Unified Multimodal Model with Unlikelihood Training for Visual Dialog

The task of visual dialog requires a multimodal chatbot to answer sequential questions from humans about image content. Prior work performs the standard likelihood training for answer generation on the positive instances (involving correct answers). However, the likelihood objective often leads to frequent and dull outputs and fails to exploit the useful knowledge from negative instances (involving incorrect answers). In this paper, we propose a Unified Multimodal Model with UnLikelihood Training, named UniMM-UL, to tackle this problem. First, to improve visual dialog understanding and generation by multi-task learning, our model extends ViLBERT from only supporting answer discrimination to holding both answer discrimination and answer generation seamlessly by different attention masks. Specifically, in order to make the original discriminative model compatible with answer generation, we design novel generative attention masks to implement the autoregressive Masked Language Modeling (autoregressive MLM) task. And to attenuate the adverse effects of the likelihood objective, we exploit unlikelihood training on negative instances to make the model less likely to generate incorrect answers. Then, to utilize dense annotations, we adopt different fine-tuning methods for both generating and discriminating answers, rather than just for discriminating answers as in the prior work. Finally, on the VisDial dataset, our model achieves the best generative results (69.23 NDCG score). And our model also yields comparable discriminative results with the state-of-the-art in both single-model and ensemble settings (75.92 and 76.17 NDCG scores).Comment: Accepted by the 30th ACM International Conference on Multimedia (ACM MM 2022

The Inhibitory Role of B7-H4 in Antitumor Immunity: Association with Cancer Progression and Survival

B7-H4 is one of the most recently identified members of B7 superfamily of costimulatory molecules serving as an inhibitory modulator of T-cell response. B7-H4 is broadly expressed in human peripheral tissues and inducibly expressed in immune cells. The expression of B7-H4 has been observed in various types of human cancer tissues, and its soluble form has been detected in blood samples from cancer patients. However, its precise physiological role is still elusive, as its receptor has not been identified and the expression levels are not consistent. This paper summarizes the pertinent data on the inhibitory role of B7-H4 in antitumor immunity and its association with cancer progression and survival in human patients. The paper also discusses the clinical significance of investigating B7-H4 as potential markers for cancer diagnosis and prognosis, and as therapeutic targets

Observation of the out-of-plane orbital antidamping-like torque

The out-of-plane antidamping-like orbital torque fosters great hope for high-efficiency spintronic devices. Here we report experimentally the observation of out-of-plane antidamping-like torque that could be generated by z-polarized orbital current in ferromagnetic-metal/oxidized Cu bilayers, which is presented unambiguously by the magnetic field angle dependence of spin-torque ferromagnetic resonance signal. The oxidized Cu thickness dependence of orbital torque ratios highlights the interfacial effect would be responsible for the generation of orbital current. Besides that, the oxidized Cu thickness dependence of damping parameter further proves the observation of antidamping-like torque. This result contributes to enriching the orbital-related theory of the generation mechanism of the orbital torque

Schema theory based data engineering in gene expression programming for big data analytics

Gene expression programming (GEP) is a data driven evolutionary technique that well suits for correlation mining. Parallel GEPs are proposed to speed up the evolution process using a cluster of computers or a computer with multiple CPU cores. However, the generation structure of chromosomes and the size of input data are two issues that tend to be neglected when speeding up GEP in evolution. To fill the research gap, this paper proposes three guiding principles to elaborate the computation nature of GEP in evolution based on an analysis of GEP schema theory. As a result, a novel data engineered GEP is developed which follows closely the generation structure of chromosomes in parallelization and considers the input data size in segmentation. Experimental results on two data sets with complementary features show that the data engineered GEP speeds up the evolution process significantly without loss of accuracy in data correlation mining. Based on the experimental tests, a computation model of the data engineered GEP is further developed to demonstrate its high scalability in dealing with potential big data using a large number of CPU cores

CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model

Large language models (LLMs) have demonstrated great potential in the financial domain. Thus, it becomes important to assess the performance of LLMs in the financial tasks. In this work, we introduce CFBenchmark, to evaluate the performance of LLMs for Chinese financial assistant. The basic version of CFBenchmark is designed to evaluate the basic ability in Chinese financial text processing from three aspects~(\emph{i.e.} recognition, classification, and generation) including eight tasks, and includes financial texts ranging in length from 50 to over 1,800 characters. We conduct experiments on several LLMs available in the literature with CFBenchmark-Basic, and the experimental results indicate that while some LLMs show outstanding performance in specific tasks, overall, there is still significant room for improvement in basic tasks of financial text processing with existing models. In the future, we plan to explore the advanced version of CFBenchmark, aiming to further explore the extensive capabilities of language models in more profound dimensions as a financial assistant in Chinese. Our codes are released at https://github.com/TongjiFinLab/CFBenchmark.Comment: 12 pages, 4 figure

Giant efficiency of long-range orbital torque in Co/Nb bilayers

We report unambiguously experimental evidence of a strong orbital current in Nb films with weak spin-orbit coupling via the spin-torque ferromagnetic resonance (ST-FMR) spectrum for Fe/Nb and Co/Nb bilayers. The sign change of the damping-like torque in Co/Nb demonstrates a large spin-orbit correlation and thus great efficiency of orbital torque in Co/Nb. By studying the efficiency as a function of the thickness of Nb sublayer, we reveal a long orbital diffusion length (~3.1 nm) of Nb. Further planar Hall resistance (PHE) measurements at positive and negative applying current confirm the nonlocal orbital transport in ferromagnetic-metal/Nb heterostructures

An adaptive multilevel indexing method for disaster service discovery

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods