WindGP: Efficient Graph Partitioning on Heterogenous Machines

Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large graphs. However, existing works fail to balance the computation cost and communication cost on machines with different power (including computing capability, network bandwidth and memory size), as they only consider replication factor and neglect the difference of machines in realistic data centers. In this paper, we propose a general graph partitioning algorithm WindGP, which can support fast and high-quality edge partitioning on heterogeneous machines. WindGP designs novel preprocessing techniques to simplify the metric and balance the computation cost according to the characteristics of graphs and machines. Also, best-first search is proposed instead of BFS and DFS, in order to generate clusters with high cohesion. Furthermore, WindGP adaptively tunes the partition results by sophisticated local search methods. Extensive experiments show that WindGP outperforms all state-of-the-art partition methods by 1.35 - 27 times on both dense and sparse distributed graph algorithms, and has good scalability with graph size and machine number.Comment: 19 pages, 15 figures, 18 table

Construction of a eukaryotic expression vector for pEGFP-FST and its biological activity in duck myoblasts

Background: Follistatin (FST), a secreted glycoprotein, is intrinsically linked to muscle hypertrophy. To explore the function of duck FST in myoblast proliferation and differentiation, the pEGFP-FST eukaryotic expression vector was constructed and identified. The biological activities of this vector were analyzed by transfecting pEGFP-FST into cultured duck myoblasts using Lipofectamine\u2122 2000 and subsequently determining the mRNA expression profiles of FST and myostatin (MSTN). Results: The duck pEGFP-FST vector was successfully constructed and was confirmed to have high liposome-mediated transfection efficiency in duck myoblasts. Additionally, myoblasts transfected with pEGFP-FST had a higher biological activity. Significantly, the overexpression of FST in these cells significantly inhibited the mRNA expression of MSTN (a target gene that is negatively regulated by FST). Conclusions: The duck pEGFP-FST vector has been constructed successfully and exhibits biological activity by promoting myoblast proliferation and differentiation in vitro

BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data

We introduce a text-to-speech (TTS) model called BASE TTS, which stands for $\textbf{B}$ig $\textbf{A}$daptive $\textbf{S}$treamable TTS with $\textbf{E}$mergent abilities. BASE TTS is the largest TTS model to-date, trained on 100K hours of public domain speech data, achieving a new state-of-the-art in speech naturalness. It deploys a 1-billion-parameter autoregressive Transformer that converts raw texts into discrete codes ("speechcodes") followed by a convolution-based decoder which converts these speechcodes into waveforms in an incremental, streamable manner. Further, our speechcodes are built using a novel speech tokenization technique that features speaker ID disentanglement and compression with byte-pair encoding. Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences. We design and share a specialized dataset to measure these emergent abilities for text-to-speech. We showcase state-of-the-art naturalness of BASE TTS by evaluating against baselines that include publicly available large-scale text-to-speech systems: YourTTS, Bark and TortoiseTTS. Audio samples generated by the model can be heard at https://amazon-ltts-paper.com/.Comment: v1.1 (fixed typos

An Ultra-Small Area and High-Sensitivity Wireless Receiver for ISM and MICS Band Application

In this work, a 0.43mm2 high-sensitivity low-intermediate-frequency (low-IF) receiver under $0.18~\mu \text{m}$ technology is reported for Industrial Scientific Medical (ISM) and Medical Implant Communications Service (MICS) band applications, which supports the 2ASK/GFSK demodulation mode. To reduce the area, a low noise amplifier (LNA) with an active inductor, a compact Gm-C filter, an AC current bleeding technique for controlling the receiver gain and, a ring-VCO LO PLL were used, without any passive inductors. The main methods for improving sensitivity are reducing the receiver noise figure (NF) and improving the signal-to-noise ratio for demodulation. Thus, the LNA adopts a two-stage 40 dB gain to suppress the NF of the subsequent stage. An automatic gain control (AGC) loop is used to control the receiver gain to overcome the large signal nonlinearity from the large LNA gains. Additionally, a Gm-C complex filter rejects image and blocks interference, improving the sensitivity to harsh environments. Under the CSMC $0.18~\mu \text{m}$ process, the die of the receiver is only 0.43 mm2 and covers 300–500 MHz, MICS and some ISM bands. The measurement results show that when the internal 2ASK demodulator is adopted, it has a −115 dBm sensitivity at 2 Kbps; and when the external GFSK digital baseband is adopted, it has a −121 dBm sensitivity at 2 Kbps. At 300 Kbps, only 6.5 mW of power is consumed. It is suitable for low-power wide-area network (LPWAN) applications

Air traffic controllers' mental fatigue recognition: a multi-sensor information fusion-based deep learning approach

With the growing density of air passenger traffic, accurately recognizing the level of mental fatigue (MF) experienced by air traffic controllers (ATCOs) is crucial for developing intelligent ATCOs' mental state monitoring systems, which can achieve a more effective and safer human–machine cooperative pattern. However, the existing methods for recognizing ATCOs' MF face significant challenges due to pattern variations between ATCOs and sensor artifacts. This study introduces a framework for ATCOs' MF recognition, utilizing a deep neural network called RecMF, which incorporates multi-sensor information fusion to enhance the performance of MF detection. Specifically, the RecMF employs an attention-enabled CNN-LSTM architecture that simultaneously captures time-series feature representations of electroencephalogram (EEG) signals and eye movements. To validate the effectiveness of RecMF, a fatigue-inducing experiment is conducted involving 28 subjects who are tasked with performing a series of air traffic control (ATC) tasks. The model's performance is evaluated across various time horizons and typical cognitive tasks to gain insights into its capabilities. The evaluation results indicate that the proposed model outperforms other existing methods, thereby confirming its feasibility and effectiveness. Additionally, the effects of MF on ATCOs' cognitive performance are analyzed using analysis of variance (ANOVA). The results reveal that higher levels of MF significantly reduce ATCOs' reaction speed and operational accuracy

An 8-Gbps, Low-Jitter, Four-Channel Transmitter with a Fractional-Spaced Feed-Forward Equalizer

An 8 gigabits per second (Gbps), low-jitter, four-channel transmitter with fractional-spaced feed-forward equalizer (FFE) is designed to meet the demand for broad transmission bandwidth in serial data communications. A novel frequency divider chain (FDC) architecture is developed, to satisfy the time requirements for high-speed data serialization. Moreover, a reconfigurable output driver circuit is employed to ensure compatibility with different protocols. In addition, a three-tap fractional-spaced FFE, which can enhance signal bandwidth significantly, is proposed, to compensate for channel loss. The transmitter was simulated and validated based on the Semiconductor Manufacturing International Corporation (SMIC) 55-nm process. The post-layout simulation results show the following: The tuning range of the phase-locked loop (PLL) can cover 1.6 to 4.6 GHz. At an output frequency of 4 GHz, the root-mean-square jitter (RJ) of the PLL after integration from phase noise was 1.93 ps. With an 8 Gbps output data rate, using the pseudo-random binary sequence (PRBS)-31 as a data source to simulate the whole transmitter, the power consumption values of the PLL and drive circuit were 27.0 and 29.2 mW, respectively, and the eye width and the valid eye height of output data were 0.76 unit interval (UI) and 0.68

An 8-Gbps, Low-Jitter, Four-Channel Transmitter with a Fractional-Spaced Feed-Forward Equalizer

An 8 gigabits per second (Gbps), low-jitter, four-channel transmitter with fractional-spaced feed-forward equalizer (FFE) is designed to meet the demand for broad transmission bandwidth in serial data communications. A novel frequency divider chain (FDC) architecture is developed, to satisfy the time requirements for high-speed data serialization. Moreover, a reconfigurable output driver circuit is employed to ensure compatibility with different protocols. In addition, a three-tap fractional-spaced FFE, which can enhance signal bandwidth significantly, is proposed, to compensate for channel loss. The transmitter was simulated and validated based on the Semiconductor Manufacturing International Corporation (SMIC) 55-nm process. The post-layout simulation results show the following: The tuning range of the phase-locked loop (PLL) can cover 1.6 to 4.6 GHz. At an output frequency of 4 GHz, the root-mean-square jitter (RJ) of the PLL after integration from phase noise was 1.93 ps. With an 8 Gbps output data rate, using the pseudo-random binary sequence (PRBS)-31 as a data source to simulate the whole transmitter, the power consumption values of the PLL and drive circuit were 27.0 and 29.2 mW, respectively, and the eye width and the valid eye height of output data were 0.76 unit interval (UI) and 0.68

GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique provides a new remote sensing method that shows great potential for soil moisture detection and vegetation growth, as well as for climate research, water cycle management, and ecological environment monitoring. Considering that the land surface is always covered by vegetation, it is essential to take into account the impacts of vegetation growth when detecting soil moisture (SM). In this paper, based on the GNSS-IR technique, the SM was retrieved from multi-GNSS and multi-frequency data using a machine learning model, accounting for the impact of the vegetation moisture content (VMC). Both the signal-to-noise ratio (SNR) data that was used to retrieve SM and the multipath data that was used to eliminate the vegetation influence were collected from a standard geodetic GNSS station located in Nanjing, China. The normalized microwave reflectance index (NMRI) calculated by multipath data was mapped to a normalized difference vegetation index (NDVI), which was derived from Sentinel-2 data on the Google Earth Engine platform to estimate and eliminate the influence of VMC. Based on the characteristic parameters of amplitude and phase extracted from detrended SNR signals and NDVI derived from multipath data, three machine learning methods, including random forest (RF), multiple linear regression (MLR), and multivariate adaptive regression spline (MARS), were employed for data fusion. The results show that the vegetation effect can be well eliminated using the NMRI method. Comparing MLR and MARS, RF is more suitable for GNSS-IR SM inversion. Furthermore, the SM reversed from amplitude and phase fusion is better than only those from either amplitude fusion or phase fusion. The results prove the feasibility of the proposed method based on a multipath approach to characterize the vegetation effect, as well as the RF model to fuse multi-GNSS and multi-frequency data to retrieve SM with vegetation error-correcting