Towards Stable Backdoor Purification through Feature Shift Tuning

It has been widely observed that deep neural networks (DNN) are vulnerable to backdoor attacks where attackers could manipulate the model behavior maliciously by tampering with a small set of training samples. Although a line of defense methods is proposed to mitigate this threat, they either require complicated modifications to the training process or heavily rely on the specific model architecture, which makes them hard to deploy into real-world applications. Therefore, in this paper, we instead start with fine-tuning, one of the most common and easy-to-deploy backdoor defenses, through comprehensive evaluations against diverse attack scenarios. Observations made through initial experiments show that in contrast to the promising defensive results on high poisoning rates, vanilla tuning methods completely fail at low poisoning rate scenarios. Our analysis shows that with the low poisoning rate, the entanglement between backdoor and clean features undermines the effect of tuning-based defenses. Therefore, it is necessary to disentangle the backdoor and clean features in order to improve backdoor purification. To address this, we introduce Feature Shift Tuning (FST), a method for tuning-based backdoor purification. Specifically, FST encourages feature shifts by actively deviating the classifier weights from the originally compromised weights. Extensive experiments demonstrate that our FST provides consistently stable performance under different attack settings. Without complex parameter adjustments, FST also achieves much lower tuning costs, only 10 epochs. Our codes are available at https://github.com/AISafety-HKUST/stable_backdoor_purification.Comment: NeurIPS 2023 paper. The first two authors contributed equall

Tag-based annotation creates better avatars

Avatar creation from human images allows users to customize their digital figures in different styles. Existing rendering systems like Bitmoji, MetaHuman, and Google Cartoonset provide expressive rendering systems that serve as excellent design tools for users. However, twenty-plus parameters, some including hundreds of options, must be tuned to achieve ideal results. Thus it is challenging for users to create the perfect avatar. A machine learning model could be trained to predict avatars from images, however the annotators who label pairwise training data have the same difficulty as users, causing high label noise. In addition, each new rendering system or version update requires thousands of new training pairs. In this paper, we propose a Tag-based annotation method for avatar creation. Compared to direct annotation of labels, the proposed method: produces higher annotator agreements, causes machine learning to generates more consistent predictions, and only requires a marginal cost to add new rendering systems.Comment: 15 pages, 7 figures, 4 table

Digitisation of Weather Records of Seungjeongwon Ilgi: A Historical Weather Dynamics Dataset of the Korean Peninsula (1623-1910)

Introduction This study has exploited the daily weather records of Seungjeongwon Ilgi from the NIKH database (http://sjw.history.go.kr/main.do). Seungjeongwon Ilgi is a daily record of the Seungjeongwon, the Royal Secretariat of the Joseon Dynasty of Korea. These diaries span from 1623 to 1910 and generally involve daily weather records in the entry header. Their observational site would be located in Seoul (N37°35′, E126°59′). We have exploited the weather records from the NIKH database and classified the daily weather using text mining method. We have also converted the report dates from the traditional lunisolar calendar to the Gregorian calendar, to better contextualise our data into the contemporary daily measurements. Data We provide different formats (csv, xlsx, json) to facilitate the usage of data. The main contents of data are listed as below. ID: The unique identifier of a specific record in the metadata, which can also serve as the identifier to merge with external data in the NIKH digital database. Traditional calendar: The original lunar dates in the NIKH digital database, which are listed in data format "YYYY-MM-DD". More specifically, "L0" implies the leap year and "L1" implies the common year. Leap: The identifier of a leap year. Gregorian calendar: The Gregorian calendar date that converted by the traditional calendar date. Weather Text: The text that describe the weather conditions. Specifically, multiple weather descriptions of the same day have been put together. Flag: The computed value that indicates different combinations of weather conditions. Volume: The volume of text in the original record. Herbal Volume: The volume of text in the herbal record. Sunny: A dummy variable that represents whether the weather description contains the expression of sunny. Cloudy: A dummy variable that represents whether the weather description contains the expression of cloudy. Rainy: A dummy variable that represents whether the weather description contains the expression of rainy. Snow: A dummy variable that represents whether the weather description contains the expression of snow. Wind: A dummy variable that represents whether the weather description contains the expression of wind. Import Data # Python # CSV file import pandas as pd data=pd.read_csv('~/SJWilgi_Seoul_Weather_YR1623_1910.csv',encoding="utf-8") # JSON file data=pd.read_json('~/SJWilgi_Seoul_Weather_YR1623_1910.json',encoding="utf-8") # Excel file data=pd.read_excel('~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx') # Excel file # R # CSV file library(readr) data<- read_csv("~/SJWilgi_Seoul_Weather_YR1623_1910.csv") # Excel file library(readxl) data <- read_excel("~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx"

Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page

A Novel Hybrid Battery Thermal Management System for Prevention of Thermal Runaway Propagation

Lithium-ion batteries (LiBs) are extensively used in electric vehicles (EVs) because of their high energy density and long service life. Designing a battery thermal management system (BTMS) that prevents thermal runaway (TR) propagation in the event of abusive accidents is crucial. The goal of this study is to design a novel hybrid BTMS with both active liquid cooling (LC) and passive cooling for preventing TR propagation in the battery module. A numerical model for a battery module (16 cylindrical 18 650 cells) was developed in COMSOL multiphysics software to examine the TR propagation caused by a single cell. Copper foam and expanded graphite-paraffin (EG-PCM) composite material were used for passive cooling. In addition to the TR scenario, the thermal behaviors of the battery module with the hybrid BTMS were evaluated under the 3C discharging and driving cycle circumstances. A conventional BTMS with natural air cooling is chosen as the baseline. The findings reveal that the proposed hybrid BTMS, which uses EG-PCM with a melting temperature of 52 °C and thermal diffusivity of 9.68 mm2/s and copper foam with a porosity of 0.7–0.9, is capable of limiting the maximum cell temperature below the thermal safety threshold (80 °C) to prevent TR propagation. Under the New European Driving Cycle (NEDC) load cycle, the battery module can be maintained within an optimal working temperature range by passive cooling only. By applying active LC with a flow rate of 0.3 m/s for BTMS, the average temperature reduction of the battery module at a 3C discharging rate can be up to 72.5% and 52.7% compared to passive cooling with copper foam and EG-PCM, respectively. The study highlights that the combination of active LC and appropriate passive cooling is an efficient thermal management solution for Li-ion battery applications in EVs, notably in the consideration of thermal safety

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments. The study shows that pFL methods with partial model-sharing can significantly boost robustness against backdoor attacks. In contrast, pFL methods with full model-sharing do not show robustness. To analyze the reasons for varying robustness performances, we provide comprehensive ablation studies on different pFL methods. Based on our findings, we further propose a lightweight defense method, Simple-Tuning, which empirically improves defense performance against backdoor attacks. We believe that our work could provide both guidance for pFL application in terms of its robustness and offer valuable insights to design more robust FL methods in the future. We open-source our code to establish the first benchmark for black-box backdoor attacks in pFL: https://github.com/alibaba/FederatedScope/tree/backdoor-bench.Comment: KDD 202

Spiking NeRF: Making Bio-inspired Neural Networks See through the Real World

Spiking neuron networks (SNNs) have been thriving on numerous tasks to leverage their promising energy efficiency and exploit their potentialities as biologically plausible intelligence. Meanwhile, the Neural Radiance Fields (NeRF) render high-quality 3D scenes with massive energy consumption, and few works delve into the energy-saving solution with a bio-inspired approach. In this paper, we propose spiking NeRF (SpikingNeRF), which aligns the radiance ray with the temporal dimension of SNN, to naturally accommodate the SNN to the reconstruction of Radiance Fields. Thus, the computation turns into a spike-based, multiplication-free manner, reducing the energy consumption. In SpikingNeRF, each sampled point on the ray is matched onto a particular time step, and represented in a hybrid manner where the voxel grids are maintained as well. Based on the voxel grids, sampled points are determined whether to be masked for better training and inference. However, this operation also incurs irregular temporal length. We propose the temporal condensing-and-padding (TCP) strategy to tackle the masked samples to maintain regular temporal length, i.e., regular tensors, for hardware-friendly computation. Extensive experiments on a variety of datasets demonstrate that our method reduces the $76.74\%$ energy consumption on average and obtains comparable synthesis quality with the ANN baseline

Experimental study of battery passive thermal management system using copper foam-based phase change materials

Lithium-ion batteries (LiBs) have been widely applied in electric vehicles (EVs) and energy storage devices. The battery thermal management system (BTMS) critically impacts the safety and degradation of LiBs. Phase change material (PCM) is a promising passive BTMS solution owing to its high latent heat and non-parasitic power consumption requirements. In this paper, paraffin (PA) as the PCM was embedded in the copper foam to enhance the heat dissipation of the cooling material. The thermal responses of the battery module were comparatively investigated under different thermal management solutions, including natural air, pure PCM, and copper foam-PCM. A battery module consisting of 16 thermal dummy cells (TDC) was designed, built, and calibrated to replace real commercial 21,700 NMC battery cells. The findings indicate that the proposed copper foam-PCM solution effectively enhances heat dissipation and improves the temperature uniformity of the battery module. For instance, in the condition of intensive operation (60% depth of discharge and 3C discharge), copper foam-PCM composite material reduces the maximum temperature rise from 57.4 °C to 51.4 °C (-10.4%) compared to pure PCM. At ambient temperatures of 25 °C and 35 °C, the temperature inhomogeneity of the battery module with copper foam-PCM is maintained within 5 °C and 2 °C, respectively. Besides, the effect of copper foam-PCM cooling on the cell-to-pack conversion efficiency was evaluated. The gravimetric cell-to-pack ratio (GCTP) and volumetric cell-to-pack ratio (VCTP) of the battery pack employing the proposed BTMS reached 53.1% and 45.6%, respectively

Interdecadal variation of precipitation over Yunnan, China in summer and its possible causes

In recent decades, severe drought conditions have become increasingly frequent in Yunnan, Southwest China. The extreme drought events cause huge losses to agricultural economy, ecological security and human health. To uncover the reasons behind the worsening drought conditions, this study investigates the interdecadal variability (IDV) of summer precipitation in Yunnan during 1961–2019 and its association with the Indo-Pacific Sea surface temperature (SST) configuration based on gauge observation and reanalysis data. The dominant mode of summer precipitation IDV in Yunnan shows a uniform pattern characterizing the alternations of flood and drought. Specifically, a relatively wet period persists from the early 1990s to the early 2000s, followed by a relatively dry period from the early 2000s to the late 2010s. The IDV of precipitation is consistent with the IDV of the column-integrated water vapor flux divergence, where the wind anomalies play a major role in modulating the moisture supply. The main SST forcings of the IDV of precipitation include the sea surface temperature anomalies (SSTAs) over the Bay of Bengal (BOB), the Western Pacific Warm Pool (WPWP), and the western North Pacific (WNP). The negative SSTAs over the BOB and the WPWP trigger a Gill-Matsuno-type response that enhances the cyclonic curvature over Yunnan. The SSTAs over the WNP show a tripole pattern that weakens the WNP subtropical high and further enhances the cyclonic anomaly over Yunnan. The above SST configuration also favors moisture transport to Yunnan. Numerical experiments verify the key physical processes