Ternary Compression for Communication-Efficient Federated Learning

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. A convergence proof of the quantization factor and the unbiasedness of FTTQ is given. In addition, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate the effectiveness of FTTQ and T-FedAvg compared with the canonical federated learning algorithms in reducing communication costs and maintaining the learning performance

PSNet : fast data structuring for hierarchical deep learning on point cloud

In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learning models. Due to the disorder nature of the points in a point cloud, the significant time cost may be consumed when grouping and subsampling the points, which consequently results in poor scalability. This paper proposes a fast data structuring method called PSNet (Point Structuring Net). PSNet transforms the spatial features of the points and matches them to the features of local areas in a point cloud. PSNet achieves grouping and sampling at the same time while the existing methods process sampling and grouping in two separate steps (such as using FPS plus kNN). PSNet performs feature transformation pointwise while the existing methods uses the spatial relationship among the points as the reference for grouping. Thanks to these features, PSNet has two important advantages: 1) the grouping and sampling results obtained by PSNet is stable and permutation invariant; and 2) PSNet can be easily parallelized. PSNet can replace the data structuring methods in the mainstream point cloud deep learning models in a plug-and-play manner. We have conducted extensive experiments. The results show that PSNet can improve the training and reasoning speed significantly while maintaining the model accuracy

Incorporating persuasion into a decision support system: The case of the water user classification function

Utility stakeholders often view autonomic feedback systems as valuable tools for moderating consumption of household resources (e.g. electricity). However, to be successful, such technology must be not only informative but also persuasive. This paper presents the water user classification (WUC) function of a decision support system (DSS) for residential water consumers. This function has been designed to harness personal value systems and wider social norms in order to promote water conservation. It uses data on home appliance efficiency, routine water usage and environmental values to attribute DSS users with a water user identity. Where the attributed identity is at odds with a self-defined 'green' identity, users may be prompted to reevaluate their everyday practices. The function also offers 'smart' personalized water saving advice. In these ways, it aims to encourage consumers to adopt sustainable water saving behaviors. This paper describes the design of the WUC function and its contribution to the DSS. It additionally highlights the crucial role of behavior change theory in the delivery of successful technology-based interventions

The Nonsmooth Vibration of a Relative Rotation System with Backlash and Dry Friction

We investigate a relative rotation system with backlash and dry friction. Firstly, the corresponding nonsmooth characters are discussed by the differential inclusion theory, and the analytic conditions for stick and nonstick motions are developed to understand the motion switching mechanism. Based on such analytic conditions of motion switching, the influence of the maximal static friction torque and the driving torque on the stick motion is studied. Moreover, the sliding time bifurcation diagrams, duty cycle figures, time history diagrams, and the K-function time history diagram are also presented, which confirm the analytic results. The methodology presented in this paper can be applied to predictions of motions in nonsmooth dynamical systems

High-Speed Low-Power Viterbi Decoder Design for TCM Decoders

High-speed, low-power design of Viterbi decoders for trellis coded modulation (TCM) systems is presented in this paper. It is well known that the Viterbi decoder (VD) is the dominant module determining the overall power consumption of TCM decoders. We propose a pre-computation architecture incorporated with T-algorithm for VD, which can effectively reduce the power consumption without degrading the decoding speed much. A general solution to derive the optimal pre-computation steps is also given in the paper. Implementation result of a VD for a rate-3/4 convolutional code used in a TCM system shows that compared with the full trellis VD, the pre-computation architecture reduces the power consumption by as much as 70% without performance loss, while the degradation in clock speed is negligible.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE and can be found at: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6257480. ©2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Keywords: Trellis coded modulation (TCM), Viterbi decoder, VLS

Physics-data-driven intelligent optimization for large-scale meta-devices

Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices.Comment: manuscripts:19 pages, 4 figures; Supplementary Information: 11 pages, 12 figure

Seismic behavior of steel-reinforced high-strength concrete composite beams with bonded tendons

To study the seismic behavior of steel-reinforced high-strength concrete composite beams with bonded tendons (PSRHSCBs) under low cyclic loading, 13 PSRHSCBs were designed with the main parameters of the span-to-height ratio of beams (L/H), the cubic strength of concrete (fcu), the eccentricity of H-shaped steel (eH), the volume stirrup ratio (pv), the longitudinal reinforcement ratio (p), and the tension control stress of tendons(σcon). Using the simplified bilinear constitutive model of steel and the nonlinear constitutive model of high-strength concrete, and introducing plastic damage of concrete, fine finite element models were established with ABAQUS software. 11 similar test specimens were conducted by above modeling method, by comparing existing test curves and numerical simulation curves, both of them match well, which verified the validity of the modeling method. Subsequently, parameter analysis for 13 PSRHSCB specimens was performed, and the influence regularity of different parameters on the seismic behavior of this kind of composite beams was obtained. The results show that hysteretic curves of this kind of composite beams are full, and the failure mode is manifested as bending failure. The ultimate load, the energy dissipation capacity and the ductility coefficient of specimens can be improved significantly by increasing pv, on the contrary, the energy dissipation capacity and the ductility coefficient decrease gradually by increasing fcu. The stiffness degradation of specimens significantly slows down with the increasing of L/H and σcon, and the decreasing of eH. Finally, the trilinear skeleton curve model and the restoring force model are established by statistical regression, and the corresponding seismic design suggestions are given, and these can provide theoretical support for the seismic design of such composite beams in actual engineering

Screening chemical modulators of benzoic acid derivatives to improve lipid accumulation in Schizochytrium limacinum SR21 with metabolomics analysis.

Background(#br) Schizochytrium sp. is a marine fungus with great potential as an alternative commercial source of lipids rich in polyunsaturated fatty acids (PUFAs). To further increase lipid accumulation in Schizochytrium sp., the effect of exogenous additives has become one of the hotspots of current research. Although benzoic acid derivatives showed positive effects on lipid accumulation in Schizochytrium , the biochemical mechanism needs further investigation.(#br)Results(#br)Four benzoic acid derivatives (sodium benzoate, p -aminobenzoic acid, p -methyl benzoic acid and folic acid) were screened and evaluated for their effect on lipid accumulation in Schizochytrium limacinum SR21. The lipid yield was increased by 56.84% with p -aminobenzoic acid ( p -ABA) at a concentration of 200 mg/L among the four tested chemical modulators. The metabolomics analysis showed that 200 mg/L p -ABA was optimal for promoting glucose catabolism in glycolysis with an increase in the mevalonate pathway and a weakening of the tricarboxylic acid (TCA) cycle. Moreover, p -ABA increased NADPH generation by enhancing the pentose phosphate pathway (PPP), ultimately redirecting the metabolic flux to lipid synthesis. Fed-batch fermentation further proved that p -ABA could significantly increase the yield of lipid by 30.01%, reaching 99.67 g/L, and the lipid content was increased by 35.03%, reaching 71.12%. More importantly, the yields of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were increased by 33.28% and 42.0%, respectively.(#br)Conclusion(#br)The addition of p -ABA could promote the synthesis of tetrahydrofolate, enhancing NADPH, which ultimately promoted the flow of carbon flux to lipid synthesis. These findings provide a valuable strategy for improving the lipid accumulation in Schizochytrium by additives

Efficacy and safety comparison of esketamine-propofol with nalbuphine-propofol for upper gastrointestinal endoscopy in children: a multi-center randomized controlled trial

Background and AimsAnesthetics such as propofol, esketamine and nalbuphine are used during the upper gastrointestinal endoscopy to achieve and maintain the desired sedation level. The aim of the study was to evaluate the effectiveness and safety of propofol-nalbuphine and propofol-esketamine in children.MethodsA multi-centered study was performed at three tertiary class-A hospitals. Children between 3 and 12 years old undergoing diagnostic painless upper gastrointestinal endoscopy were included and randomly divided into esketamine or nalbuphine group to estimate the primary outcome of successful endoscope insertion. The patients were given esketamine 0.5 mg/kg and propofol 2 mg/kg intravenously in esketamine group, with nalbuphine 0.2 mg/kg and propofol 2 mg/kg in the nalbuphine group. The primary outcome was success rate for the first attempt of endoscope insertion in each group. Secondary outcomes included the safety of both anesthesia regimens and gastroenterologist's satisfaction. We used the Face, Leg, Activity, Cry and Consolability (FLACC) scale to evaluate the level of pain before and during the procedure and the Pediatric Anesthesia Emergence Delirium (PAED) scale to assess the level of agitation and delirium after awakening from anesthesia.ResultsAmong 246 patients, 200 were randomly included in the final intention-to-treat analysis, with 100 patients in each group. The success rate for the first attempt of endoscope insertion in the esketamine group was higher than the nalbuphine group (97% vs. 66%; P < 0.01). The heart rate and mean arterial pressure after intraoperative administration in the esketamine group were higher than those in the nalbuphine group, while the delirium incidence during awakening was higher in esketamine group (all P < 0.05).ConclusionThe success rate for the first attempt of endoscope insertion of children undergoing upper gastrointestinal endoscopy in the esketamine group was higher than the nalbuphine group, propofol-related hemodynamic changes were reduced accordingly, while the incidence of esketamine-related adverse effects could be high.Clinical Trial RegistrationChinese Clinical Trial Registry: ChiCTR2000040500

The synthesis of Co 3 O 4 /C composite with aloe juice as the carbon aerogel substrate for asymmetric supercapacitors

Abstract(#br)Transition metal oxide (TMO)-based 3D hollow nanocomposites with a high loading capacity, large specific surface area, and good dispersity have gained considerable attention for energy-related applications. The controllable fabrication of TMO-based hollow nanostructures with adequate mass transfer channels through a simple and eco-friendly method is highly desirable. Herein, 3D net-like Co 3 O 4 /C composites were solvothermally fabricated and calcined using aloe juice as a novel carbon substrate. The as-prepared material showed a specific capacitance of 1345.2 F g −1 at 1 A g −1 and excellent charge–discharge stability with capacitance retention of 92.7% after 10000 cycles. Moreover, an asymmetric supercapacitor composed of an ALC-700 positive electrode and an active-carbon negative electrode exhibited a high energy density of 68.17 Wh⋅kg −1 at 549 W kg −1 and excellent cycling performance. We report the first use of carbonized aloe juice as a carbon matrix to obtain a 3D hierarchical porous structure for energy devices. The proposed method of aerogel can be generalized to utilize the juices of other fruits, sugarcane, and various plants