Miracle-dz/UniFAR-FTUs huggingface dataset:
Toric Sylvester forms

In this paper, we investigate the structure of the saturation of ideals generated by sparse homogeneous polynomials over a projective toric variety $X$ with respect to the irrelevant ideal of $X$. As our main results, we establish a duality property and make it explicit by introducing toric Sylvester forms, under a certain positivity assumption on $X$. In particular, we prove that toric Sylvester forms yield bases of some graded components of $I^{\text{sat}}/I$, where $I$ denotes an ideal generated by $n+1$ generic forms, $n$ is the dimension of $X$ and $I^{\text{sat}}$ the saturation of $I$ with respect to the irrelevant ideal of the Cox ring of $X$. Then, to illustrate the relevance of toric Sylvester forms we provide three consequences in elimination theory over smooth toric varieties: (1) we introduce a new family of elimination matrices that can be used to solve sparse polynomial systems by means of linear algebra methods, including overdetermined polynomial systems; (2) by incorporating toric Sylvester forms to the classical Koszul complex associated to a polynomial system, we obtain new expressions of the sparse resultant as a determinant of a complex; (3) we explote a new formula for computing toric residues of the product of two forms.
Test-time Training for Data-efficient UCDR

Image retrieval under generalized test scenarios has gained significant momentum in literature, and the recently proposed protocol of Universal Cross-domain Retrieval is a pioneer in this direction. A common practice in any such generalized classification or retrieval algorithm is to exploit samples from many domains during training to learn a domain-invariant representation of data. Such criterion is often restrictive, and thus in this work, for the first time, we explore the generalized retrieval problem in a data-efficient manner. Specifically, we aim to generalize any pre-trained cross-domain retrieval network towards any unknown query domain/category, by means of adapting the model on the test data leveraging self-supervised learning techniques. Toward that goal, we explored different self-supervised loss functions~(for example, RotNet, JigSaw, Barlow Twins, etc.) and analyze their effectiveness for the same. Extensive experiments demonstrate the proposed approach is simple, easy to implement, and effective in handling data-efficient UCDR.
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.
A nonparametric framework for treatment effect modifier discovery in high dimensions

Heterogeneous treatment effects are driven by treatment effect modifiers, pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering these variables are limited to low-dimensional data, data with weakly correlated covariates, or data generated according to parametric processes. We resolve these issues by developing a framework for defining model-agnostic treatment effect modifier variable importance parameters applicable to high-dimensional data with arbitrary correlation structure, deriving one-step, estimating equation and targeted maximum likelihood estimators of these parameters, and establishing these estimators' asymptotic properties. This framework is showcased by defining variable importance parameters for data-generating processes with continuous, binary, and time-to-event outcomes with binary treatments, and deriving accompanying multiply-robust and asymptotically linear estimators. Simulation experiments demonstrate that these estimators' asymptotic guarantees are approximately achieved in realistic sample sizes for observational and randomized studies alike. This framework is applied to gene expression data collected for a clinical trial assessing the effect of a monoclonal antibody therapy on disease-free survival in breast cancer patients. Genes predicted to have the greatest potential for treatment effect modification have previously been linked to breast cancer. An open-source R package implementing this methodology, unihtee, is made available on GitHub at https://github.com/insightsengineering/unihtee.
Observation of orbital pumping

Harnessing spin and orbital angular momentum is a fundamental concept in condensed matter physics, materials science, and quantum-device applications. In particular, the search for new phenomena that generate a flow of spin angular momentum, a spin current, has led to the development of spintronics, advancing the understanding of angular momentum dynamics at the nanoscale. In contrast to this success, the generation and detection of orbital currents, the orbital counterpart of spin currents, remains a significant challenge. Here, we report the observation of orbital pumping, a phenomenon in which magnetization dynamics pumps an orbital current, a flow of orbital angular momentum. The orbital pumping is the orbital counterpart of the spin pumping, which is one of the most versatile and powerful mechanisms for spin-current generation. We show that the orbital pumping in Ni/Ti bilayers injects an orbital current into the Ti layer, which is detected through the inverse orbital Hall effect. Our findings provide a promising approach for generating orbital currents and pave the way for exploring the physics of orbital transport in solids.
Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring

The aim of in-trawl catch monitoring for use in fishing operations is to detect, track and classify fish targets in real-time from video footage. Information gathered could be used to release unwanted bycatch in real-time. However, traditional multi-object tracking (MOT) methods have limitations, as they are developed for tracking vehicles or pedestrians with linear motions and diverse appearances, which are different from the scenarios such as livestock monitoring. Therefore, we propose a novel MOT method, built upon an existing observation-centric tracking algorithm, by adopting a new iterative association step to significantly boost the performance of tracking targets with a uniform appearance. The iterative association module is an extendable component that can be merged into most existing tracking methods. Our method offers improved performance in tracking targets with uniform appearance and outperforms state-of-the-art techniques on our underwater fish datasets as well as the MOT17 dataset, without increasing latency nor sacrificing accuracy as measured by HOTA, MOTA, and IDF1 performance metrics.
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predict future values. However, recently, there has been an emergence of methods that employ the Channel Independent (CI) strategy. These methods view multivariate time series data as separate univariate time series and disregard the correlation between channels. Surprisingly, our empirical results have shown that models trained with the CI strategy outperform those trained with the Channel Dependent (CD) strategy, usually by a significant margin. Nevertheless, the reasons behind this phenomenon have not yet been thoroughly explored in the literature. This paper provides comprehensive empirical and theoretical analyses of the characteristics of multivariate time series datasets and the CI/CD strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. In contrast, the CI approach trades capacity for robust prediction. Practical measures inspired by these analyses are proposed to address the capacity and robustness dilemma, including a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy. We hope our findings can raise awareness among researchers about the characteristics of multivariate time series and inspire the construction of better forecasting models.
Multi-step Jailbreaking Privacy Attacks on ChatGPT

With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. Though model developers and researchers work hard on dialog safety to avoid generating harmful content from LLMs, it is still challenging to steer AI-generated content (AIGC) for the human good. As powerful LLMs are devouring existing text data from various domains (e.g., GPT-3 is trained on 45TB texts), it is natural to doubt whether the private information is included in the training data and what privacy threats can these LLMs and their downstream applications bring. In this paper, we study the privacy threats from OpenAI's ChatGPT and the New Bing enhanced by ChatGPT and show that application-integrated LLMs may cause new privacy threats. To this end, we conduct extensive experiments to support our claims and discuss LLMs' privacy implications.
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, named M-ATTA and V-ATTA) based on test time augmentation, to improve the uncertainty calibration of deep models for image classification. By leveraging na adaptive weighting system, M/V-ATTA improves uncertainty calibration without affecting the model's accuracy. The performance of these techniques is evaluated by considering diverse metrics related to uncertainty calibration, demonstrating their robustness. Empirical results, obtained on CIFAR-10, CIFAR-100, Aerial Image Dataset, as well as in two different scenarios under distribution-shift, indicate that the proposed methods outperform several state-of-the-art post-hoc calibration techniques. Furthermore, the methods proposed also show improvements in terms of predictive entropy on out-of-distribution samples. Code for M/V-ATTA available at: https://github.com/pedrormconde/MV-ATTA
Adverse effects of methamphetamine on vital organs of male rats: Histopathological and immunohistochemical investigations

Objective(s): Methamphetamine (named crystal, ice, and crank), is a strong psychostimulant drug with addictive and neurotoxic properties. It is absorbed by various organs and induces tissue damage in abusers. Most METH studies have focused on the central nervous system and its effects on other organs have been neglected. Experimental investigations of animal models are used to provide significant additional information. We have studied the histopathological effects of methamphetamine in the brains, hearts, livers, testes, and kidneys of rats. Materials and Methods: Methamphetamine (0.5 mg/kg) was administered subcutaneously for 21 days. Immunohistochemistry was carried out with markers including glial fibrillary acidic protein (GFAP) for reactive astrocytes, vimentin as an intermediate filament in different cells, and CD45 marker for the detection of reactive microglia in the brain. Also, some samples were taken from livers, kidneys, hearts, and testes. Results: Degenerative changes and necrosis were the most common histopathological effects in the liver, kidneys, heart, testes, and brains of rats treated with methamphetamine. Immunohistochemical analyses by vimentin and GFAP markers revealed reactive microglia and astrocytes with the appearance of swollen cell bodies and also short, thickened, and irregular processes. Moreover, the number of CD45-positive cells was higher in this group. Reactive cells were more noticeable in the peduncles and subcortical white matter of the cerebellum. Conclusion: Our results showed the toxic effects of methamphetamine on the vital organs and induction of neurotoxicity, cardiomyopathy, renal damage, and infertility in male rats. We could not attribute observed hepatic changes to METH and further evaluation is needed.
ANGPTL4 suppresses the profibrogenic functions of atrial fibroblasts induced by angiotensin II by up-regulating PPARγ

Objective(s): The present study’s objective was to investigate the association between angiopoietin-like 4 (ANGPTL4) levels and the prognosis of Atrial fibrillation (AF), the causative effect in angiotensin II- (Ang II) induced AF, and its underlying mechanisms. Materials and Methods: Baseline serum ANGPTL-4 concentrations were measured in 130 patients with AF. Rat atrial fibroblasts were isolated from 14-day-old SD rats and transfected with Ang II treatment. Transfected cells were divided into: The control group, ANGPTL4-OE group, Ang II group, and Ang II+ANGPTL4-OE group. Transfected cells were used to analyze fibroblasts’ proliferation, migration, and collagen production at the cellular level. RT-qPCR and western blotting evaluated the ANGPTL4-targeted gene and PPARγ-Akt pathway. Results: In patients with AF, serum ANGPTL4 concentrations decreased significantly compared with the healthy group. ANGPTL4 mRNA and protein expressions were significantly down-regulated in Ang II-induced cardiac fibroblasts. ANGPTL4 overexpression potentially attenuated Ang IIinduced fibroblast proliferation, migration, and collagen production in atrial tissue. ANGPTL4 inhibited the signaling proteins, such as PPARγ, α-SMA, and Akt. Conclusion: Our experimental data speculate that ANGPTL4 is a key factor in regulating AF progression. Therefore, increasing ANGPTL4 expression could be an effective strategy for AF treatment.
LDH as an adjuvant makes Brucella outer-membrane vesicles and outer-membrane vesicle-associated proteins highly protective in mice

Objective(s): Existing Brucella vaccines are attenuated and can cause vaccine-associated brucellosis; and these safety concerns have affected their application. Although subunit vaccines have the advantages of safety, efficacy, low cost, and rapid production, they are usually poorly immunogenic and insufficient to trigger persistent immunity. Therefore, we added layered double hydroxide (LDH) as an adjuvant to Brucella subunit vaccine formulations to enhance the immune response to the antigen. Materials and Methods: LDH and Freund’s adjuvant were combined with Brucella outer-membrane vesicles (OMVs) and OMV-associated proteins to form a subunit vaccine, respectively. The immunogenicity of LDH as an adjuvant was assessed in BALB/c mice. We examined levels of immunoglobulin G, G1, and G2a (IgG, IgG1, and IgG2a) antibodies (aBs); percentages of Cluster of Differentiation 4-positive (CD4+) and CD8+ T cells in peripheral-blood lymphocytes; and secretion of cytokines in mouse spleen lymphocytes. Finally, splenic index and splenic bacterial load were assessed via Brucella challenge experiments on mice. Results: The LDH subunit vaccine also produced high levels of specific aBs in mice compared with Freund’s adjuvant subunit vaccine and induced mainly T-helper 1 cell (Th1)-type immune responses. In addition, mice in the LDH subunit vaccine group had significantly lower bacterial loads in their spleens than those in the Freund’s adjuvant subunit vaccine group, and the LDH-OMV vaccine offered a higher level of protection against Brucella attack. Conclusion: LDH as an adjuvant-paired vaccine provided a high level of protection against Brucella infection.
Relationship between Anger Expression in Mothers and Acceptance of Behavior Management Techniques in Pediatric Dentistry

Statement of the Problem: The parental acceptance rate of the behavior management methods used in pediatric dentistry is influenced by various factors. Purpose: This study was aimed to investigate the relationship between mothers’ anger expression and their acceptance of behavior management methods. Materials and Method: In this cross-sectional study, 110 mothers of children who had less than 12 years of age were recruited. They completed Spielberg’s state-trait anger expression inventory (STAXI). The respondents watched an educational video that included explanations of seven behavior management methods. Then, they reported their acceptance of each of these methods using the visual analog scale (VAS). Results: The acceptance rates of behavior management methods from the highest to lowest included tell-show-do, pre-appointment behavior shaping, mouthpiece, voice control, general anesthesia, active restraint, and passive restraint, respectively. There was only a significant inverse relationship between the acceptance of the tell-show-do method and the anger expression (p< 0.05). There was no significant relationship between the acceptance of behavioral management methods and the previous history of a pediatric dental visit, age, and mothers’ education. Conclusion: There was a significant inverse relationship between acceptance of the tell-show-do behavioral management method and mothers’ anger expression. However, there is no significant relationship between other methods and mothers’ anger expression. According to this study, there was a relation between mothers' ages and their acceptance of using a mouthpiece.
Solitary Plasmacytoma of the Mandible: A Case Report

Plasmacytoma is an abnormal proliferation of monoclonal B-cells, and it can occur in several forms including multiple myeloma, solitary plasmacytoma of the bone, and extramedullary plasmacytoma primary. The solitary plasmacytoma of the bone accounts for about 3-10% of all plasma cell neoplasms, and occurs most often in the vertebrae, whereas the solitary plasmacytoma of the mandible is an extremely rare occurrence. This case report presents a 61-year-old woman with various underlying diseases diagnosed with solitary plasmacytoma of the mandible. This case is very well documented with radiographic imagery, clinical, and histopathological findings.
Remark on Asymptotic Completeness for Nonlinear Klein-Gordon Equations with Metastable States

In this paper, we are in a further exploration of metastable states constructed by Soffer and Weinstein in [15]. These metastable states are the outcome of instabilities of excited states for Klein-Gordon equations under nonlinear Fermi golden rule. In [15], Soffer and Weinstein derived an upper bound ($1/(1+t)^{1/4}$) for an anomalously slow decay rate of these states. We derive the asymptotic behavior (including a lower bound $1/(1+t)^{1/4}$) and prove asymptotic completeness for current cases. This means that at very late stage ($t$ goes to $+\infty$) the solutions to this nonlinear Klein-Gordon equation could be written as the sum of free waves and error terms; and the $L^2_x$ norm of error terms will vanish eventually ($t$ goes to $+\infty$). Therefore, even though there is a lower bound for the anomalously slow decay rate for these states, asymptotic completeness still holds.
Neural synchrony reflects closure of jabberwocky noun phrases but not predictable pseudoword sequences

Successful language comprehension requires the combination of individual words into larger linguistic units. In the present minimal‐phrase study, we used electroencephalography (EEG) to investigate whether syntactic combination is indexed by changes in neural synchrony, while testing for both token‐based and type‐based effects. To do this, we analysed intertrial phase coherence (ITPC) elicited by reading two item (words or pseudowords) phrases that were either unifiable or nonunifiable. Results indicated that type‐based unifiable phrases elicited increased ITPC relative to all other conditions in the frequency band corresponding to the rate of phrases (0.5 Hz) but not the rate of words (1 Hz). Conversely, we observed a complementary pattern for the N400, which was more sensitive to token‐based effects. These findings provide evidence that the combination of single words into larger syntactic structures may be indexed by the synchronous firing of assemblies of neurons oscillating at the rate of phrases during reading.
Stability for 6 months and accuracy of a specific enteral caffeine base preparation for a multisite clinical trial

The ‘Intermittent Hypoxia and Caffeine in Infants Born Preterm (ICAF)’ study (NCT03321734) uses an extemporaneously compounded enteral caffeine base solution for its study drug. The primary aim of this report is to determine the stability of this specific enteral caffeine base preparation stored for up to 6 months and assess optimal storage temperature. To analyse stability, caffeine solutions were prepared and stored at 4°C and 25°C (room temperature). The caffeine concentrations were analysed over time using high‐performance liquid chromatography (HPLC). To confirm the accuracy of compounded caffeine concentrations, study drug samples from three research pharmacies were analysed. Stability results showed that caffeine concentrations are within 5% of the expected concentration when stored for up to 6 months at room temperature. Our results also show that accurate caffeine concentrations were achieved by multiple research pharmacies.
THE PERIODIC TABLE OF n-CATEGORIES II: DEGENERATE TRICATEGORIES

Nous continuons le travail commence en [5] en etudiant les tricategories degenerees et en les comparant avec les structures predites par le tableau periodique des n-categories. Pour les tricategories trois fois degenerees nous demontrons une triequivalence avec la tricategorie partiellement discrete des monoides commutatifs. Pour les tricategories deux fois degenerees nous expliquons comment on peut construire une categorie monoidale tressee d'une tricategorie deux fois degeneree donnee, mais nous demontrons que cette construction n'induit pas une comparaison simple entre BrMonCat et Tricat. Nous discutons comment on peut iterer la construction des "icones" pour produire un equivalence, mais nous esperons a la suite pour donner les details. Finalement nous etudions les tricategories degenerees pour donner la premiere definition de bicategorie monoidale completement algebrique et la structure entiere de tricategorie de MonBicat. We continue the project begun in [5] by examining degenerate tricategories and comparing them with the structures predicted by the Periodic table. For triply degenerate tricategories we exhibit a triequivalence with the partially discrete tricategory of commutative monoids. For the doubly degenerate case we explain how to construct a braided monoidal category from a given doubly degenerate category, but show that this does not induce a straightforward comparison between BrMonCat and Tricat. We indicate how to iterate the icon construction to produce an equivalence, but leave the details to a sequel. Finally we study degenerate tricategories in order to give the first fully algebraic definition of monoidal bicategories and the full tricategory structure MonBicat.