Miracle-dz/UniFAR-FTUs huggingface dataset:
GLS-CSC: A Simple but Effective Strategy to Mitigate Chinese STM Models' Over-Reliance on Superficial Clue

Pre-trained models have achieved success in Chinese Short Text Matching (STM) tasks, but they often rely on superficial clues, leading to a lack of robust predictions. To address this issue, it is crucial to analyze and mitigate the influence of superficial clues on STM models. Our study aims to investigate their over-reliance on the edit distance feature, commonly used to measure the semantic similarity of Chinese text pairs, which can be considered a superficial clue. To mitigate STM models' over-reliance on superficial clues, we propose a novel resampling training strategy called Gradually Learn Samples Containing Superficial Clue (GLS-CSC). Through comprehensive evaluations of In-Domain (I.D.), Robustness (Rob.), and Out-Of-Domain (O.O.D.) test sets, we demonstrate that GLS-CSC outperforms existing methods in terms of enhancing the robustness and generalization of Chinese STM models. Moreover, we conduct a detailed analysis of existing methods and reveal their commonality.
Graded Modal Types for Integrity and Confidentiality

Graded type systems, such as the one underlying the Granule programming language, allow various different properties of a program's behaviour to be tracked via annotating types with additional information, which we call grades. One example of such a property, often used as a case study in prior work on graded types, is information flow control, in which types are graded by a lattice of security levels allowing noninterference properties to be automatically verified and enforced. These typically focus on one particular aspect of security, however, known as confidentiality; public outputs are prohibited from depending on private inputs. Integrity, a property specifying that trusted outputs must not depend on untrusted inputs, has not been examined in this context. This short paper aims to remedy this omission. It is well-known that confidentiality and integrity are in some sense dual properties, but simply reversing the ordering of the security lattice turns out to be unsatisfactory for the purpose of combining both kinds of property in a single system, at least in our setting. We analogize the situation to recent work on embedding both linear and uniqueness types in a graded framework, and use this framing to demonstrate that we can enforce both integrity and confidentiality alongside one another. The main idea is to add an additional flavour of modality annotated for integrity, such that the existing graded comonad for tracking confidentiality now also acts as a relative monad over the new modality, with rules allowing information to flow from trusted to public to private.
Robot Localization and Mapping Final Report - Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry

Visual odometry (VO) and SLAM have been using multi-view geometry via local structure from motion for decades. These methods have a slight disadvantage in challenging scenarios such as low-texture images, dynamic scenarios, etc. Meanwhile, use of deep neural networks to extract high level features is ubiquitous in computer vision. For VO, we can use these deep networks to extract depth and pose estimates using these high level features. The visual odometry task then can be modeled as an image generation task where the pose estimation is the by-product. This can also be achieved in a self-supervised manner, thereby eliminating the data (supervised) intensive nature of training deep neural networks. Although some works tried the similar approach [1], the depth and pose estimation in the previous works are vague sometimes resulting in accumulation of error (drift) along the trajectory. The goal of this work is to tackle these limitations of past approaches and to develop a method that can provide better depths and pose estimates. To address this, a couple of approaches are explored: 1) Modeling: Using optical flow and recurrent neural networks (RNN) in order to exploit spatio-temporal correlations which can provide more information to estimate depth. 2) Loss function: Generative adversarial network (GAN) [2] is deployed to improve the depth estimation (and thereby pose too), as shown in Figure 1. This additional loss term improves the realism in generated images and reduces artifacts.
Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics

Logs have been widely adopted in software system development and maintenance because of the rich runtime information they record. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on developing intelligent and automated log analysis techniques. However, only a few of these techniques have reached successful deployments in industry due to the lack of public log datasets and open benchmarking upon them. To fill this significant gap and facilitate more research on AI-driven log analytics, we have collected and released loghub, a large collection of system log datasets. In particular, loghub provides 19 real-world log datasets collected from a wide range of software systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical usage scenarios of the loghub datasets, and present our benchmarking results on loghub to benefit the researchers and practitioners in this field. Up to the time of this paper writing, the loghub datasets have been downloaded for roughly 90,000 times in total by hundreds of organizations from both industry and academia. The loghub datasets are available at https://github.com/logpai/loghub.
Identifying Single-Input Linear System Dynamics from Reachable Sets

This paper is concerned with identifying linear system dynamics without the knowledge of individual system trajectories, but from the knowledge of the system's reachable sets observed at different times. Motivated by a scenario where the reachable sets are known from partially transparent manufacturer specifications or observations of the collective behavior of adversarial agents, we aim to utilize such sets to determine the unknown system's dynamics. The paper has two contributions. Firstly, we show that the sequence of the system's reachable sets can be used to uniquely determine the system's dynamics for asymmetric input sets under some generic assumptions, regardless of the system's dimensions. We also prove the same property holds up to a sign change for two-dimensional systems where the input set is symmetric around zero. Secondly, we present an algorithm to determine the system's dynamics. We apply and verify the developed theory and algorithms on an unknown band-pass filter circuit solely provided the unknown system's reachable sets over a finite observation period.
Sparse-DFT and WHT Precoding with Iterative Detection for Highly Frequency-Selective Channels

Various precoders have been recently studied by the wireless community to combat the channel fading effects. Two prominent precoders are implemented with the discrete Fourier transform (DFT) and Walsh-Hadamard transform (WHT). The WHT precoder is implemented with less complexity since it does not need complex multiplications. Also, spreading can be applied sparsely to decrease the transceiver complexity, leading to sparse DFT (SDFT) and sparse Walsh-Hadamard (SWH). Another relevant topic is the design of iterative receivers that deal with inter-symbol-interference (ISI). In particular, many detectors based on expectation propagation (EP) have been proposed recently for channels with high levels of ISI. An alternative is the maximum a-posterior (MAP) detector, although it leads to unfeasible high complexity in many cases. In this paper, we provide a relatively low-complexity \textcolor{black}{computation} of the MAP detector for the SWH. We also propose two \textcolor{black}{feasible methods} based on the Log-MAP and Max-Log-MAP. Additionally, the DFT, SDFT and SWH precoders are compared using an EP-based receiver with one-tap FD equalization. Lastly, SWH-Max-Log-MAP is compared to the (S)DFT with EP-based receiver in terms of performance and complexity. The results show that the proposed SWH-Max-Log-MAP has a better performance and complexity trade-off for QPSK and 16-QAM under highly selective channels, but has unfeasible complexity for higher QAM orders.
Caveat (IoT) Emptor: Towards Transparency of IoT Device Presence

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation. This prompts, respectively, privacy and security/safety issues. The dangers of hidden IoT devices have been recognized and prior research suggested some means of mitigation, mostly based on traffic analysis or using specialized hardware to uncover devices. While such approaches are partially effective, there is currently no comprehensive approach to IoT device transparency. Prompted in part by recent privacy regulations (GDPR and CCPA), this paper1 motivates and constructs a privacy-agile Root-of-Trust architecture for IoT devices, called PAISA: Privacy-Agile IoT Sensing and Actuation. It guarantees timely and secure announcements of nearby IoT devices' presence and their capabilities. PAISA has two components: one on the IoT device that guarantees periodic announcements of its presence even if all device software is compromised, and the other on the user device, which captures and processes announcements. PAISA requires no hardware modifications; it uses a popular off-the-shelf Trusted Execution Environment (TEE) -- ARM TrustZone. To demonstrate its viability, PAISA is instantiated as an open-source prototype which includes: an IoT device that makes announcements via IEEE 802.11 WiFi beacons and an Android smartphone-based app that captures and processes announcements. Security and performance of PAISA design and its prototype are also discussed.
Characterization of monogalactosyldiacylglycerol synthases in Gracilariopsis lemaneiformis and their potential roles in the fading of the thallus

Membrane lipids play essential roles in regulating physiological properties in higher plants and algae. Monogalactosyldiacylglycerol (MGDG) is a major thylakoid membrane lipid, and it is an important source of polyunsaturated fatty acids for cells, plays a key role in the biogenesis of plastids, and maintains the function of the photosynthetic machinery. Several studies have indicated that the knockdown of MGDG synthase results in membrane lipid remodeling, albino seedlings, and changes in photosynthetic performance. However, the effects of MGDG synthase (MGD) inhibitors on lipids in macroalgae have not yet been clarified. Here, we characterized the effects of MGD inhibitors (ortho‐phenanthroline and N‐ethylmaleimide) on the composition of the fatty acids observed in MGDG and digalactosyldiacylglycerol (DGDG) in Gracilariopsis lemaneiformis using electrospray ionization–mass spectrometry. The most abundant MGDG species contained 16:0/18:1 (sn‐1/sn‐2) fatty acids, and the most dominant DGDG species contained 20:5/16:0 (sn‐1/sn‐2) fatty acids. Measurements of photosynthetic pigments and photosynthetic parameters revealed that photosynthesis of G. lemaneiformis was impaired. Principal component analysis and Spearman's correlation analysis revealed interactions between specific MGDG structural composition patterns and key metabolites involved in photosynthesis, indicating that 20:4/16:0 (sn‐1/sn‐2) MGDG and 16:0/18:1 (sn‐1/sn‐2) MGDG affect the structure and function of phycobilisomes and thus the color of G. lemaneiformis. Three genes (GlMGD1, GlMGD2, and GlMGD3) were cloned and identified. The addition of N‐ethylmaleimide to G. lemaneiformis did not affect the abundance of GlMGD mRNA, and the abundance of transcripts was significantly decreased by ortho‐phenanthroline.
The Geographic Revolution in Early America: Maps, Literacy, and National Identity

The rapid rise in popularity of maps and geography handbooks in the eighteenth century ushered in a new geographic literacy among non elite Americans. In a path breaking and richly illustrated examination of this transformation, Martin Bruckner argues that geographic literacy as it was played out in popular literary genres significantly influenced the formation of identity in America from the 1680s to the 1820s. Drawing on historical geography, cartography, literary history, and material culture, Bruckner recovers a vibrant culture of geography consisting of property plats and surveying manuals, decorative wall maps and school geographies, the nation's first atlases, and sentimental objects such as needlework samplers. By showing how this geographic revolution affected the production of literature, Bruckner demonstrates that the internalization of geography as a kind of language helped shape the literary construction of the modern American subject. Empirically rich and provocative in its readings, "The Geographic Revolution in Early America" proposes a new, geographical basis for Anglo-Americans' understanding of their character and its expression in pedagogical and literary terms.
Evidence Synthesis for Complex Interventions Using Meta-Regression Models

Abstract A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.
Microstates of the cortical brain‐heart axis

Electroencephalographic (EEG) microstates are brain states with quasi‐stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain‐heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness‐of‐fit evaluations, we demonstrated the existence of microstates of the functional brain‐heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous‐system microstates are spatio‐temporal quasi‐stable states that exclusively refer to the efferent brain‐to‐heart direction. We demonstrated brain‐heart microstates that could be associated with specific experimental conditions as well as brain‐heart microstates that are non‐specific to tasks.
Recommendations on prevention of infections in patients with T-cell lymphomas: a narrative review and synthesis

Abstract T/Natural killer (NK) cell lymphomas (TCL) represent a heterogenous subgroup of non-Hodgkin lymphoma, associated with poorer prognosis and higher treatment toxicity. A cohesive synthesis of infection outcomes among TCL patients is lacking. International guidelines offer no specific recommendations regarding prophylaxis or supportive infection care for TCL patients. This systematic narrative review highlights infection outcomes in TCL patients treated with conventional, and novel therapies. Recommendations for infection screening, antimicrobial prophylaxis and vaccination strategies are outined.
Non-perturbative lattice approaches to complex path integrals: from non-perturbative saddle points to real-time physics of chiral media

In this thesis we discuss novel techniques for studying non-perturbative physics of strongly coupled systems such as QCD-like asymptotically free models. The leitmotif of these studies is appreciation of important role of complex saddle points of path integrals. In this context we consider problems related to intimate resurgent relations of asymptotic perturbative expansions and non-perturbative physics, sign problem in lattice simulations and real-time physics of chiral plasmas.
Epigenome‐wide methylation haplotype association analysis identified HLA‐DRB1, HLA‐DRB5 and HLA‐DQB1 as risk factors for rheumatoid arthritis

The aim of this study was to compare nonrandom associations between physically adjacent single methylation polymorphism loci among rheumatoid arthritis (RA) and normal subjects for investigating RA‐risk methylation haplotypes (meplotype). With 354 ACPA‐positive RA patients and 335 normal controls selected from a case–control study based on Swedish population, we conducted the first RA epigenome‐wide meplotype association study using our software EWAS2.0, mainly including (i) converted the β value to methylation genotype (menotype) data, (ii) identified methylation disequilibrium (MD) block, (iii) calculated frequent of each meplotypes in MD block and performed case–control association test and (iv) screened for RA‐risk meplotypes by odd ratio (OR) and p‐values. Ultimately, 545 meplotypes on 334 MD blocks were identified significantly associated with RA (p‐value < .05). These meplotypes were mapped to 329 candidate genes related to RA. Subsequently, combined with gene optimization, eight RA‐risk meplotypes were identified on three risk genes: HLA‐DRB1, HLA‐DRB5 and HLA‐DQB1. Our results reported the relationship between DNA methylation pattern on HLA‐DQB1 and the risk of RA for the first time, demonstrating the co‐demethylation of ‘cg22984282’ and ‘cg13423887’ on HLA‐DQB1 gene (meplotype UU, p‐value = 2.90E − 6, OR = 1.68, 95% CI = [1.35, 2.10]) may increase the risk of RA. Our results demonstrates the potential of methylation haplotype analysis to identify RA‐related genes from a new perspective and its applicability to the study of other disease.
Am I Wrong, or Is the Autograder Wrong? Effects of AI Grading Mistakes on Learning

Errors in AI grading and feedback often have an intractable set of causes and are, by their nature, difficult to completely avoid. Since inaccurate feedback potentially harms learning, there is a need for designs and workflows that mitigate these harms. To better understand the mechanisms by which erroneous AI feedback impacts students’ learning, we conducted surveys and interviews that recorded students’ interactions with a short-answer AI autograder for “Explain in Plain English” code reading problems. Using causal modeling, we inferred the learning impacts of wrong answers marked as right (false positives, FPs) and right answers marked as wrong (false negatives, FNs). We further explored explanations for the learning impacts, including errors influencing participants’ engagement with feedback and assessments of their answers’ correctness, and participants’ prior performance in the class. FPs harmed learning in large part due to participants’ failures to detect the errors. This was due to participants not paying attention to the feedback after being marked as right, and an apparent bias against admitting one’s answer was wrong once marked right. On the other hand, FNs harmed learning only for survey participants, suggesting that interviewees’ greater behavioral and cognitive engagement protected them from learning harms. Based on these findings, we propose ways to help learners detect FPs and encourage deeper reflection on FNs to mitigate the learning harms of AI errors.
Porting AI/ML Models to Intelligence Processing Units (IPUs)

Intelligence processing units (IPUs) are specifically designed accelerators that are dedicated to support artificial intelligence (AI) and machine learning (ML) workflows. Here, we report on the performance characteristics and code-porting experiences on Graphcore IPUs offered on the new National Science Foundation (NSF)-funded Accelerating Computing for Emerging Sciences (ACES) testbed. Our benchmarks compared performance of AI/ML frameworks on ACES IPUS to similar runs on the Graphcloud environment, a commercial IPU cloud service offered by Graphcore. We also ported two PyTorch neural network models from Graphics Processing Units (GPUs) to IPUs to ensure the efficacy of the software environment. The ported models include the TransCycleGAN model that is used in reconstructing high-resolution images from low-resolution images, and the Hierarchical Autoencoder that is for large-scale high-resolution scientific data compression in climate models. These models were successfully ported on mulitple IPUs using utilities in the Graphcore Poplar software development kit. Increasing the number of IPUs resulted in a considerable enhancement in the model's throughput.
Exploring Unsupervised Machine Learning Techniques for Genomic Island Categorization

1 BACKGROUND Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal gene transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with a great impact on their evolution and behavior [1]. In recent years, advancements in sequencing technologies and bioinformatics analysis have enabled the identification and characterization of genomic islands in many bacterial and archaeal genomes, revealing a diverse and dynamic landscape of mobile genetic element. To that end, our recently developed tool called Target / Integrative Genetic Element Retriever (TIGER) can precisely identify mobile DNAs in a given genome and identify genomic islands with high accuracy [2]. The aim of current abstract is to show our efforts on categorizing the discovered Genomic Islands by TIGER for E. coli genomes and exploring their relationship, with the ultimate goal of generating phylogenetic-like structures. We will employ unsupervised learning method called hierarchical clustering to categorize the GIs. We will use average nucleotide identity (ANI) score as our similarity score between a given pair of genomic islands.