Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of CO2 Hydrate on Deep-Sea Foraminiferal Assemblages

THE EFFECTS OF CO2 HYDRATE ON DEEP-SEA FORAMINIFERAL ASSEMBLAGES E. R. Ricketts*, J. P. Kennett and T. M. Hill Department of Geological Sciences University of California, Santa Barbara, CA USA Jim Barry Monterey Bay Aquarium Research Institute Moss Landing, CA USA ABSTRACT This study, conducted with the Monterey Bay Aquarium Research Institute (MBARI), is the first to investigate potential effects of CO2 hydrates on benthic microfossils, specifically foraminifera. The experiment was conducted in September 2003 aboard the R/V Western Flier using the ROV Tiburon. Experimental (CO2 exposed) and control cores were collected at 3600m and stained to distinguish live (stained) from dead (unstained) individuals. Foraminifera are ideal because of differing test composition (calcareous and agglutinated) and thickness, and diverse epifaunal and infaunal depth preferences. The effects of the CO2 on assemblages have been tracked both vertically (10cm depth) and horizontally, and between live and dead individuals. Increased mortality and dissolution of calcareous forms resulted from exposure to CO2 hydrate. Preliminary results suggest several major effects on surface sediment assemblages: 1) total number of foraminifera in a sample decreases; 2) foraminiferal diversity decreases in both stained and unstained specimens. The number of planktonic and hyaline calcareous tests declines greatly, with milliolidsmore » being more resistant to dissolution when stained; and 3) percentage of stained (live) forms is higher. Down-core trends (up to 10cm) indicate: 1) percent agglutinated forms decline and calcareous forms increasingly dominate; 2) agglutinated diversity decreases with depth; and 3) assemblages become increasingly similar with depth to those in control cores not subjected to CO2 hydrate. These results imply almost complete initial mortality and dissolution upon CO2 hydrate emplacement. « less
From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices

The proliferation of consumer IoT products in our daily lives has raised the need for secure device authentication and access control. Unfortunately, these resource-constrained devices typically use token-based authentication, which is vulnerable to token compromise attacks that allow attackers to impersonate the devices and perform malicious operations by stealing the access token. Using hardware fingerprints to secure their authentication is a promising way to mitigate these threats. However, once attackers have stolen some hardware fingerprints (e.g., via MitM attacks), they can bypass the hardware authentication by training a machine learning model to mimic fingerprints or reusing these fingerprints to craft forge requests. In this paper, we present MCU-Token, a secure hardware fingerprinting framework for MCU-based IoT devices even if the cryptographic mechanisms (e.g., private keys) are compromised. MCU-Token can be easily integrated with various IoT devices by simply adding a short hardware fingerprint-based token to the existing payload. To prevent the reuse of this token, we propose a message mapping approach that binds the token to a specific request via generating the hardware fingerprints based on the request payload. To defeat the machine learning attacks, we mix the valid fingerprints with poisoning data so that attackers cannot train a usable model with the leaked tokens. MCU-Token can defend against armored adversary who may replay, craft, and offload the requests via MitM or use both hardware (e.g., use identical devices) and software (e.g., machine learning attacks) strategies to mimic the fingerprints. The system evaluation shows that MCU-Token can achieve high accuracy (over 97%) with a low overhead across various IoT devices and application scenarios.
Maginot Line: Assessing a New Cross-app Threat to PII-as-Factor Authentication in Chinese Mobile Apps

—Authentication is one of the established practices to ensure user security. Personally identifiable information (PII), such as national identity card number (ID number) and bank card number, is used widely in China’s mobile apps as an additional secret to authenticate users, i.e., PII-as-Factor Au-thentication ( PaFA ). In this paper, we found a new threat that calls on the cautiousness of PaFA : the simultaneous usages and business-related interactions of apps make the authentication strength of a target app weaker than designed. An adversary, who knows fewer authentication factors (only SMS OTP) than a PaFA system required, can break the authentication by gathering information or abusing cross-app authorization from other apps. To systematically study the potential risks, we proposed a semi-automatic system, MAGGIE , to evaluate the security of PaFA in target apps. By measuring 234 real-world apps in Chinese app markets with the help of MAGGIE , we found 75.4% of apps that deployed PaFA can be bypassed, including the popular and sensitive ones (e.g., AliPay, WeChat, UnionPay), leading to severe consequences like hijack user accounts and making unauthorized purchases. Additionally, we conducted a survey to demonstrate the practical implications of the new risk on users. Finally, we reported our findings to the vendors and provided several mitigation measures.
Don't Interrupt Me - A Large-Scale Study of On-Device Permission Prompt Quieting in Chrome

—A recent large-scale experiment conducted by Chrome [4] has demonstrated that a “quieter” web permission prompt can reduce unwanted interruptions while only marginally affecting grant rates. However, the experiment and the partial roll-out were missing two important elements: (1) an effective and context-aware activation mechanism for such a quieter prompt, and (2) an analysis of user attitudes and sentiment towards such an intervention. In this paper, we address these two limitations by means of a novel ML-based activation mechanism – and its real-world on-device deployment in Chrome – and a large-scale user study with 13.1k participants from 156 countries. First, the telemetry-based results, computed on more than 20 million samples from Chrome users in-the-wild, indicate that the novel on-device ML-based approach is both extremely precise ( > 99% post-hoc precision) and has very high coverage (96% recall for notiﬁcations permission). Second, our large-scale, in-context user study shows that quieting is often perceived as helpful and does not cause high levels of unease for most respondents.
GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity

—Generating accurate call graphs for large programs, particularly at the operating system (OS) level, poses a well-known challenge. This difficulty stems from the widespread use of indirect calls within large programs, wherein the computation of call targets is deferred until runtime to achieve program polymorphism. Consequently, compilers are unable to statically determine indirect call edges. Recent advancements have attempted to use type analysis to globally match indirect call targets in programs. However, these approaches still suffer from low precision when handling large target programs or generic types. This paper presents GNNIC, a Graph Neural Network (GNN) based Indirect Call analyzer. GNNIC employs a technique called abstract-similarity search to accurately identify indirect call targets in large programs. The approach is based on the observation that although indirect call targets exhibit intricate polymorphic behaviors, they share common abstract characteristics, such as function descriptions, data types, and invoked function calls. We consolidate such information into a representative abstraction graph (RAG) and employ GNNs to learn function embeddings. Abstract-similarity search relies on at least one anchor target to bootstrap. Therefore, we also propose a new program analysis technique to locally identify valid targets of each indirect call. Starting from anchor targets, GNNIC can expand the search scope to find more targets of indirect calls in the whole program. The implementation of GNNIC utilizes LLVM and GNN, and we evaluated it on multiple OS kernels. The results demonstrate that GNNIC outperforms state-of-the-art type-based techniques by reducing 86% to 93% of false target functions. Moreover, the abstract similarity and precise call graphs generated by GNNIC can enhance security applications by discovering new bugs, alleviating path-explosion issues, and improving the efficiency of static program analysis. The combination of static analysis and GNNIC resulted in finding 97 new bugs in Linux and
Characteristics of Paleogene Source Rocks and Prediction of Petroleum Resources in Huizhou Depression,Pearl River Mouth Basin

Huizhou depression is one of the major petroleum contribution areas in Pearl River Mouth basin,and it is significant to the research of abundant hydrocarbon depression with the further confirmation of source rock characteristics and petroleum resources.Comprehensive analysis of the abundance,type,maturity of organic matter,effective thickness,organic carbon recovery factor and production rate,shows that Paleogene Wenchang formation and Enping formation are good source rocks.The quantitative prediction technology of source rock TOC seismic data makes up for the lack of less core and measured sample discontinuous distribution.TOC seismic data predict quantitatively TOC contents and variations in the different depths and different time planes,which has laid a good foundation for the more accurate assessment of source rocks and petroleum resources.Based on the quantitative prediction technology of source rock TOC seismic data and the basic stratigraphy unit of third-order sequence,that,petroleum resources quantities of Paleogene source rocks through the genetic method in Huizhou Depression,are 1.698 times last one.The Paleogene transfer zones which are close to source rocks have the advantage of near-source for hydrocarbons with great exploration potential.
Condition evaluation of water retaining structures by a functional approach Comparative practices in Canada and France

ABSTRACT Hydro-Québec and Cemagref have implemented condition evaluation methods for water retaining structures: the condition index (CI) method for embankment dams in Quebec, and SIRS Digue for fluvial dykes in France. These methods were developed from similar approaches, including functional analysis, gathering and formalization of knowledge about failure mechanisms, and definition of performance indicators. Development of both methods was done within a group of experts and lead to technical guides that explain the application limits. The main objective is to prioritize maintenance tasks within a large inventory of dams for Hydro-Quebec, and for fluvial dykes, in France. The use of these methods reaches also other goals at the same time: aid to safety review and dam diagnosis, and experience transfer among the technical staff.
Jejunal Gastrointestinal Stromal Tumor and Gallstones

Gastrointestinal stromal tumors (GISTs) are considered the most common mesenchymal tumors of the digestive system. However, they make up less than 1% of all GI tumors. GISTs arise from the interstitial cells of Cajal and are commonly found in the stomach and small intestine, and rarely in the colon and esophagus. In this case report, we present a 57-year-old male, a known diabetic, who complained of abdominal pain. He was diagnosed with cholelithiasis and a GIST in the jejunum, which was managed laparoscopically without complications. Most of the time, patients with GIST present with vague symptoms, or sometimes, they are asymptomatic. The most common symptoms are abdominal pain, GI bleeding, and an abdominal mass. These symptoms are usually related to the site of tumor growth, tumor size, and tumor rupture or perforation. Regardless of the tumor location, surgical excision is the gold standard for treating GISTs.
Private Aggregate Queries to Untrusted Databases

Private information retrieval (PIR), a privacy-preserving cryptographic tool, solves a simplified version of this problem by hiding the database item that a client accesses. Most PIR protocols require the client to know the exact row index of the intended database item, which cannot support the complicated aggregation-based statistical query in a similar setting. Some works in the PIR space contain keyword searching and SQL-like queries, but most need multiple interactions between the PIR client and PIR servers. Some schemes support searching SQL-like expressive queries in a single round but fail to enable aggregate queries. These schemes are the main focus of this paper. To bridge the gap, we have built a general-purpose novel information-theoretic PIR (IT-PIR) framework that permits a user to fetch the aggregated result, hiding all sensitive sections of the complex query from the hosting PIR server in a single round of interaction. In other words, the server will not know which records contribute to the aggregation. We then evaluate the feasibility of our protocol for both benchmarking and real-world application settings. For instance, in a complex aggregate query to the Twitter microblogging database of 1 million tweets, our protocol takes 0.014 seconds for a PIR server to generate the result when the user is interested in one of 3K user handles. In contrast, for a much-simplified task, not an aggregate but a positional query, Goldberg's regular IT-PIR (Oakland 2007) takes 1.13 seconds. For all possible user handles, 300K, it takes equal time compared to the regular IT-PIR. This example shows that complicated aggregate queries through our framework do not incur additional overhead if not less, compared to the conventional query.
Generic Cryo-CMOS Device Modeling and EDACompatible Platform for Reliable Cryogenic IC Design

This paper outlines the establishment of a generic cryogenic CMOS database in which key electrical parameters and transfer characteristics of the MOSFETs are quantified as functions of device size, temperature/frequency responses. Meanwhile, comprehensive device statistical study is conducted to evaluate the influence of variation and mismatch effects at low temperatures. Furthermore, by incorporating the Cryo-CMOS compact model into the process design kit (PDK), the cryogenic 4 Kb SRAM, 5-bit flash ADC and 8-bit current steering DAC are designed, and their performance is readily investigated and optimized on the EDA-compatible platform, hence laying a solid foundation for large-scale cryogenic IC design.
Effect of Knee Valgus Deformity on Symptomatic Venous Thromboembolism and Prosthesis Revision Risk after Total Knee Arthroplasty: A Multicenter Retrospective Study

Objective Symptomatic venous thromboembolism (VTE) and prosthesis failure are the most serious complications after total knee arthroplasty (TKA). However, whether knee valgus deformity aggravates these complications has not been fully clarified. To study the difference between perioperative symptomatic VTE and prosthesis revision rate in patients with valgus knee osteoarthritis by comparing with patients undergoing TKA for varus deformity and analyze the reasons for revision. At the same time, the distribution and radiographic features of lower extremity deep venous thrombosis were recorded. Methods The data of patients who underwent TKA in two tertiary hospitals from January 2016 to December 2020 were retrospectively reviewed, and a total of 8917 patients were included. According to preoperative manifestations of knee malformations, all patients were divided into two groups: valgus group (n = 412) and varus group (n = 8505). Main indicators included the incidence of symptomatic VTE and prosthesis revision. Secondary outcomes included general information on operative time, Kellgren and Lawrence score, total hospital stay, and total costs. The patient data of the two groups were analyzed by Pearson chi‐square test, Student t test, or Mann–Whitney U test. The revision was evaluated using Kaplan–Meier survival analysis. Results The proportion of valgus knees in TKA patients was 4.62% (412/8917). The incidence of VTE was 6.23‰ (53/8505) and 16.99‰ (7/412) in the varus and valgus groups, and the results were statistically different (p = 0.009). There was no significant difference in echogenicity, number of occluded vessels, and thrombus length between the valgus group (p = 0.102; p = 0.645; p = 0.684). Patients with valgus deformity had 12.14‰ (5/412) prosthesis revision, the incidence of varus deformity was 4.82‰ (41/8505), and the revision risk of valgus group was 2.5 times higher than varus group, and the results were statistically different (p = 0.043). The operation time and hospital stay in the valgus group were longer than those in the varus group, and the results were statistically different (p = 0.018; p < 0.001). Conclusions Valgus deformity increases risk of symptomatic VTE and prosthesis revision after TKA. These results have guiding significance for the prevention of complications after TKA in patients with valgus deformity.
Arctic climate change and decadal variability

High northern latitudes exhibit enhanced near-surface warming in a climate with increasing greenhouse gases compared to other parts of the globe, indicating an amplified climate response to external forcing. Decadal to multidecadal variability sometimes enhances and at other times reduces the long-term trends. Therefore, the influence of internal variability should be taken into account when externally forced climate signals are assessed. This thesis contributes to our understanding of Arctic climate projections by clarifying the role of coupled ocean-sea-ice-atmosphere processes on the long-term trends and decadal variability of the Arctic climate system. Important mechanisms linked to the location of the sea ice margin and ocean heat transports into the Arctic have been identified and were shown to have a substantial effect on the Arctic's response to climate change.
Representations and uses of indigenous areas in tourism experiences in the Russian Arctic

ABSTRACT This paper compares the representation of indigenous tourism experiences in advertisement materials with representations gathered from site visits and tourism sector interviews in the Nenets Autonomous Okrug (NAO) in northern Russia. The study shows that a majority of the visual images and text used for marketing of the area construct a picture of an indigenous people living ‘in harmony’ with nature, representing a romanticized and historical image of indigenous communities. In contrast with these marketing images, large variations exist in everyday practices and among the numerous practical considerations related to the recent and limited development of tourism in the NAO.
Linear Stability Analysis of Oblique Couette-Poiseuille flows

We perform a detailed numerical study of modal and non-modal stability in oblique Couette-Poiseuille profiles, which are among the simplest examples of three-dimensional boundary layers. Through a comparison with the Orr-Sommerfeld operator for the aligned case, we show how an effective wall speed succinctly characterizes modal stability. Large-scale parameter sweeps reveal that the misalignment between the pressure gradient and wall motion is, in general, destabilizing. For flows that are sufficiently oblique, the instability is found to depend exclusively on the direction of wall motion and not on its speed, a conclusion supported, in part, by the perturbation energy budget and the evolution of the critical layers. Closed forms for the critical parameters in this regime are derived using a simple analysis. Finally, a modified long-wavelength approximation is developed, and the resulting asymptotic eigenvalue problem is used to show that there is no cutoff wall speed for unconditional stability whenever the angle of wall motion is non-zero, in stark contrast to the aligned case. From a non-modal perspective, pseudo-resonance is examined through the resolvent and the $\epsilon$-pseudospectra. An analysis of the unforced initial value problem shows that the maximum energy gain is highly dependent on both the magnitude and direction of the wall velocity. However, the strongest amplification is always achieved for configurations that are only weakly skewed. Finally, the optimal perturbations appear to develop via a lift-up effect induced by an Orr-like mechanism.
Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks

Lightweight design of Convolutional Neural Networks (CNNs) requires co-design efforts in the model architectures and compression techniques. As a novel design paradigm that separates training and inference, a structural re-parameterized (SR) network such as the representative RepVGG revitalizes the simple VGG-like network with a high accuracy comparable to advanced and often more complicated networks. However, the merging process in SR networks introduces outliers into weights, making their distribution distinct from conventional networks and thus heightening difficulties in quantization. To address this, we propose an operator-level improvement for training called Outlier Aware Batch Normalization (OABN). Additionally, to meet the demands of limited bitwidths while upkeeping the inference accuracy, we develop a clustering-based non-uniform quantization framework for Quantization-Aware Training (QAT) named ClusterQAT. Integrating OABN with ClusterQAT, the quantized performance of RepVGG is largely enhanced, particularly when the bitwidth falls below 8.
Improving LSH via Tensorized Random Projection

Locality sensitive hashing (LSH) is a fundamental algorithmic toolkit used by data scientists for approximate nearest neighbour search problems that have been used extensively in many large scale data processing applications such as near duplicate detection, nearest neighbour search, clustering, etc. In this work, we aim to propose faster and space efficient locality sensitive hash functions for Euclidean distance and cosine similarity for tensor data. Typically, the naive approach for obtaining LSH for tensor data involves first reshaping the tensor into vectors, followed by applying existing LSH methods for vector data $E2LSH$ and $SRP$. However, this approach becomes impractical for higher order tensors because the size of the reshaped vector becomes exponential in the order of the tensor. Consequently, the size of LSH parameters increases exponentially. To address this problem, we suggest two methods for LSH for Euclidean distance and cosine similarity, namely $CP-E2LSH$, $TT-E2LSH$, and $CP-SRP$, $TT-SRP$, respectively, building on $CP$ and tensor train $(TT)$ decompositions techniques. Our approaches are space efficient and can be efficiently applied to low rank $CP$ or $TT$ tensors. We provide a rigorous theoretical analysis of our proposal on their correctness and efficacy.
Differentially Private Training of Mixture of Experts Models

This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As Large Language Models (LLMs) scale to billions of parameters, leveraging expansive datasets, they exhibit enhanced linguistic capabilities and emergent abilities. However, this growth raises significant computational and privacy concerns. Our study addresses these issues by exploring the potential of MoE models, known for their computational efficiency, and the application of DP, a standard for privacy preservation. We present the first known attempt to train MoE models under the constraints of DP, addressing the unique challenges posed by their architecture and the complexities of DP integration. Our initial experimental studies demonstrate that MoE models can be effectively trained with DP, achieving performance that is competitive with their non-private counterparts. This initial study aims to provide valuable insights and ignite further research in the domain of privacy-preserving MoE models, softly laying the groundwork for prospective developments in this evolving field.