Reevaluating Adversarial Examples in Natural Language

State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model and follows some linguistic constraints. We then analyze the outputs of two state-of-the-art synonym substitution attacks. We find that their perturbations often do not preserve semantics, and 38% introduce grammatical errors. Human surveys reveal that to successfully preserve semantics, we need to significantly increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.With constraints adjusted to better preserve semantics and grammaticality, the attack success rate drops by over 70 percentage points.Comment: 15 pages; 9 Tables; 5 Figure

Advancing Solar Irradiance Measurement for Climate-Related Studies: Accurate Constraint on Direct Aerosol Radiative Effect (DARE)

Earth's climate is driven primarily by solar radiation. As summarized in various IPCC reports, the global average of radiative forcing for different agents and mechanisms, such as aerosols or CO2 doubling, is in the range of a few W/sq m. However, when solar irradiance is measured by broadband radiometers, such as the fleet of Eppley Precision Solar Pyranometers (PSP) and equivalent instrumentation employed worldwide, the measurement uncertainty is larger than 2% (e.g., WMO specification of pyranometer, 2008). Thus, out of the approx. 184 W/sq m (approx.263 W/sq m if cloud-free) surface solar insolation (Trenberth et al. 2009), the measurement uncertainty is greater than +/-3.6 W/sq m, overwhelming the climate change signals. To discern these signals, less than a 1 % measurement uncertainty is required and is currently achievable only by means of a newly developed methodology employing a modified PSP-like pyranometer and an updated calibration equation to account for its thermal effects (li and Tsay, 2010). In this talk, we will show that some auxiliary measurements, such as those from a collocated pyrgeometer or air temperature sensors, can help correct historical datasets. Additionally, we will also demonstrate that a pyrheliometer is not free of the thermal effect; therefore, comparing to a high cost yet still not thermal-effect-free "direct + diffuse" approach in measuring surface solar irradiance, our new method is more economical, and more likely to be suitable for correcting a wide variety of historical datasets. Modeling simulations will be presented that a corrected solar irradiance measurement has a significant impact on aerosol forcing, and thus plays an important role in climate studies

Leading Power Accuracy in Lattice Calculations of Parton Distributions

In lattice-QCD calculations of parton distribution functions (PDFs) via large-momentum effective theory, the leading power (twist-three) correction appears as ${\cal O}(\Lambda_{\rm QCD}/P^z)$ due to the linear-divergent self-energy of Wilson line in quasi-PDF operators. For lattice data with hadron momentum $P^z$ of a few GeV, this correction is dominant in matching, as large as 30\% or more. We show how to eliminate this uncertainty through choosing the mass renormalization parameter consistently with the resummation scheme of the infrared-renormalon series in perturbative matching coefficients. An example on the lattice pion PDF data at $P^z = 1.9$ GeV shows an improvement of matching accuracy by a factor of more than $3\sim 5$ in the expansion region $x= 0.2\sim 0.5$.Comment: Updated to version published on PL

Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highest-ranked tokens in order to minimize the edit distance of the perturbation, yet change the original classification. We evaluated DeepWordBug on eight real-world text datasets, including text classification, sentiment analysis, and spam detection. We compare the result of DeepWordBug with two baselines: Random (Black-box) and Gradient (White-box). Our experimental results indicate that DeepWordBug reduces the prediction accuracy of current state-of-the-art deep-learning models, including a decrease of 68\% on average for a Word-LSTM model and 48\% on average for a Char-CNN model.Comment: This is an extended version of the 6page Workshop version appearing in 1st Deep Learning and Security Workshop colocated with IEEE S&

Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients

Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wireless sensors are developed, including: (1) a C-shape central node placed around the patient's ears, which collects electroencephalogram (EEG), detects FoG using an on-device DL model, and generates auditory alerts when FoG is detected; (2) a stretchable patch-type sensor attached to the patient's legs, which collects electromyography (EMG) and movement information from accelerometers. The patch-type sensors wirelessly send collected data to the central node through low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on flexible printed circuit boards. Adhesive gel-free acetylene carbon black and polydimethylsiloxane electrodes are fabricated on the flexible substrate to allow conformal wear over the long term. Custom integrated circuits (IC) are developed in 180 nm CMOS technology and used in both types of sensors for signal acquisition, digitization, and wireless communication. A novel lightweight DL model is trained using multi-modal sensory data. The inference of the DL model is performed on a low-power microcontroller in the central node. The DL model achieves a high detection sensitivity of 0.81 and a specificity of 0.88. The developed wearable sensors are ready for clinical experiments and hold great promise in improving the quality of life of patients with PD. The proposed design methodologies can be used in wearable medical devices for the monitoring and treatment of a wide range of neurodegenerative diseases

Suicidal ideation and attempted suicide amongst Chinese transgender persons:National population study

BACKGROUND: This study aims to understand suicidal ideation and suicide attempts among transgender individuals through an in-depth analysis of a nation-wide population general survey in China. METHODS: Transgender Men (TM) and Women (TW) were investigated through a cross-sectional survey. A structured questionnaire was used to investigate participants\u27 demographic information, perceived sexuality conflicts, childhood adversity and mental health conditions. Logistic regression models were utilized to investigate risk factors associated with suicidal ideation and suicide attempts in these groups. We also conducted a quasi-meta-analysis in order to compare the prevalence of suicidal ideation and attempted suicide between general and transgender populations in China. RESULTS: A total of 1309 participants across 32 provinces and municipalities in China took part in this survey, out of 2060 valid questionnaires. In this transgender population, the lifetime prevalence of suicidal ideation and an attempt at suicide were 56.4% and 16.1%, respectively. This estimated prevalence rate is far greater than in Chinese community samples. For all transgender people, disliking birth-assigned sex, seeking sex reassignment surgery, having intense conflicts with parents, lifetime history of suffering from major depressive disorder, a recent episode of depression, self-harm, and seeking mental health services were significantly associated with increased risk of suicidal ideation. An education level of high school or equivalent, being married and/or separated/divorced, having intense conflicts with parents, or self-harm and seeking mental health services were all significantly associated with increased risk of suicide attempt. Although most risk factors for TM and TW were equivalent across groups, differences were observed in both suicidal ideation and suicide attempt models. LIMITATIONS: The cross-sectional study design and lack of follow-up data are limitations of this study. CONCLUSIONS: This is the first study to examine suicide within a Chinese transgender population. The clinical implications of these findings for Chinese mental health professionals are discussed. Also, the evidence from this study can be used to inform the practices of suicide prevention workers, and policy makers working with the transgender population