Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep transfer learning for image classification: a survey

Deep neural networks such as convolutional neural networks (CNNs) and transformers have achieved many successes in image classification in recent years. It has been consistently demonstrated that best practice for image classification is when large deep models can be trained on abundant labelled data. However there are many real world scenarios where the requirement for large amounts of training data to get the best performance cannot be met. In these scenarios transfer learning can help improve performance. To date there have been no surveys that comprehensively review deep transfer learning as it relates to image classification overall. However, several recent general surveys of deep transfer learning and ones that relate to particular specialised target image classification tasks have been published. We believe it is important for the future progress in the field that all current knowledge is collated and the overarching patterns analysed and discussed. In this survey we formally define deep transfer learning and the problem it attempts to solve in relation to image classification. We survey the current state of the field and identify where recent progress has been made. We show where the gaps in current knowledge are and make suggestions for how to progress the field to fill in these knowledge gaps. We present a new taxonomy of the applications of transfer learning for image classification. This taxonomy makes it easier to see overarching patterns of where transfer learning has been effective and, where it has failed to fulfill its potential. This also allows us to suggest where the problems lie and how it could be used more effectively. We show that under this new taxonomy, many of the applications where transfer learning has been shown to be ineffective or even hinder performance are to be expected when taking into account the source and target datasets and the techniques used.
Development and validation of the Converging Lenses Concept Inventory for middle school physics education

Optics is a core field in the curricula of secondary physics education. In this study, we present the development and validation of a test instrument in the field of optics, the Converging Lenses Concept Inventory (CLCI). It can be used as a formative or a summative assessment of middle school students' conceptual understanding of image formation by converging lenses. The CLCI assesses: (1) the overall understanding of fundamental concepts related to converging lenses, (2) the understanding of specific concepts, and (3) students' propensity for difficulties within this topic. The initial CLCI consists of 16 multiple-choice items; however, one item was removed based on various quality checks. We validated the CLCI thoroughly with distractor analyses, classical test theory, item response theory, structural analyses, and analyses of students' total scores at different measurement points as quantitative approaches, as well as student interviews and an expert survey as qualitative approaches. The quantitative analyses are mostly based on a dataset of N = 318 middle school students who took the CLCI as a posttest. The student interviews were conducted with seven middle school students after they were taught the concepts of converging lenses. The expert survey included five experts who evaluated both individual items and the test as a whole. The analyses showed good to excellent results for the test instrument, corroborating the 15-item CLCI's validity and its compliance with the three foci outlined above.
Training Language Models with Language Feedback

Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work approaches the above issue by learning from a simple form of human evaluation: comparisons between pairs of model-generated task outputs. Comparison feedback conveys limited information about human preferences per human evaluation. Here, we propose to learn from natural language feedback, which conveys more information per human evaluation. We learn from language feedback on model outputs using a three-step learning algorithm. First, we condition the language model on the initial output and feedback to generate many refinements. Second, we choose the refinement with the highest similarity to the feedback. Third, we finetune a language model to maximize the likelihood of the chosen refinement given the input. In synthetic experiments, we first evaluate whether language models accurately incorporate feedback to produce refinements, finding that only large language models (175B parameters) do so. Using only 100 samples of human-written feedback, our learning algorithm finetunes a GPT-3 model to roughly human-level summarization ability.
Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores

Causal discovery, the learning of causality in a data mining scenario, has been of strong scientific and theoretical interest as a starting point to identify"what causes what? "Contingent on assumptions and a proper learning algorithm, it is sometimes possible to identify and accurately estimate a causal directed acyclic graph (DAG), as opposed to a Markov equivalence class of graphs that gives ambiguity of causal directions. The focus of this paper is in highlighting the identifiability and estimation of DAGs with general error distributions through a general sequential sorting procedure that orders variables one at a time, starting at root nodes, followed by children of the root nodes, and so on until completion. We demonstrate a novel application of this general approach to estimate the topological ordering of a DAG. At each step of the procedure, only simple likelihood ratio scores are calculated on regression residuals to decide the next node to append to the current partial ordering. The computational complexity of our algorithm on a p-node problem is O(pd), where d is the maximum neighborhood size. Under mild assumptions, the population version of our procedure provably identifies a true ordering of the underlying DAG. We provide extensive numerical evidence to demonstrate that this sequential procedure scales to possibly thousands of nodes and works well for high-dimensional data. We accompany these numerical experiments with an application to a single-cell gene expression dataset.
Nonlinear Initialization Methods for Low-Rank Neural Networks

We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-rank matrices. The most successful prior existing approach, spectral initialization, draws a sample from the initialization distribution for the full-rank setting and then optimally approximates the full-rank initialization parameters in the Frobenius norm with a pair of low-rank initialization matrices via singular value decomposition. Our method is inspired by the insight that approximating the function corresponding to each layer is more important than approximating the parameter values. We provably demonstrate that there is a significant gap between these two approaches for ReLU networks, particularly as the desired rank of the approximating weights decreases, or as the dimension of the inputs to the layer increases (the latter point holds when the network width is super-linear in dimension). Along the way, we provide the first provably efficient algorithm for solving the ReLU low-rank approximation problem for fixed parameter rank $r$ -- previously, it was unknown that the problem was computationally tractable to solve even for rank $1$. We also provide a practical algorithm to solve this problem which is no more expensive than the existing spectral initialization approach, and validate our theory by training ResNet and EfficientNet models (He et al., 2016; Tan&Le, 2019) on ImageNet (Russakovsky et al., 2015).
How to understand the Structure of Beta Functions in Six-derivative Quantum Gravity?

We extensively motivate the studies of higher-derivative gravities, and in particular we emphasize which new quantum features theories with six derivatives in their definitions possess. Next, we discuss the mathematical structure of the exact on the full quantum level beta functions obtained previously for three couplings in front of generally covariant terms with four derivatives (Weyl tensor squared, Ricci scalar squared and the Gauss-Bonnet scalar) in minimal six-derivative quantum gravity in $d=4$ spacetime dimensions. The fundamental role here is played by the ratio $x$ of the coupling in front of the term with Weyl tensors to the coupling in front of the term with Ricci scalars in the original action. We draw a relation between the polynomial dependence on $x$ and the absence/presence of enhanced conformal symmetry and renormalizability in the models where formally $x\to+\infty$ in the case of four- and six-derivative theories respectively.
On the cozero-divisor graphs associated to rings

Let $R$ be a ring with unity. The cozero-divisor graph of a ring $R$, denoted by $\Gamma'(R)$, is an undirected simple graph whose vertices are the set of all non-zero and non-unit elements of $R$, and two distinct vertices $x$ and $y$ are adjacent if and only if $x \notin Ry$ and $y \notin Rx$. In this paper, first we study the Laplacian spectrum of $\Gamma'(\mathbb{Z}_n)$. We show that the graph $\Gamma'(\mathbb{Z}_{pq})$ is Laplacian integral. Further, we obtain the Laplacian spectrum of $\Gamma'(\mathbb{Z}_n)$ for $n = p^{n_1}q^{n_2}$, where $n_1, n_2 \in \mathbb{N}$ and $p, q$ are distinct primes. In order to study the Laplacian spectral radius and algebraic connectivity of $\Gamma'(\mathbb{Z}_n)$, we characterized the values of $n$ for which the Laplacian spectral radius is equal to the order of $\Gamma'(\mathbb{Z}_n)$. Moreover, the values of $n$ for which the algebraic connectivity and vertex connectivity of $\Gamma'(\mathbb{Z}_n)$ coincide are also described. At the final part of this paper, we obtain the Wiener index of $\Gamma'(\mathbb{Z}_n)$ for arbitrary $n$.
Default Distances Based on the CEV-KMV Model

This paper presents a new method to assess default risk based on applying the CEV process to the KMV model. We find that the volatility of the firm asset value may not be a constant, so we assume the firm's asset value dynamics are given by the CEV process $\frac{dV_A}{V_A} = \mu_A dt + \delta V_A^{\beta-1}dB$ and use the equivalent volatility method to estimate parameters. Focus on the distances to default, our CEV-KMV model fits the market better when forecasting the credit risk compared to the classical KMV model. Besides, The estimation results show the $\beta>1$ for non ST companies while $\beta<1$ for ST companies, which means their difference in the local volatility structure: ST volatility is decreasing with respect to the firm's asset while non ST volatility is increasing.
Serotonin 5‐HT1A and 5‐HT1B receptor‐mediated inhibition of glutamatergic transmission onto rat basal forebrain cholinergic neurones

Cholinergic neurones in the basal forebrain (BF) project into various brain regions and receive excitatory inputs from the cortex and brain stem. These cholinergic neurones receive serotonergic fibres from the dorsal raphe nuclei. This study was aimed to elucidate serotonin (5‐HT)‐induced modulation of glutamatergic transmission onto rat BF cholinergic neurones identified with Cy3‐192IgG. Excitatory postsynaptic currents (EPSCs) were evoked by focal stimulation. Bath application of either 5‐HT, the 5‐HT1A receptor agonist 8‐OH‐DPAT (DPAT), or the 5‐HT1B receptor agonist CP93129 (CP), inhibited the amplitude of EPSCs. In the presence of both 5‐HT1A and 5‐HT1B receptor antagonists, the 5‐HT‐induced effect disappeared. The paired‐pulse ratio (PPR) and coefficient of variation (CV) of the EPSCs were increased by CP, whereas DPAT had no effect on PPR or CV. DPAT inhibited the inward currents induced by puff application of l‐glutamate, which were unaffected by CP. DPAT suppressed the amplitude of miniature EPSCs (mEPSCs) without affecting their frequency. CP decreased the frequency of mEPSCs in more than half of the neurones examined, whereas the amplitude was unaffected. DPAT or CP alone inhibited the NMDA receptor‐mediated currents. 5‐HT‐induced inhibition of EPSCs was reduced in the presence of ω‐agatoxin TK (Aga). Furthermore, CP‐induced inhibition of EPSCs was eliminated in the presence of Aga. DPAT‐induced inhibition of EPSCs was unchanged in the presence of Aga. These results suggest that activation of 5‐HT1A receptors reduces the sensitivity of postsynaptic glutamate receptors to glutamate, whereas presynaptic activation of 5‐HT1B receptors inhibits glutamate release by blocking P/Q‐type calcium channels.
Laboratory evaluation of the compatibility of Beauveria bassiana with the egg parasitoid Trichogramma dendrolimi (Hymenoptera: Trichogrammatidae) for joint application against oriental fruit moth, Grapholita molesta (Lepidoptera: Tortricidae).

BACKGROUND The entomopathogenic fungus Beauveria bassiana and the egg parasitoid Trichogramma dendrolimi can both contribute to biological control of the global fruit borer, Grapholita molesta. To derive insights for optimizing their combined application in augmentation programs, we assayed fungal pathogenicity to both G. molesta (eggs, larvae and adults) and adult parasitoids, and assessed wasp acquisition and transmission of fungal spores following their emergence from B. bassiana-treated host eggs. RESULTS Concentrations of 108 conidia mL-1 of B. bassiana and higher caused virtually complete mortality of G. molesta larvae, but less than 30% mortality of eggs, and female moths surviving treatments had reduced fecundity. More than 80% of adult T. dendrolimi survived B. bassiana treatments of 107 conidia mL-1 , and more than 60% survived 109 conidia mL-1 , with surviving females achieving reproductive success equivalent to controls. Parasitoid females preferred healthy eggs over B. bassiana-infected ones in choice tests; wasps emerging from treated eggs carried about 104 conidia each, and contributed an additional 11% host mortality in trials combining both agents. CONCLUSION Our results indicate that combined applications of B. bassiana and T. dendrolimi can have complimentary impacts on G. molesta, the wasps compensating for low fungal pathogenicity to eggs, and their progeny potentially aiding in subsequent disease transmission. Although foraging wasps tended to avoid infected eggs, negative interactions between the two agents might be further mitigated by timing B. bassiana applications from late instar larva to early moth stage, and T. dendrolimi releases several days later to coincide with peak oviposition periods. This article is protected by copyright. All rights reserved.
Effects of Intracranial Interventional Embolization and Intracranial Clipping on the Cognitive and Neurologic Function of Patients with Intracranial Aneurysms.

BACKGROUND Intracranial interventional embolization and intracranial clipping have been two typical therapies for the emergent rescue of intracranial aneurysm. However, there are still controversies over the impact of these two surgical treatments of aneurysms on cognitive and neurological functions of patients. METHODS A total of 144 patients with intracranial aneurysms were enrolled as the test subjects, who were randomly and evenly divided into the Intracranial Clipping group and the Interventional Embolization group. Cognitive and neurologic functions were evaluated by Glasgow Outcome Scale, Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE) scales, National Institutes of Health Stroke Scale (NIHSS) and Activities of Daily Living (ADL) scale. Enzyme-linked immunosorbent assay was used to analyze the serum levels of neuron-specific enolase (NSE) and S100β. RESULTS There were no significant differences in the preoperative MMSE, MoCA, NIHSS or ADL scale between two groups (p > 0.05). However, after operation, the MMSE and MoCA scores of the interventional embolization group were significantly higher, whereas the NIHSS and ADL scales were significantly lower than those of the intracranial clipping group (p < 0.05). The levels of NSE and S100β in the intracranial clipping group were significantly higher than in the interventional embolization group. CONCLUSION Intracranial interventional embolization exerts better effects on the cognitive and neurologic functions than intracranial clipping.
Exploring the Role of Self-Nanoemulsifying Systems in Drug Delivery: Challenges, Issues, Applications and Recent Advances.

Nanotechnology has attracted researchers around the globe owing to the small size and targeting properties of the drug delivery vectors. The interest in self-nanoemulsifying drug delivery systems (SNEDDS) has shown an exponential increase from the formulator's point of view. SNEDDS have shown wide applicability in terms of controlled and targeted delivery of various types of drugs. They chemically consist of oil, surfactants and co-surfactants that decrease the emulsion particle size to the range of <100 nm. However, stability issues such as drug precipitation during storage, incompatibility of ingredients in shell, decrease their application for the long run and these issues have been highlighted in this paper. The current review throws limelight on the biological aspects and process parameters. In addition, the process of absorption from GI is also discussed in detail. SNEDDS have been utilized as a treatment option for various diseases like cancer, diabetes, ocular and pulmonary diseases. Along with this, the authors highlight the advances involving in vivo and in vitro lipolysis studies on SNEDDS, also highlighting recent innovations in this field, such as novel combinations of drug-free solid SNEDDS + solid dispersions, lipid-modified chitosan containing mucoadhesive SNEDDS, pH-sensitive SNEDDS and several others.
Automated Opportunistic Osteoporosis Screening in Routine Computed Tomography of the Spine: Comparison With Dedicated Quantitative CT

Opportunistic osteoporosis screening in nondedicated routine computed tomography (CT) is of increasing importance. The purpose of this study was to compare lumbar volumetric bone mineral density (vBMD) assessed by a convolutional neural network (CNN)‐based framework in routine CT to vBMD from dedicated quantitative CT (QCT), and to evaluate the ability of vBMD and surrogate measurements of Hounsfield units (HU) to distinguish between patients with and without osteoporotic vertebral fractures (VFs). A total of 144 patients (median age: 70.7 years, 93 females) with clinical routine CT (eight different CT scanners, 120 kVp or 140 kVp, with and without intravenous contrast medium) and dedicated QCT acquired within ≤30 days were included. Vertebral measurements included (i) vBMD from the CNN‐based approach including automated vertebral body labeling, segmentation, and correction of the contrast media phase for routine CT data (vBMD_OPP), (ii) vBMD from dedicated QCT (vBMD_QCT), and (iii) noncalibrated HU from vertebral bodies of routine CT data as previously proposed for immanent opportunistic osteoporosis screening based on CT attenuation. The intraclass correlation coefficient (ICC) for vBMD_QCT versus vBMD_OPP indicated better agreement (ICC = 0.913) than the ICC for vBMD_QCT versus noncalibrated HU (ICC = 0.704). Bland‐Altman analysis showed data points from 137 patients (95.1%) within the limits of agreement (LOA) of −23.2 to 25.0 mg/cm3 for vBMD_QCT versus vBMD_OPP. Osteoporosis (vBMD <80 mg/cm3) was detected in 89 patients (vBMD_QCT) and 88 patients (vBMD_OPP), whereas no patient crossed the diagnostic thresholds from normal vBMD to osteoporosis or vice versa. In a subcohort of 88 patients (thoracolumbar spine covered by imaging for VF reading), 69 patients showed one or more prevalent VFs, and the performance for discrimination between patients with and without VFs was best for vBMD_OPP (area under the curve [AUC] = 0.862; 95% confidence interval [CI], 0.771–0.953). In conclusion, automated opportunistic osteoporosis screening in routine CT of various scanner setups is feasible and may demonstrate high diagnostic accuracy for prevalent VFs. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
After the spawn and on the hook: Sea trout Salmo trutta biophysical responses to different components of catch and release in a coastal fishery

Abstract This study investigated the biophysical responses of sea run brown trout Salmo trutta to catch‐and‐release in the coastal fishery around Gotland, Sweden. It used information recorded on individual angled S. trutta (n = 162), including fight time, handling time, total air exposure time, injury, bleeding, fish length, body condition, spawning status, water temperature, hook location and difficulty of hook removal. Reflex action mortality predictors (equilibrium, operculum beats, tail grab response, body flex response and vestibular‐ocular response), tests of blood glucose and lactate, and observation of hooking injury were used to measure the relative impact of the angling event on the fish's physical state and stress experienced. The results of this study suggest low rates of post‐release mortality and generally limited stress responses to angling events, and relatively high post‐release survival supported by the recapture of many tagged S. trutta. However, a number of scenarios were identified in which stress responses are likely to be compounded and where anglers should take additional action to reduce sublethal physiological disturbances and the risk of delayed mortality. Particular care should be taken to limit cumulative total air exposure to <10 s, and to reduce handling time and risk of additional injury in angling events with extended fight times, when water temperatures >10°C, or where S. trutta show evidence of being physically compromised by injury or having recently spawned. The results also indicate the importance of using appropriately sized single hooks rather than larger treble hooks to reduce hooking injury and handling time during unhooking.