Model Debiasing via Gradient-based Explanation on Representation

Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these approaches may suffer from incomplete disentanglement and overlook proxy attributes (proxies for sensitive attributes) when processing real-world data, especially for unstructured data, causing performance degradation in fairness and loss of useful information for downstream tasks. In this paper, we propose a novel fairness framework that performs debiasing with regard to both sensitive attributes and proxy attributes, which boosts the prediction performance of downstream task models without complete disentanglement. The main idea is to, first, leverage gradient-based explanation to find two model focuses, 1) one focus for predicting sensitive attributes and 2) the other focus for predicting downstream task labels, and second, use them to perturb the latent code that guides the training of downstream task models towards fairness and utility goals. We show empirically that our framework works with both disentangled and non-disentangled representation learning methods and achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches

Transscleral cyclophotocoagulation followed by cataract surgery:a novel protocol to treat refractory acute primary angle closure

Background: To introduce a novel protocol to treat refractory acute primary angle closure (APAC): transscleral cyclophotocoagulation (TCP) followed by cataract surgery. Methods: Thirteen APAC eyes (13 patients) were enrolled in this prospective case series as study group. All patients underwent emergency TCP (20 pulses of 2000 mW during 2000 ms applied to the inferior quadrant) followed by scheduled cataract surgery. They were compared to 13 age- and gender-matched patients treated with emergency phacotrabeculectomy. We recorded intraocular pressure (IOP), best corrected visual acuity (BCVA), and complications, and several ultrasound biomicroscopy (UBM) parameters before and after TCP. Results: In the study group, IOP decreased from 51.5 +/- 7.0 mmHg (mean +/- standard deviation) before TCP to 16.4 +/- 5.4 mmHg 1 day after TCP (P <0.001). At 6 months, there was no significant difference in IOP between the study group (14.0 +/- 3.4 mmHg) and control group (16.7 +/- 4.3 mmHg;P = 0.090); IOP lowering medications were used by 0/13 in the study group and 2/13 patients in the control group (P = 0.48). At 6 months, there was no significant difference in BCVA between the study group and the control group (20/25 (20/200 to 20/25) and 20/30 (20/50 to 20/25), respectively;P = 1.0). The UBM parameters anterior chamber depth (P = 0.016), angle-opening distance at 500 mu m (P = 0.011), and maximum ciliary body thickness (P <0.001) increased significantly while the iris-ciliary process distance decreased significantly (P = 0.020) after TCP. Conclusions: TCP effectively lowers IOP and modifies the anterior chamber morphology in APAC; TCP followed by cataract surgery can be considered an alternative to treat refractory APAC but needs further evaluation

A Causal Framework to Unify Common Domain Generalization Approaches

Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem in machine learning and has attracted much attention in recent years. A large number of approaches have been proposed. Different approaches are motivated from different perspectives, making it difficult to gain an overall understanding of the area. In this paper, we propose a causal framework for domain generalization and present an understanding of common DG approaches in the framework. Our work sheds new lights on the following questions: (1) What are the key ideas behind each DG method? (2) Why is it expected to improve generalization to new domains theoretically? (3) How are different DG methods related to each other and what are relative advantages and limitations? By providing a unified perspective on DG, we hope to help researchers better understand the underlying principles and develop more effective approaches for this critical problem in machine learning

STP Models of Optimal Differential and Linear Trail for S-box Based Ciphers

Automatic tools have played an important role in designing new cryptographic primitives and evaluating the security of ciphers. Simple Theorem Prover constraint solver (STP) has been used to search for differential/linear trails of ciphers. This paper proposes general STP-based models searching for differential and linear trails with the optimal probability and correlation for S-box based ciphers. In order to get trails with the best probability or correlation for ciphers with arbitrary S-box, we give an efficient algorithm to describe probability or correlation of S-Box. Based on the algorithm we present a search model for optimal differential and linear trails, which is efficient for ciphers with S-Boxes whose DDTs/LATs contain entities not equal to the power of two. Meanwhile, the STP-based model for single-key impossible differentials considering key schedule is proposed, which traces the propagation of values from plaintext to ciphertext instead of propagations of differences. And we found that there is no 5-round AES-128 single-key truncated impossible differential considering key schedule, where input and output differences have only one active byte respectively. Finally, our proposed models are utilized to search for trails of bit-wise ciphers GIFT-128, DES, DESL and ICEBERG and word-wise ciphers ARIA, SM4 and SKINNY-128. As a result, improved results are presented in terms of the number of rounds or probabilities/correlations

Dynamic changes in the thylakoid proteome of cyanobacteria during light-regulated thylakoid membrane development

Cyanobacteria were among the oldest organisms to undertake oxygenic photosynthesis and have an essential impact on the atmosphere and carbon/nitrogen cycles on the planet. The thylakoid membrane of cyanobacteria represents an intricate compartment that houses a variety of multi-component (pigment–)protein complexes, assembly factors, and regulators, as well as transporters involved in photosynthetic light reactions, and respiratory electron transport. How these protein components are incorporated into membranes during thylakoid formation and how individual complexes are regulated to construct the functional machinery remains elusive. Here, we carried out an in-depth statistical analysis of the thylakoid proteome data obtained during light-induced thylakoid membrane biogenesis in the model cyanobacterium Synechococcus elongatus PCC 7942. A total of 1581 proteins were experimentally quantified, among which 457 proteins demonstrated statistically significant variations in abundance at distinct thylakoid biogenesis stages. Gene Ontology and KEGG enrichment analysis revealed that predominantly photosystems, light-harvesting antennae, ABC transporters, and pathway enzymes involved in oxidative stress responses and protein folding exhibited notable alternations in abundance between high light and growth light. Moreover, through cluster analysis the 1581 proteins were categorized into six distinct clusters that have significantly different trajectories of the change in their abundance during thylakoid development. Our study provides insights into the physiological regulation for the membrane integration of protein components and functionally linked complexes during the cyanobacterial TM biogenesis process. The findings and analytical methodologies developed in this study may be valuable for studying the global responses of TM biogenesis and photosynthetic acclimation in plants and algae

Synthetic engineering of a new biocatalyst encapsulating [NiFe]-hydrogenases for enhanced hydrogen production

Hydrogenases are microbial metalloenzymes capable of catalyzing the reversible interconversion between molecular hydrogen and protons with high efficiency, and have great potential in the development of new electrocatalysts for renewable...</jats:p

Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases

Genome-wide association studies (GWASs) have identified hundreds of susceptibility genes, including shared associations across clinically distinct autoimmune diseases. We performed an inverse χ(2) meta-analysis across ten pediatric-age-of-onset autoimmune diseases (pAIDs) in a case-control study including more than 6,035 cases and 10,718 shared population-based controls. We identified 27 genome-wide significant loci associated with one or more pAIDs, mapping to in silico-replicated autoimmune-associated genes (including IL2RA) and new candidate loci with established immunoregulatory functions such as ADGRL2, TENM3, ANKRD30A, ADCY7 and CD40LG. The pAID-associated single-nucleotide polymorphisms (SNPs) were functionally enriched for deoxyribonuclease (DNase)-hypersensitivity sites, expression quantitative trait loci (eQTLs), microRNA (miRNA)-binding sites and coding variants. We also identified biologically correlated, pAID-associated candidate gene sets on the basis of immune cell expression profiling and found evidence of genetic sharing. Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases

Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting

In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting