Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait Abnormalities and Aberrant D2 Receptor Expression and Signaling in Mice Carrying the Human Pathogenic Mutation DRD2I212F

A dopamine D2 receptor mutation was recently identified in a family with a novel hyperkinetic movement disorder. That allelic variant D2-I212F is a constitutively active and G protein–biased receptor. We now describe mice engineered using CRISPR-Cas9–mediated gene editing technology to carry the D2-I212F variant. Drd2I212F mice exhibited gait abnormalities resembling those in other mouse models of chorea and/or dystonia and had striatal D2 receptor expression that was decreased approximately 30% per Drd2I212F allele. Electrically evoked inhibitory postsynaptic conductances in midbrain dopamine neurons and striatum from Drd2I212F mice, caused by G protein activation of potassium channels, exhibited slow kinetics (e.g., approximately four- to sixfold slower decay) compared with Drd2+/+ mice. Current decay initiated by photolytic release of the D2 antagonist sulpiride from CyHQ-sulpiride was also ∼fourfold slower in midbrain slices from Drd2I212F mice than Drd2+/+ mice. Furthermore, in contrast to Drd2+/+ mice, in which dopamine is several-fold more potent at neurons in the nucleus accumbens than in the dorsal striatum, reflecting activation of Gαo versus Gαi, dopamine had similar potencies in those two brain regions of Drd2I212F mice. Repeated cocaine treatment, which decreases dopamine potency in the nucleus accumbens of Drd2+/+ mice, had no effect on dopamine potency in Drd2I212F mice. The results demonstrate the pathogenicity of the D2-I212F mutation and the utility of this mouse model for investigating the role of pathogenic DRD2 variants in early-onset hyperkinetic movement disorders. SIGNIFICANCE STATEMENT The first dopamine receptor mutation to cause a movement disorder, D2-I212F, was recently identified. The mutation makes receptor activation of G protein–mediated signaling more efficient. To confirm the pathogenesis of D2-I212F, this study reports that mice carrying this mutation have gait abnormalities consistent with the clinical phenotype. The mutation also profoundly alters D2 receptor expression and function in vivo. This mouse model will be useful for further characterization of the mutant receptor and for evaluation of potential therapeutic drugs.
The damping of ocean waves by surface films: A new look at an old problem

A new theory is presented explaining why not only short surface ripples, but also longer ocean surface waves are damped by oil films floating on the sea surface. The wave attenuation by viscoelastic surface films is attributed to the Marangoni effect, which causes a strong resonance-type wave damping in the short-gravity-wave region, and to nonlinear wave-wave interaction, by means of which wave energy is transferred from the longer waves to the energy sink in the Marangoni resonance region. A viscoelastic surface film changes the free surface boundary condition in the tangential direction and thus strongly modifies the flow pattern in the boundary layer. As a consequence, wave energy is dissipated by enhanced viscous damping in the short-gravity-wave region due to large velocity gradients induced in the viscous boundary layer. Estimates of the influence of surface films on nonlinear transfer rates are given. Data on wave damping obtained in laboratory and field experiments by previous investigators are discussed in the light of the proposed theory. It is found that this theory is capable of explaining the observed strong wave damping by viscoelastic surface films. The theory predicts that in the equilibrium range of the spectrum, but outside the Marangoni resonance region, wave damping increases with wave number k as k3/2 and increases quadratically with wind speed (up to the limit where the film is “washed down”). The higher the elasticity of the surface film, the stronger is the wave damping.
Glutamine promotes the proliferation of epithelial cells via mTOR/S6 pathway in oral lichen planus.

BACKGROUND Although abnormal cell proliferation and apoptosis are associated with the pathogenesis of OLP, the exactly mechanism of which is not yet known. It has been reported that glutamine (Gln) can promote cell proliferation and inhibit apoptosis of various tumor cells. This study aims to evaluate the effect of Gln metabolism on the balance of proliferation and apoptosis in epithelial cells of OLP. METHODS 30 Human OLP specimens and 11 normal controls were stained by immunohistochemistry (IHC) to detect the levels of proliferation and Gln metabolism related proteins. Then, the critical role of Gln in cell proliferation and apoptosis was determined by Gln deprivation or treatment with glutaminase inhibitor (CB-839) to intervene Gln metabolism in human gingival epithelial cells (HGECs). Cell proliferation was detected using CCK8, p-mTOR and p-S6 proteins were detected using Western Blot, cell apoptosis and cell cycle were detected using flow cytometry, and cell stress was detected using immunofluorescence. RESULTS Compared with normal controls, OLP specimens showed higher levels of Ki-67 and Gln metabolism-related proteins, including Gln transporter (ASCT2), glutaminase (GLS), and pathway proteins (p-mTOR and p-S6). In vitro, Gln promoted cell proliferation and simultaneously upregulated the activity of mTOR/S6 pathway. Moreover, rapamycin, an mTOR pathway inhibitor, could effectively block the Gln-induced cell proliferation. MHY1485, an mTOR pathway agonist, could effectively reverse the decline of cell proliferation under Gln deprivation. In addition, inhibiting Gln metabolism caused the accumulation of intracellular ROS and induced cell apoptosis. However, N-acetylcysteine (NAC) reversed this state and then decreased cell apoptosis by eliminating intracellular ROS. CONCLUSION Gln metabolism is essential to maintain the balance of proliferation and apoptosis in oral epithelial cells, and inhibition of Gln metabolism may have a beneficial effect on OLP treatment.
Interplay of built-in potential and piezoelectric field on carrier recombination in green light emitting InGaN quantum wells

The authors measure the interplay of piezoelectric field and built-in potential on carrier recombination and quantum confined Stark effect in green light emitting InGaN quantum wells by modulating the external bias voltage of the pn junction. Time-resolved electroluminescence shows a temporal separation of carrier injection into the active region and radiative recombination within the InGaN quantum wells. During the time when the bias voltage is off, the piezoelectric field is partially compensated by the built-in potential of the pn junction, resulting in an increased radiative recombination rate.
Has It Really Been Demonstrated That Most Genomic Research Findings Are False?

In a widely cited article, Ioannidis argued that most published research findings are false; particularly discovery research involving massive testing, genomics being a typical example. However, his argument ignores adjustment for multiple testing and thus should be taken with a large grain of salt. This is a potential example for statistics courses that concentrate on problem formulation.
BVR Photometry of Northern Hemisphere Luminous Stars. V. 123 Stars in the First Two Galactic Quadrants and the Distribution of O–B2 Supergiants

CCD‐based BVR photometry of 123 previously unobserved stars drawn from volumes I, II, III, and V of the Luminous Stars in the Northern Milky Way catalog (LS catalog) is reported. In addition, the Galactic distribution of over 400 LS‐catalog supergiants as derived from spectroscopic parallaxes is examined.
The Influence of the Madden–Julian Oscillation on Canadian Wintertime Surface Air Temperature

Abstract Using the homogenized Canadian historical daily surface air temperature (SAT) for 210 relatively evenly distributed stations across Canada, the lagged composites and probability of the above- and below-normal SAT in Canada for different phases of the Madden–Julian oscillation (MJO) in the winter season are analyzed. Significant positive SAT anomalies and high probability of above-normal events in the central and eastern Canada are found 5–15 days following MJO phase 3, which corresponds to an enhanced precipitation over the Indian Ocean and Maritime Continent and a reduced convective activity near the tropical central Pacific. On the other hand, a positive SAT anomaly appears over a large part of northern and northeastern Canada about 5–15 days after the MJO is detected in phase 7. An analysis of the evolution of the 500-hPa geopotential height and sea level pressure anomalies indicates that the Canadian SAT anomaly is a result of a Rossby wave train associated with the tropical convection anomal.
ENIDrift: A Fast and Adaptive Ensemble System for Network Intrusion Detection under Real-world Drift

Machine Learning (ML) techniques have been widely applied for network intrusion detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer from fundamental limitations that hinder them from being deployed in the real world. They consider a narrow scope rather than real-world drift that involves dynamically distributed network packets and well-crafted ML attacks. Besides, they pose high runtime overhead and have low processing speed. In this paper, we solve the limitations and design ENIDrift, a fast and adaptive ensemble system for real-world network intrusion detection. ENIDrift employs iP2V, a novel incremental feature extraction method based on network packet fields, which adopts a simple three-layer neural network with relatively lightweight computation and achieves high efficiency. ENIDrift uses a stable sub-classifier generation module that constructs new sub-classifiers based on the stability and accuracy of incoming data chunks, and its training time is reduced from to . We extend the threat model and place experiments in real-world settings. We make the first real-world drift dataset, RWDIDS, which contains intense and different drifts for NIDS. Our extensive evaluation under real-world drift demonstrates that ENIDrift significantly outperforms the state-of-the-art solutions by up to 69.78% of F1 and reduces running time by 87.6%. ENIDrift achieves a 100% F1 against our adversarial attack and is adaptive to various real-world drifts. Our field test also shows ENIDrift functions well even with delayed, inadequate training data, which is practical for real-world usage.
Analysis of Payment Service Provider SDKs in Android

Payment Service Providers (PSPs) provide software development toolkits (SDKs) for integrating complex payment processing code into applications. Security weaknesses in payment SDKs can impact thousands of applications. In this work, we propose AARDroid for statically assessing payment SDKs against OWASP’s MASVS industry standard for mobile application security. In creating AARDroid, we adapted application-level requirements and program analysis tools for SDK-specific analysis, tailoring dataflow analysis for SDKs using domain-specific ontologies to infer the security semantics of application programming interfaces (APIs). We apply AARDroid to 50 payment SDKs and discover security weaknesses including saving unencrypted credit card information to files, use of insecure cryptographic primitives, insecure input methods for credit card information, and insecure use of WebViews. These results demonstrate the value of applying security analysis at the SDK granularity to prevent the widespread deployment of insecure code.
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition

Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally expensive but also neglects the fact that different videos rely on different modalities for predictions. This article introduces Hierarchical and Conditional Modality Selection (HCMS), a simple yet efficient multimodal learning framework for efficient video recognition. HCMS operates on a low-cost modality, i.e., audio clues, by default, and dynamically decides on-the-fly whether to use computationally expensive modalities, including appearance and motion clues, on a per-input basis. This is achieved by the collaboration of three LSTMs that are organized in a hierarchical manner. In particular, LSTMs that operate on high-cost modalities contain a gating module, which takes as inputs lower-level features and historical information to adaptively determine whether to activate its corresponding modality; otherwise, it simply reuses historical information. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate the proposed approach can effectively explore multimodal information for improved classification performance while requiring much less computation.
FAuST: Striking a Bargain between Forensic Auditing’s Security and Throughput

System logs are invaluable to forensic audits, but grow so large that in practice fine-grained logs are quickly discarded – if captured at all – preventing the real-world use of the provenance-based investigation techniques that have gained popularity in the literature. Encouragingly, forensically-informed methods for reducing the size of system logs are a subject of frequent study. Unfortunately, many of these techniques are designed for offline reduction in a central server, meaning that the up-front cost of log capture, storage, and transmission must still be paid at the endpoints. Moreover, to date these techniques exist as isolated (and, often, closed-source) implementations; there does not exist a comprehensive framework through which the combined benefits of multiple log reduction techniques can be enjoyed. In this work, we present FAuST, an audit daemon for performing streaming audit log reduction at system endpoints. After registering with a log source (e.g., via Linux Audit’s audisp utility), FAuST incrementally builds an in-memory provenance graph of recent system activity. During graph construction, log reduction techniques that can be applied to local subgraphs are invoked immediately using event callback handlers, while techniques meant for application on the global graph are invoked in periodic epochs. We evaluate FAuST, loaded with eight different log reduction modules from the literature, against the DARPA Transparent Computing datasets. Our experiments demonstrate the efficient performance of FAuST and identify certain subsets of reduction techniques that are synergistic with one another. Thus, FAuST dramatically simplifies the evaluation and deployment of log reduction techniques.
Learning from Failures: Secure and Fault-Tolerant Aggregation for Federated Learning

Federated learning allows multiple parties to collaboratively train a global machine learning (ML) model without sharing their private datasets. To make sure that these local datasets are not leaked, existing works propose to rely on a secure aggregation scheme that allows parties to encrypt their model updates before sending them to the central server that aggregates the encrypted inputs. In this work, we design and evaluate a new secure and fault-tolerant aggregation scheme for federated learning that is robust against client failures. We first develop a threshold-variant of the secure aggregation scheme proposed by Joye and Libert. Using this new building block together with a dedicated decentralized key management scheme and an input encoding solution, we design a privacy-preserving federated learning protocol that, when executed among n clients, can recover from up to failures. Our solution is secure against a malicious aggregator who can manipulate messages to learn clients’ individual inputs. We show that our solution outperforms the state-of-the-art fault-tolerant secure aggregation schemes in terms of computation cost on the client. For example, with an ML model of 100,000 parameters, trained with 600 clients, our protocol is 5.5x faster (1.6x faster in case of 180 clients drop).
Polling-Based Memory Interface

Non-volatile memory has been extensively researched as the alternative for a DRAM-based system; however, the traditional memory controller cannot efficiently track and schedule operations for all the memory devices in heterogeneous systems due to different timing requirements and complex architecture supports of various memory technologies. To address this issue, we propose a hybrid memory architecture framework called POMI (POlling-based Memory Interface). It uses a small buffer chip inserted on each DIMM (Dual In-line Memory Module) to decouple operation scheduling from the controller to enable the support for diverse memory technologies in the system. Unlike the conventional DRAM-based system, POMI uses a polling-based memory bus protocol for communication and to resolve any bus conflicts between memory modules. The buffer chip on each DIMM will provide feedback information to the main memory controller so that the polling overhead is trivial. We propose two unique designs. The first one adds additional bus lines for sending the feedback information, and the second one utilizes the Command/Address bus. The framework provides several benefits: a technology-independent memory system, higher parallelism, and better scalability. Our experimental results show that POMI can efficiently support both homogeneous and heterogeneous systems. Compared with the conventional DDR4-2400 implementation, our scheme improves the performance of memory-intensive workloads by 3.7% on average. Compared with an existing interface for hybrid memory systems, Twin-Load, it also improves performance by 22.0% on average for memory-intensive workloads.
Formal Modeling and Security Analysis for Intra-level Privilege Separation

Privileged system software such as mainstream operating system kernels and hypervisors have an ongoing stream of vulnerabilities. Even the inflated secure world in Trusted Execution Environment (TEE) is no longer secure in complex real-world scenarios. Since higher privilege levels cannot always be stacked to provide protection, intra-level privilege separation has become a powerful way to build trustworthy systems. However, existing intra-level privilege separation systems lack sound security analysis and cannot give formal guarantees. In this paper, we introduce a general and extensible formal framework based on a privilege-centric model (PCM) and define the security properties that should be satisfied by intra-level privilege separation. We then instantiate two models using the B-method: Nested Kernel and Hilps, which utilize x86 WP and AArch64 TxSZ mechanisms, respectively. Their security is verified by model checking in ProB. The machine-checked analysis shows that our approach can not only effectively detect design errors and attacks, but also guide future system design.
Snappy: Efficient Fuzzing with Adaptive and Mutable Snapshots

Modern coverage-oriented fuzzers play a crucial role in vulnerability finding. While much research focuses on improving the core fuzzing techniques, some fundamental speed bottlenecks, such as the redundant computations incurred by re-executing the target for every input, remain. Prior solutions mitigate the impact of redundant computations by instead fuzzing a program snapshot, such as the one placed by a fork server at the program entry point or generalizations for annotated APIs, drivers, networked servers, etc. Such snapshots are static and, as such, cannot adapt to the characteristics of the target and the input, missing opportunities to further reduce redundancy and improve fuzzing speed. In this paper, we present Snappy, a new approach to speed up fuzzing by aggressively pruning redundant computations with adaptive and mutable snapshots. The key ideas are to: (i) push the snapshot as deep in the target execution as possible and also end its execution as early as possible, according to how the target processes the relevant input data (adaptive placement); (ii) for each identified placement, cache snapshots across different inputs by patching the snapshot just-in-time with the relevant input data (mutable restore). We propose a generic design applicable to both branch-agnostic and branch-guided input mutation operators and demonstrate Snappy on top of Angora (supporting both classes of operators). Our evaluation shows that, while general, Snappy scores gains even compared to a fork server with hand-optimized static placement such as in FuzzBench, for instance obtaining up to ≈ 1.8x speedups across benchmarks.
Global Filter: Augmenting Images to Support Seeing the “Big Picture” for People with Local Interference

Some neurodivergent people prioritize visual details over the “big picture”. While excellent attention to detail has many advantages, some contexts require the rapid integration of global and local information. A local processing style can be so strong that local details interfere with the fluid integration of global information required for processing of information rapidly displayed on user interfaces. This disconnect between context of an interaction and processing style can be termed local interference. Personalization of visual stimuli can promote a more accessible computing experience. We describe how technological interventions can support shifting of visual attention from local to global features to make them more accessible. We present two empirical studies. One study with one autistic adult revealed a significant shift in eye gaze fixation, and the other study with 20 autistic children revealed filters that visually emphasize primary aspects encouraged more global comments about the image content.
DitDetector: Bimodal Learning based on Deceptive Image and Text for Macro Malware Detection

Macro malware has always been a severe threat to cyber security although the Microsoft Office suite applies the default macro-disabling policy. Among the defense solutions at different stages of the attack chain, document analysis is more targeted through detecting malicious documents with macro malware. It is effective, especially with machine learning methods, but still faces problems handling malware variants, supporting file formats, and attack countermeasures with advanced attack techniques (e.g., Excel 4.0 macro and remote template injection). In this paper, we find it promising to detect deceptive information embedded in documents which tricks users into enabling macros instead of detecting file metadata or extracted macro codes. Thus, we propose a novel solution for macro malware detection named DitDetector, which leverages bimodal learning based on deceptive images and text. Specifically, we extract preview images of documents based on an image export SDK of Oracle and extract textual information from preview images based on an open-source OCR engine. The bimodal model of DitDetector contains a visual encoder, a textual encoder, and a forward neural network, which learns based on the joint representation of the two encoders’ outputs. We evaluate DitDetector on three datasets, including an open-source malicious document dataset (i.e., MalDoc) and two collected real-world adversary datasets (i.e., a database of Excel macros and a database of remote template injection samples). Our experiments show that DitDetector outperforms four existing macro code-based machine learning methods and five reputable Anti-Virus engines. Especially in the real-world test of advanced macro malware, DitDetector gets the F1-score of 99.93% which is at least 3.16% higher than compared solutions.