosage (measured based on the MMT mass), a temperature of 25 °C, and a reaction time of two hours. Upon organic modification of the optimized Na-MMT, OTAC entered the NA-MMT interlayer, and the contact angle was increased from 20.0° to 61.4°, the layer spacing was increased from 1.58 to 2.47 nm, and the thermal stability was conspicuously increased. Thus, MMT and Na-MMT were modified by the OTAC modifier

Regional Control of Hairless versus Hair-Bearing Skin by Dkk2

Haired skin is a defining characteristic of mammals. However, some specialized skin regions, such as human palms, soles and ventral wrist, and mouse plantar foot, are entirely hairless. Using mouse plantar skin as a model system, we show that the endogenous secreted Wnt inhibitor DKK2 suppresses plantar hair follicle development and permits the formation of hairless skin. Plantar skin retains all of the mechanistic components needed for hair follicle development, as genetic deletion of Dkk2 permits formation of fully functional plantar hair follicles that give rise to external hair, contain sebaceous glands and a stem cell compartment, and undergo regenerative growth. In the absence of Dkk2, Wnt/β-catenin signaling activity is initially broadly elevated in embryonic plantar skin and gradually becomes patterned, mimicking follicular development in normally haired areas. These data provide a paradigm in which regionally restricted expression of a Wnt inhibitor underlies specification of hairless versus hairy skin