Miracle-dz/UniFAR-FTUs huggingface dataset:
MedBN: Robust Test-Time Adaptation against Malicious Test Samples

Test-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA methods excel in adapting to test data variations, such adaptability exposes a model to vulnerability against malicious examples, an aspect that has received limited attention. Previous studies have uncovered security vulnerabilities within TTA even when a small proportion of the test batch is maliciously manipulated. In response to the emerging threat, we propose median batch normalization (MedBN), leveraging the robustness of the median for statistics estimation within the batch normalization layer during test-time inference. Our method is algorithm-agnostic, thus allowing seamless integration with existing TTA frameworks. Our experimental results on benchmark datasets, including CIFAR10-C, CIFAR100-C and ImageNet-C, consistently demonstrate that MedBN outperforms existing approaches in maintaining robust performance across different attack scenarios, encompassing both instant and cumulative attacks. Through extensive experiments, we show that our approach sustains the performance even in the absence of attacks, achieving a practical balance between robustness and performance.
An RNA polymerase I enhancer in Saccharomyces cerevisiae

By the use of an artificial gene coding for rRNA (rDNA gene), we found that transcription of the major precursor rRNA in Saccharomyces cerevisiae cells is stimulated 15-fold by a positive control element located 2 kilobases upstream of the transcription initiation site. Analysis of in vitro runon transcripts suggests that this promoter element increases the frequency of initiation by RNA polymerase I molecules. A 190-base-pair fragment encompassing the promoter element can stimulate transcription on a centromere plasmid in either orientation, upstream or downstream of the transcription initiation site, suggesting that it is an enhancer element. Integration of artificial rDNA genes into a nonribosomal locus in the genome demonstrates that the rDNA enhancer functions either 5' or 3' to an rRNA transcription unit, suggesting it may operate in both directions within the rDNA tandem array. This is the first observation in S. cerevisiae of the stimulation of transcription by an element placed downstream. Finally, enhancer activity is dependent upon sequences that lie at both boundaries of the 190-base-pair fragment. In particular, a 5-base-pair deletion at the extreme 3' boundary of the 190-base-pair fragment greatly reduces the activation of transcription and implicates a set of inverted repeats.
Observational Constraints on the Maximum Masses of White Dwarfs, Neutron Stars, and Exotic Stars in Non-Minimal Derivative Coupling Gravity

The advancement of astronomical observations opens the possibility of testing our current understanding of gravitational theory in the strong-field regime and probing any deviation from general relativity. We explore to what extent compact stars predicted by non-minimal derivative coupling (NMDC) gravity theory agree with observed data. We investigate white dwarfs (WDs), neutron stars (NSs), and quark stars (QSs) mass and radius in various values of constant scalar $|Q_{\infty}|$ at coupling strength of $\eta=\pm1$. This study focuses on the astrophysical impacts of altering maximum masses by values of $|Q_{\infty}|$ and $\eta$. From an observational point of view, we found that WD stars are consistent with ultra-cold WD data at $|Q_{\infty}| \lesssim 0.2$. We also found that QS has a similar impact of mass-radius to NS, where the modification is more significant at higher (central) density. For NS and QS EoSs, the value $|Q_{\infty}|$ strongly alters the critical mass and might eliminate the $M-\rho_c$ turning point in the negative $\eta$ case. In that case, the sufficiently large $|Q_{\infty}|$ could predict $M>2.6 M_\odot$ NS and QS, i.e., larger than GW190814 secondary counterpart. We suggest that the lower mass gap in the gravitational wave and x-ray binary mass population data might restrict the theory's $|Q_{\infty}|$.
Correlation of psychooncological distress- screening and quality of life assessment in neurosurgical patients

Background Cerebral tumors are associated with high rates of anxiety, depression and reduced health related quality of life. But still psychooncological screening instruments are not implemented in the daily routine of neurosurgical departments. In contrast the EORTC QLQ-C30/ EORTC QLQ- BN20 questionnaire is often used to evaluate quality of life in the framework of clinical studies. We were therefore interested, if conspicuous distress screening results are also reflected by HRQOL assessment. Patients and Methods Patients who were electively admitted for surgery of intracranial lesions were screened for their psychooncological distress using two self-assessment instruments (Hospital Anxiety and Depression Scale (HADS) and Distress Thermometer (DT)) and one external assessment questionnaire (Psychooncological base documentation (PO-Bado). Results were correlated with three subscales of the EORTC-QLQ-C30 and EORTC-QLQ-BN20 questionnaire. Results From October 2013 to March 2015, 594 patients were admitted for elective cranial neurosurgical procedure. 489 neurosurgical patients were screened for increased distress. Data from 450 patients could be correlated with the EORTC-QLQ-C30 and EORTC-QLQ-BN20. In 265 patients screening revealed increased distress. A concurrent reduced global health /higher rates of future uncertainty and conspicuous distress screening results are found in 173 patients (69.5%) compared to 30.5% of patients (n= 76) with unremarkable screening. Increased distress screening was highly significant with increased level of future uncertainty as well as decreased level of quality of life and global health (p<0.0001). Conclusion Psychooncological distress is accompanied by reduced quality of life, global heath and increased future uncertainty. Therefore HQOL assessment can be helpful identifying patients with increased distress.
Characterizing Controllability and Observability for Systems with Locality, Communication, and Actuation Constraints

This paper presents a closed-form notion of controllability and observability for systems with communication delays, actuation delays, and locality constraints. The formulation reduces to classical notions of controllability and observability in the unconstrained setting. As a consequence of our formulation, we show that the addition of locality and communication constraints may not affect the controllability and observability of the system, and we provide an efficient sufficient condition under which this phenomenon occurs. This contrasts with actuation and sensing delays, which cause a gradual loss of controllability and observability as the delays increase. We illustrate our results using linearized swing equations for the power grid, showing how actuation delay and locality constraints affect controllability.
Co-Expression of TWIST1 and ZEB2 in Oral Squamous Cell Carcinoma Is Associated with Poor Survival

Oral squamous cell carcinoma (OSCC) is an aggressive disease accounting for more than 260,000 cancer cases diagnosed and 128,000 deaths worldwide. A large majority of cancer deaths result from cancers that have metastasized beyond the primary tumor. The relationship between genetic changes and clinical outcome can reflect the biological events that promote cancer’s aggressive behavior, and these can serve as molecular markers for improved patient management and survival. To this end, epithelial-mesenchymal transition (EMT) is a major process that promotes tumor invasion and metastasis, making EMT-related proteins attractive diagnostic biomarkers and therapeutic targets. In this study, we used immunohistochemistry to study the expression of a panel of transcription factors (TWIST1, SNAI1/2, ZEB1 and ZEB2) and other genes intimately related to EMT (CDH1 and LAMC2) at the invasive tumor front of OSCC tissues. The association between the expression of these proteins and clinico-pathological parameters were examined with Pearson Chi-square and correlation with survival was analyzed using Kaplan Meier analysis. Our results demonstrate that there was a significant differential expression of CDH1, LAMC2, SNAI1/2 and TWIST1 between OSCC and normal oral mucosa (NOM). Specifically, CDH1 loss was significantly associated with Broder’s grading, while diffused LAMC2 was similarly associated with non-cohesive pattern of invasion. Notably, co-expression of TWIST1 and ZEB2 in OSCC was significantly associated with poorer overall survival, particularly in patients without detectable lymph node metastasis. This study demonstrates that EMT-related proteins are differentially expressed in OSCC and that the co-expression of TWIST1 and ZEB2 could be of clinical value in identifying patients with poor survival for appropriate patient management.
Rationale Dataset and Analysis for the Commit Messages of the Linux Kernel Out-of-Memory Killer

Code commit messages can contain useful information on why a developer has made a change. However, the presence and structure of rationale in real-world code commit messages is not well studied. Here, we detail the creation of a labelled dataset to analyze the code commit messages of the Linux Kernel Out-Of-Memory Killer component. We study aspects of rationale information, such as presence, temporal evolution, and structure. We find that 98.9% of commits in our dataset contain sentences with rationale information, and that experienced developers report rationale in about 60% of the sentences in their commits. We report on the challenges we faced and provide examples for our labelling.
Cefuroxime for Empiric Treatment of Community-Acquired Pneumococcal Pneumonia: Is There a Generation Gap?

Background:Streptococcus pneumoniae infection is an important cause of morbidity and mortality. The recommendations to use expanded-spectrum β-lactam drugs for patients with community-acquired pneumonia derived from the growing prevalence of penicillin-resistant pneumococci. Controversy exists regarding the use of second generation cephalosporins for empirical treatment of community-acquired pneumonia. Methods: In a retrospective study, 31 adult patients with pneumococcal pneumonia and bacteremia caused by S. pneumoniae that was intermediately resistant to penicillin were compared with 31 control patients with similar infection caused by penicillin-susceptible pneumococci. All patients were treated empirically with cefuroxime, alone or in combination with other antibiotics. Morbidity and mortality were studied. Results: All unsusceptible pneumococci isolates were intermediately resistant to penicillin. No cases of fully resistant pneumococci were isolated from blood cultures in our hospital. Two factors were significantly associated with non-susceptibility to penicillin: hematologic malignancy and immunosuppression. No significant difference in morbidity or mortality was detected between the 2 groups, and penicillin minimum inhibitory concentration was not found to be a factor associated with mortality. Conclusions: Patients with pneumococcal pneumonia caused by intermediately resistant pneumococci can be empirically treated with cefuroxime. In regions where fully resistant pneumococci are rare, the use of a second generation cephalosporin for empiric treatment of community-acquired pneumonia may be appropriate.
On Uncertainty Quantification for Near-Bayes Optimal Algorithms

Bayesian modelling allows for the quantification of predictive uncertainty which is crucial in safety-critical applications. Yet for many machine learning (ML) algorithms, it is difficult to construct or implement their Bayesian counterpart. In this work we present a promising approach to address this challenge, based on the hypothesis that commonly used ML algorithms are efficient across a wide variety of tasks and may thus be near Bayes-optimal w.r. t. an unknown task distribution. We prove that it is possible to recover the Bayesian posterior defined by the task distribution, which is unknown but optimal in this setting, by building a martingale posterior using the algorithm. We further propose a practical uncertainty quantification method that apply to general ML algorithms. Experiments based on a variety of non-NN and NN algorithms demonstrate the efficacy of our method.
Generation of Brain Microvascular Endothelial-Like Cells from Human Induced Pluripotent Stem Cells by Co-Culture with C6 Glioma Cells

The blood brain barrier (BBB) is formed by brain microvascular endothelial cells (BMECs) and tightly regulates the transport of molecules from blood to neural tissues. In vitro BBB models from human pluripotent stem cell (PSCs)-derived BMECs would be useful not only for the research on the BBB development and function but also for drug-screening for neurological diseases. However, little is known about the differentiation of human PSCs to BMECs. In the present study, human induced PSCs (iPSCs) were differentiated into endothelial cells (ECs), and further maturated to BMECs. Interestingly, C6 rat glioma cell-conditioned medium (C6CM), in addition to C6 co-culture, induced the differentiation of human iPSC-derived ECs (iPS-ECs) to BMEC-like cells, increase in the trans-endothelial electrical resistance, decreased in the dextran transport and up-regulation of gene expression of tight junction molecules in human iPS-ECs. Moreover, Wnt inhibitors attenuated the effects of C6CM. In summary, we have established a simple protocol of the generation of BMEC-like cells from human iPSCs, and have demonstrated that differentiation of iPS-ECs to BMEC-like cells is induced by C6CM-derived signals, including canonical Wnt signals.
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation

In the field of 3D object detection for autonomous driving, LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still, LiDAR is relatively high cost, which hinders adoption of this technology for consumer automobiles. Alternatively, camera and radar are commonly deployed on vehicles already on the road today, but performance of Camera-Radar (CR) fusion falls behind LC fusion. In this work, we propose Camera-Radar Knowledge Distillation (CRKD) to bridge the performance gap between LC and CR detectors with a novel cross-modality KD framework. We use the Bird's-Eye-View (BEV) representation as the shared feature space to enable effective knowledge distillation. To accommodate the unique cross-modality KD path, we propose four distillation losses to help the student learn crucial features from the teacher model. We present extensive evaluations on the nuScenes dataset to demonstrate the effectiveness of the proposed CRKD framework. The project page for CRKD is https://song-jingyu.github. io/CRKD.
The review of Aneuraceae in the Russian Far East

Aneuraceae are revised for the Russian Far East where its taxonomic diversity is the highest among Russian macroregions and concentrated in amphioceanic he­ mi boreal and temperate communities of the area. The distribution of 2 species of Aneura and 6 species of Riccardia is confirmed in the Far East and one more could be expected there. All known taxa are annotated with morphological des­ crip tion (based mostly or entirely on the material collected within the Russian Far East), ecology and distribution overviews and differentiation features. Oil bodies features are photographed based on materials from treated area. K e y w o r d s : Aneuraceae, Riccardia, Aneura, Hepaticae, taxonomy, distribution, the Rus­ sian Far East, East Asia
Chapter 1 REAL ALGEBRAIC GEOMETRY AND CONSTRAINT DATABASES

The constraint database model can be seen as a generalization of the classical relational database model that was introduced by Codd in the 1970s to deal with the management of alpha-numerical data, typically in business applications (Codd, 1970). A relational database can be viewed as a finite collection of tables or relations that each contain a finite number of tuples. Fig. 13.1 shows an instance of a relational database that contains the two relations Beer and Pub. This database contains tourist information about beers and the pubs where they are served. It also contains the location of the pubs, given in (x, y)-coordinates on some tourist map. Each relation contains a finite number of tuples. Arelational database is usually modeled following a database schema. Aschema contains information on the relation names and on the names of the attributes appearing in relation. In this example, the attributes of Beer are Name, Pub, City and Postal code. The complete schema of the relational database of Fig. 13.1 could be written as Beer(Name, Pub, City, Postal code), Pub(Pub, x, y).
Note on Klein-Nishina effect in strong-field QED: the case of nonlinear Compton scattering

Suitably normalized differential probabilities of one-photon emission in external electromagnetic fields are compared to quantify the transit of nonlinear Compton scattering to linear Compton scattering, described by the Klein-Nishina formula, and to constant crossed field treatment. The known Klein-Nishina suppression at large energies is further enforced by increasing field intensity. In view of the Ritus-Narozhny conjecture, we demonstrate that different paths in the field intensity vs. energy plane towards large values of the quantum non-linearity parameter $\chi$ facilitate significantly different asymptotic dependencies, both in the Klein-Nishina regime and the constant crossed field regime and in between.
The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Game

This investigation examined the relationships among scene complexity, workload, presence, and cybersickness in virtual reality (VR) environments. Numerous factors can influence the overall VR experience, and existing research on this matter is not yet conclusive, warranting further investigation. In this between-subjects experimental setup, 44 participants engaged in the Pendulum Chair game, with half exposed to a simple scene with lower optic flow and lower familiarity, and the remaining half to a complex scene characterized by higher optic flow and greater familiarity. The study measured the dependent variables workload, presence, and cybersickness and analyzed their correlations. Equivalence testing was also used to compare the simple and complex environments. Results revealed that despite the visible differences between the environments, within the 10% boundaries of the maximum possible value for workload and presence, and 13.6% of the maximum SSQ value, a statistically significant equivalence was observed between the simple and complex scenes. Additionally, a moderate, negative correlation emerged between workload and SSQ scores. The findings suggest two key points: (1) the nature of the task can mitigate the impact of scene complexity factors such as optic flow and familiarity, and (2) the correlation between workload and cybersickness may vary, showing either a positive or negative relationship.
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search

Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies, capturing network characteristics related to the final performance. However, network rankings estimated by previous training-free NAS methods have shown weak correlations with the performance. To address this issue, we propose AZ-NAS, a novel approach that leverages the ensemble of various zero-cost proxies to enhance the correlation between a predicted ranking of networks and the ground truth substantially in terms of the performance. To achieve this, we introduce four novel zero-cost proxies that are complementary to each other, analyzing distinct traits of architectures in the views of expressivity, progressivity, trainability, and complexity. The proxy scores can be obtained simultaneously within a single forward and backward pass, making an overall NAS process highly efficient. In order to integrate the rankings predicted by our proxies effectively, we introduce a non-linear ranking aggregation method that highlights the networks highly-ranked consistently across all the proxies. Experimental results conclusively demonstrate the efficacy and efficiency of AZ-NAS, outperforming state-of-the-art methods on standard benchmarks, all while maintaining a reasonable runtime cost.
Advances in independent component analysis with applications to data mining

This thesis considers the problem of finding latent structure in high dimensional data. It is assumed that the observed data are generated by unknown latent variables and their interactions. The task is to find these latent variables and the way they interact, given the observed data only. It is assumed that the latent variables do not depend on each other but act independently. A popular method for solving the above problem is independent component analysis (ICA). It is a statistical method for expressing a set of multidimensional observations as a combination of unknown latent variables that are statistically independent of each other. Starting from ICA, several methods of estimating the latent structure in different problem settings are derived and presented in this thesis. An ICA algorithm for analyzing complex valued signals is given; a way of using ICA in the context of regression is discussed; and an ICA-type algorithm is used for analyzing the topics in dynamically changing text data. In addition to ICA-type methods, two algorithms are given for estimating the latent structure in binary valued data. Experimental results are given on all of the presented methods. Another, partially overlapping problem considered in this thesis is dimensionality reduction. Empirical validation is given on a computationally simple method called random projection: it does not introduce severe distortions in the data. It is also proposed that random projection could be used as a preprocessing method prior to ICA, and experimental results are shown to support this claim. This thesis also contains several literature surveys on various aspects of finding the latent structure in high dimensional data.
Oral Immunotherapy for Food Allergy: Towards a New Horizon

Food allergy has increased dramatically in prevalence over the past decade in westernized countries, and is now a major public health problem. Unfortunately for patients with food allergy, there is no effective therapy beyond food allergen avoidance, and rapid medical treatment for accidental exposures. Recently, oral immunotherapy (OIT) has been investigated as a treatment for this problem. In this review, we will discuss the progress in developing OIT for food allergy, including a novel approach utilizing Xolair (anti-IgE monoclonal antibody, omalizumab) in combination with OIT. This combination may enhance both the safety and efficacy of oral immunotherapy, and could lead to a widely available and safe therapy for food allergy.
Generative Quanta Color Imaging

The astonishing development of single-photon cameras has created an unprecedented opportunity for scientific and industrial imaging. However, the high data throughput generated by these 1-bit sensors creates a significant bottleneck for low-power applications. In this paper, we explore the possibility of generating a color image from a single binary frame of a single-photon camera. We evidently find this problem being particularly difficult to standard colorization approaches due to the substantial degree of exposure variation. The core innovation of our paper is an exposure synthesis model framed under a neural ordinary differential equation (Neural ODE) that allows us to generate a continuum of exposures from a single observation. This innovation ensures consistent exposure in binary images that colorizers take on, resulting in notably enhanced colorization. We demonstrate applications of the method in single-image and burst colorization and show superior generative performance over baselines. Project website can be found at https://vishal-s-p.github. io/projects/2023/generative_quanta_color. html.