Image Segmentation Based on Intuitionistic Type-2 FCM Algorithm

Due to using the fuzzy clustering algorithm, the accuracy of image segmentation is not high enough. So one hybrid clustering algorithm combined with intuitionistic fuzzy factor and local spatial information is proposed. Experimental results show that the proposed algorithm is superior to other methods in image segmentation accuracy and improves the robustness of the algorithm

Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference

A popular approach to streaming speech translation is to employ a single offline model with a \textit{wait-$k$} policy to support different latency requirements, which is simpler than training multiple online models with different latency constraints. However, there is a mismatch problem in using a model trained with complete utterances for streaming inference with partial input. We demonstrate that speech representations extracted at the end of a streaming input are significantly different from those extracted from a complete utterance. To address this issue, we propose a new approach called Future-Aware Streaming Translation (FAST) that adapts an offline ST model for streaming input. FAST includes a Future-Aware Inference (FAI) strategy that incorporates future context through a trainable masked embedding, and a Future-Aware Distillation (FAD) framework that transfers future context from an approximation of full speech to streaming input. Our experiments on the MuST-C EnDe, EnEs, and EnFr benchmarks show that FAST achieves better trade-offs between translation quality and latency than strong baselines. Extensive analyses suggest that our methods effectively alleviate the aforementioned mismatch problem between offline training and online inference.Comment: work in progres

Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyrics translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of augmented data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.Comment: 13 page

Prevalence and Associated Factors of Elder Mistreatment in a Rural Community in People's Republic of China: A Cross-Sectional Study

Background: Current knowledge about elder mistreatment is mainly derived from studies done in Western countries, which indicate that this problem is related to risk factors such as a shared living situation, social isolation, disease burden, and caregiver strain. We know little about prevalence and risk factors for elder mistreatment and mistreatment subtypes in rural China where the elder population is the most vulnerable. Methods: In 2010, we conducted a cross-sectional survey among older adults aged 60 or older in three rural communities in Macheng, a city in Hubei province, China. Of 2245 people initially identified, 2039 were available for interview and this was completed in 2000. A structured questionnaire was used to collect data regarding mistreatment and covariates. Logistic regression analysis was used to identify factors related to elder mistreatment and subtypes of mistreatment. Results: Elder mistreatment was reported by 36.2 % (95 % CI: 34.1%–38.3%) of the participants. Prevalence rates of psychological mistreatment, caregiver neglect, physical mistreatment, and financial mistreatment were 27.3 % (95 % CI

Blind anti-collision methods for RFID system: a comparative analysis

Radio Frequency Identification (RFID) is one of the critical technologies of the Internet of Things (IoT). With the rapid development of IoT and the extensive use of RFID in our life, the step of RFID development should be faster. However, the tags in an RFID system are more and more utilized, both of them communicate in the same channel. The signal the reader received is mixed, and the reader cannot get the correct message the tags send directly. This phenomenon is often called a collision, which is the main obstacle to the development of the RFID system. Traditionally, the algorithm to solve the collision problem is called the anti-collision algorithm, the widely used anti-collision algorithm is based on Time Division Multiple Access (TDMA) like ALOHA-based and Binary search-based anti-collision algorithm. The principle of the TDMA-based anti-collision algorithm is to narrow the response of tags to one in each query time. These avoidance anti-collision algorithms performance poor when the number of tags is huge, thus, some researchers proposed the Blind Source Separation (BSS)-based anti-collision algorithm. The blind anti-collision algorithms perform better than the TDMA-based algorithms; it is meaningful to do some more research about this filed. This paper uses several BSS algorithms like FastICA, PowerICA, ICA_p, and SNR_MAX to separate the mixed signals in the RFID system and compare the performance of them. Simulation results and analysis demonstrate that the ICA_p algorithm has the best comprehensive performance among the mentioned algorithms. The FastICA algorithm is very unstable, and has a lower separation success rate, and the SNR_MAX algorithm has the worst performance among the algorithms applied in the RFID system. Some advice for future work will be put up in the end

BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing

The emergence of large language models (LLMs) has sparked significant interest in extending their remarkable language capabilities to speech. However, modality alignment between speech and text still remains an open problem. Current solutions can be categorized into two strategies. One is a cascaded approach where outputs (tokens or states) of a separately trained speech recognition system are used as inputs for LLMs, which limits their potential in modeling alignment between speech and text. The other is an end-to-end approach that relies on speech instruction data, which is very difficult to collect in large quantities. In this paper, we address these issues and propose the BLSP approach that Bootstraps Language-Speech Pre-training via behavior alignment of continuation writing. We achieve this by learning a lightweight modality adapter between a frozen speech encoder and an LLM, ensuring that the LLM exhibits the same generation behavior regardless of the modality of input: a speech segment or its transcript. The training process can be divided into two steps. The first step prompts an LLM to generate texts with speech transcripts as prefixes, obtaining text continuations. In the second step, these continuations are used as supervised signals to train the modality adapter in an end-to-end manner. We demonstrate that this straightforward process can extend the capabilities of LLMs to speech, enabling speech recognition, speech translation, spoken language understanding, and speech conversation, even in zero-shot cross-lingual scenarios

Modulate Molecular Interaction between Hole Extraction Polymers and Lead Ions toward Hysteresis-Free and Efficient Perovskite Solar Cells

Herein three polymeric hole extraction materials (HEMs), poly(benzene‐dithiophene) (PB2T)‐O, PB2T‐S, and PB2T‐SO are presented for p–i–n perovskite solar cells (PVSCs). This study reveals that the perovskite device hysteresis and performance heavily rely on the perovskite grain boundary conditions. More specifically, they are predetermined through the molecular interaction between Lewis base atoms of HEMs and perovskites. It is revealed that only changing the side chain terminals (-OCH_3, -SCH_3, and –SOCH_3) of HEMs results in effective modulating PVSC performance and hysteresis, due to the effective tune of interaction strength between HEM and perovskite. With an in situ grown perovskite‐HEM bulk heterojunction structure, PB2T‐O with weak binding group (-OCH_3, −78.9 kcal mol^(−1) bonding energy) to lead ions allows delivering hysteresis‐free and efficient devices, which is sharp contrast to the strong binding PB2T‐SO (−119.3 kcal mol^(−1) bonding energy). Overall, this work provides new insights on PVSC hysteresis and the related curing methods via multifunctional HEM design in PVSCs

Eight RGS and RGS-like Proteins Orchestrate Growth, Differentiation, and Pathogenicity of Magnaporthe oryzae

A previous study identified MoRgs1 as an RGS protein that negative regulates G-protein signaling to control developmental processes such as conidiation and appressorium formation in Magnaporthe oryzae. Here, we characterized additional seven RGS and RGS-like proteins (MoRgs2 through MoRgs8). We found that MoRgs1 and MoRgs4 positively regulate surface hydrophobicity, conidiation, and mating. Indifference to MoRgs1, MoRgs4 has a role in regulating laccase and peroxidase activities. MoRgs1, MoRgs2, MoRgs3, MoRgs4, MoRgs6, and MoRgs7 are important for germ tube growth and appressorium formation. Interestingly, MoRgs7 and MoRgs8 exhibit a unique domain structure in which the RGS domain is linked to a seven-transmembrane motif, a hallmark of G-protein coupled receptors (GPCRs). We have also shown that MoRgs1 regulates mating through negative regulation of Gα MoMagB and is involved in the maintenance of cell wall integrity. While all proteins appear to be involved in the control of intracellular cAMP levels, only MoRgs1, MoRgs3, MoRgs4, and MoRgs7 are required for full virulence. Taking together, in addition to MoRgs1 functions as a prominent RGS protein in M. oryzae, MoRgs4 and other RGS and RGS-like proteins are also involved in a complex process governing asexual/sexual development, appressorium formation, and pathogenicity

Seawater nutrient and chlorophyll α distributions near the Great Wall Station, Antarctica

We examined the influences upon nutrient, temperature, salinity and chlorophyll a distributions in Great Wall Cove (GWC) and Ardley Cove (AC), near the Chinese Antarctic Great Wall Station, using measurements taken in January 2013 and other recent data. Nutrient concentrations were high, with phosphate concentrations of 1.94 (GWC) and 1.96 (AC) μmol·L−1, DIN(dissolved inorganic nitrogen) concentrations of 26.36 (GWC) and 25.94 (AC) μmol·L−1 and silicate concentrations of 78.6 (GWC) and 79.3 (AC) μmol·L−1. However, average concentrations of chlorophyll a were low (1.29 μg·L−1, GWC and 1.08 μg·L−1, AC), indicating that this region is a high-nutrient and low-chlorophyll (HNLC) area. Nutrient concentrations of freshwater (stream and snowmelt) discharge into GWC and AC in the austral summer are low, meaning freshwater discharge dilutes the nutrient concentrations in the two coves. Strong intrusion of nutrient-rich water from the Bransfield Current in the south was the main source of nutrients in GWC and AC. Low water temperature and strong wind-induced turbulence and instability in the upper layers of the water column were the two main factors that caused the low phytoplankton biomass during the austral summer