Miracle-dz/UniFAR-FTUs huggingface dataset:
Placebo and nocebo in the treatment of migraine: How much does real world effectiveness depend on contextual effects?

Purpose Treatments in medicine impact individuals beyond their intended effects, due to phenomena such as the placebo and nocebo effects. The placebo effect arises from the positive expectation of a treatment being beneficial, while the nocebo effect stems from the negative expectation of a treatment causing harm. Both in real-world practice and clinical trials, treatments can lead to outcomes unrelated to their intended mechanism of action, which we categorize as placebo and nocebo responses. These responses, combined with the inherent fluctuation in a condition's natural progression, regression to the mean, and random comorbidities, make up a significant part of the therapeutic experience. Particularly in pain management, placebo and nocebo effects play a substantial role. By addressing modifiable contextual factors such as patient expectations, lifestyle choices, and the therapeutic relationship, healthcare providers can enhance the effectiveness of migraine treatments, paving the way for a more comprehensive, individualized approach to patient care. We must also consider non-modifiable factors like personal experiences, beliefs, and information from social media and the internet. Conclusion This review offers a summary of our current understanding of the placebo and nocebo effects in migraine management.
The multiphase dolomitization of the Liassic Calcare Massiccio and Corniola successions (Montagna dei Fiori, Northern Apennines, Italy)

In this work petrographic and geochemical analyses allowed us to construct a genetic model of the pervasive dolomitization in the reference section of the Castel Manfrino Dolostones which includes the dolomitized Calcare Massiccio platform and the basinal Corniola sediments (Liassic). Cathodoluminescence and SEM analysis indicates a multiphase dolomitization: an initial calcite replacement (Dolomite 1) followed by recrystallization (Dolomite 2) and cementation events (Dolomite 3), and precipitation of saddle dolomite (Dolomite 4). Carbon and oxygen isotopic data suggest that dolostones underwent several recrystallization and stabilization events under increasing temperatures, in the presence of sea-water derived fluids. Fluid inclusion analyses established that Dolomite 3 took place at temperatures of around 70 degrees C, and Dolomite 4 precipitated at temperatures up to 132 degrees C. The fluid inclusions show salinity values for Dolomite 4 (average 16% NaCl equivalent) higher than Dolomite 3 (average 12%) suggesting a change in fluid geochemistry through time. The burial history, reconstructed from regional data and the succession outcropping in the Montagna dei Fiori area, enables us to match the diagenetic phases with the geological evolution of the area. The shallow burial phase of dolomitization (Dolomite 1) could have occurred during burial from the Upper Jurassic to the Middle Cretaceous. The second dolomitization phase (Dolomite 3), took place starting from the Middle Miocene, when the Lower Jurassic series reached temperatures of about 70 degrees C. After this period the area was subject to the Apennine thrusting, and hydrothermal fluids (with temperatures of 110-130 degrees C) arriving from deep seated faults are believed to have been the dolomitizing fluids for the last event (Dolomite 4).
Prognostic value of C-reactive protein in dogs with elevated serum pancreatic lipase immunoreactivity concentrations.

OBJECTIVE To determine the prognostic value of serum C-reactive protein (CRP) in dogs with pancreatitis. ANIMALS 503 client-owned animals with pancreatic lipase immunoreactivity (PLI) > 600 µg/L. METHODS Routine submissions to the Texas A&M Gastrointestinal Laboratory were monitored for canine samples with PLI > 600 µg/L. Clinics were emailed 2 weeks after PLI measurement and asked the following questions: (1) was the dog hospitalized, and (2) is the patient alive? If a response was received, serum CRP concentration was measured using leftover serum. RESULTS Paired PLI and CRP results were available for 503 dogs. Median PLI was 984 µg/L (range, 603 to 2,001 µg/L); median CRP was 9.9 mg/L (range, 9.9 to 395.3 mg/L; ref: < 10 mg/L). Inpatient care was provided to 136 dogs (27.0%); 49 dogs (9.7%) died or were euthanized. Median PLI values for dogs that died versus survived were similar. Median CRP was higher in hospitalized dogs (36.1 vs 9.9 mg/L; P < .0001) and those that died (37.2 vs 9.9 mg/L; P < .0001). Compared to dogs with CRP < 10 mg/L, those with CRP > 10 mg/L were 5.3 times more likely to die (CI, 2.7 to 10.2) and 5.7 times (CI, 3.7 to 8.7) more likely to be hospitalized. CLINICAL RELEVANCE In dogs with PLI > 600 µg/L, CRP > 10 mg/L was associated with increased risk of hospitalization or death. This biomarker may provide prognostic information in dogs with evidence of pancreatitis and guide decisions regarding hospitalization or referral.
CNS Efficacy of Osimertinib With or Without Chemotherapy in Epidermal Growth Factor Receptor–Mutated Advanced Non–Small-Cell Lung Cancer

PURPOSE We report CNS efficacy of first-line osimertinib plus chemotherapy versus osimertinib monotherapy in patients with epidermal growth factor receptor (EGFR)–mutated advanced non–small-cell lung cancer (NSCLC) from the phase III FLAURA2 study according to baseline CNS metastasis status. METHODS Patients were randomly assigned to osimertinib plus platinum-pemetrexed (combination) or osimertinib monotherapy until disease progression or discontinuation. Brain scans were performed in all patients at baseline and progression and at scheduled assessments until progression for patients with baseline CNS metastases; scans were assessed by neuroradiologist CNS blinded independent central review (BICR). RESULTS On the basis of baseline CNS BICR, 118 of 279 (combination) and 104 of 278 (monotherapy) randomly assigned patients had ≥one measurable and/or nonmeasurable CNS lesion and were included in the CNS full analysis set (cFAS); 40 of 118 and 38 of 104 had ≥one measurable target CNS lesion and were included in the post hoc CNS evaluable-for-response set (cEFR). In the cFAS, the hazard ratio (HR) for CNS progression or death was 0.58 (95% CI, 0.33 to 1.01). In patients without baseline CNS metastases, the HR for CNS progression or death was 0.67 (95% CI, 0.43 to 1.04). In the cFAS, CNS objective response rates (ORRs; 95% CI) were 73% (combination; 64 to 81) versus 69% (monotherapy; 59 to 78); 59% versus 43% had CNS complete response (CR). In the cEFR, CNS ORRs (95% CI) were 88% (73 to 96) versus 87% (72 to 96); 48% versus 16% had CNS CR. CONCLUSION Osimertinib plus platinum-pemetrexed demonstrated improved CNS efficacy compared with osimertinib monotherapy, including delaying CNS progression, irrespective of baseline CNS metastasis status. These data support this combination as a new first-line treatment for patients with EGFR-mutated advanced NSCLC, including those with CNS metastases.
Coseismic and other short-term strain changes recorded with Sacks-Evertson strainmeters in a deep mine, South Africa

Summary. During 1977 March and April, three Sacks-Evertson borehole dilatometers were installed at the ends of boreholes drilled into the sidewall of an experimental tunnel at a depth of 3.1 km in the ERPM gold mine near Johannesburg. In the following year coseismic strain changes ranging from 5 ± 10−10 to values exceeding 5 ± 10−6 were recorded for hundreds of mine tremors in the magnitude range -1 to 3.7 and at hypocentral distances of 50 m to about 2 km. Hypocentral coordinates and magnitudes were determined from seismograms recorded from an underground array of geophones. Amplitudes and polarities of the coseismic strain steps are generally in excellent agreement with theoretical expectations based on point-source dislocation theory; specifically, the strain steps are proportional to the seismic moment divided by the cube of hypocentral distance. At a strain level of 5 ± 10−9 or greater the tremors do not appear to be preceded by any short-term indications of instability even for tremors producing coseismic steps greater than 5 ± 10−6 and for which the strainmeters were within a source radius of the hypocentre. Continuous strain changes observed at the times when the mine excavation, at a distance of about 100 m, is extended are in good agreement with calculated changes based on the theory of elasticity. A similar calculation is consistent with post-seismic strain changes observed to follow some of the closer tremors. These post-seismic strains show a logarithmic dependence on time following the tremor and appear to be due to the interaction of a tremor with the adjacent mine excavation rather than to deformation within the actual seismic source region.
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots. Slot-based auto-encoders stand out as a prominent method for this task. Within them, crucial aspects include guiding the encoder to generate object-specific slots and ensuring the decoder utilizes them during reconstruction. This work introduces two novel techniques, (i) an attention-based self-training approach, which distills superior slot-based attention masks from the decoder to the encoder, enhancing object segmentation, and (ii) an innovative patch-order permutation strategy for autoregressive transformers that strengthens the role of slot vectors in reconstruction. The effectiveness of these strategies is showcased experimentally. The combined approach significantly surpasses prior slot-based autoencoder methods in unsupervised object segmentation, especially with complex real-world images. We provide the implementation code at https://github.com/gkakogeorgiou/spot .
Eliminating confounder-induced bias in the statistics of intervention

Experimental and observational studies often lead to spurious association between the outcome and independent variables describing the intervention, because of confounding to third-party factors. Even in randomized clinical trials, confounding might be unavoidable due to small sample sizes. Practically, this poses a problem, because it is either expensive to re-design and conduct a new study or even impossible to alleviate the contribution of some confounders due to e.g. ethical concerns. Here, we propose a method to consistently derive hypothetical studies that retain as many of the dependencies in the original study as mathematically possible, while removing any association of observed confounders to the independent variables. Using historic studies, we illustrate how the confounding-free scenario re-estimates the effect size of the intervention. The new effect size estimate represents a concise prediction in the hypothetical scenario which paves a way from the original data towards the design of future studies.
ECOLOGICAL OBSERVATIONS ON SOME SPECIES OF BATS AT HUAHONGDONG CAVE IN KUNMING, YUNNAN

1. Miniopterus sckreibersi, Ia kngimana and Hipposideros armiger are active all night, while Myotis fimbriatus, M. ricketti, M, laniger, Rhinolophus ferrumequi-nmn and R. rex are active chiefly in early mornings (4 to 7 o'clock a.m.) and evenings (7 to 12 o'clock p.m.). 2. The beginning of activities of those bats is rather accurate in time with variations of less than 10 minutes in the same season. The stimulative signal in initiating their activities is the general equivalence of temperature and humidity in and out of the caves.3. At the Hua-Hong-Tong region of Kunming, M. fimbriatus, M. ricketti, E. ferrumequinum, B. rex and B. pearsoni are endemic species, while H. armiger, M. schreibersi and Ia longimana, are migratory. These migrating species immigrate to Hua-Hong-Tong Cave in March and April and emigrate off in November and December.4. The density of populations varies with seasons. The maximum is in September and the minimum from January to March.5. During the period of giving births and feeding young, the male group and female group of H. armiger are dwelling in different caves. The two sexes of M. schreibersi behave in different ways. After the copulation season (August), most of the male bats perch individually, while most females dwell in groups.
MoSi Superconducting Nanowire Single-Photon Detectors on GaAs for On-Chip Integration

We report on MoSi-based superconducting nanowire single-photon detectors on a gallium arsenide substrate. MoSi deposited on a passivated GaAs surface has the same critical temperature as MoSi deposited on silicon. The critical temperature decreases slightly on depositing MoSi directly on the native oxide of GaAs. Hence, MoSi works well as a thin-film superconductor on GaAs. We propose that the amorphous structure of MoSi ensures compatibility with the GaAs matrix. Superconducting nanowire single-photon detectors (SNSPDs) are fabricated with MoSi on GaAs using a meander-wire design. The SNSPD metrics are very similar to those of devices fabricated with the same procedure on a silicon substrate. We observe a plateau in the response-versus-bias curve signalling a saturated internal quantum efficiency. The plateau remains even at an elevated temperature, 2.2 K, at a wavelength of 980 nm. We achieve a timing jitter of 50 ps and a recovery time of 29 ns. These results point to the promise of integrating MoSi SNSPDs with GaAs photonic circuits.
A Stress-controlled Erosion Apparatus for Studying Internal Erosion in Soils

Suffusion in soil involves selective erosion of fine particles within the matrix of coarse soil particles under seepage flow. Such loss of fine particles affects the hydraulic and mechanical behavior of the soil. In this study, a stress-controlled erosion apparatus was developed to investigate the initiation and development of suffusion under complex stress states and to study the effect of suffusion on soil stress-strain behavior. The apparatus allows independent control of hydraulic gradient and stress state. The hydraulic gradient is controlled using a water-head control method. The eroded soil and the outflow rate are measured using a soil collection system and a water collection system, respectively. The measurements can be used to study the erosion rate and variations in soil permeability during the erosion process. A series of erosion tests was conducted on a gap-graded soil under the same confining stress but different deviatoric stresses. The results show that the maximum erosion rate, the variations in soil permeability, and the total deformation of the soil specimen increase with the increase of deviatoric stress. After the loss of a significant amount of fine particles in the soil, the stress-strain behavior of the test soil changes from dilative behavior to contractive behavior.
Developmental Pretraining (DPT) for Image Classification Networks

In the backdrop of increasing data requirements of Deep Neural Networks for object recognition that is growing more untenable by the day, we present Developmental PreTraining (DPT) as a possible solution. DPT is designed as a curriculum-based pre-training approach designed to rival traditional pre-training techniques that are data-hungry. These training approaches also introduce unnecessary features that could be misleading when the network is employed in a downstream classification task where the data is sufficiently different from the pre-training data and is scarce. We design the curriculum for DPT by drawing inspiration from human infant visual development. DPT employs a phased approach where carefully-selected primitive and universal features like edges and shapes are taught to the network participating in our pre-training regime. A model that underwent the DPT regime is tested against models with randomised weights to evaluate the viability of DPT.
Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection

Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based on the current MRI, with the objective of reducing screening burden and facilitating early detection. An AI algorithm was developed on 53,858 breasts from 12,694 patients who underwent screening or diagnostic MRI and accrued over 12 years, with 2,331 confirmed cancers. A first U-Net was trained to segment lesions and identify regions of concern. A second convolutional network was trained to detect malignant cancer using features extracted by the U-Net. This network was then fine-tuned to estimate the risk of developing cancer within a year in cases that radiologists considered normal or likely benign. Risk predictions from this AI were evaluated with a retrospective analysis of 9,183 breasts from a high-risk screening cohort, which were not used for training. Statistical analysis focused on the tradeoff between number of omitted exams versus negative predictive value, and number of potential early detections versus positive predictive value. The AI algorithm identified regions of concern that coincided with future tumors in 52% of screen-detected cancers. Upon directed review, a radiologist found that 71.3% of cancers had a visible correlate on the MRI prior to diagnosis, 65% of these correlates were identified by the AI model. Reevaluating these regions in 10% of all cases with higher AI-predicted risk could have resulted in up to 33% early detections by a radiologist. Additionally, screening burden could have been reduced in 16% of lower-risk cases by recommending a later follow-up without compromising current interval cancer rate. With increasing datasets and improving image quality we expect this new AI-aided, adaptive screening to meaningfully reduce screening burden and improve early detection.
SCHEME: Scalable Channer Mixer for Vision Transformers

Vision Transformers have received significant attention due to their impressive performance in many vision tasks. While the token mixer or attention block has been studied in great detail, the channel mixer or feature mixing block (FFN or MLP) has not been explored in depth albeit it accounts for a bulk of the parameters and computation in a model. In this work, we study whether sparse feature mixing can replace the dense connections and confirm this with a block diagonal MLP structure that improves the accuracy by supporting larger expansion ratios. To improve the feature clusters formed by this structure and thereby further improve the accuracy, a lightweight, parameter-free, channel covariance attention (CCA) mechanism is introduced as a parallel branch during training. This design of CCA enables gradual feature mixing across channel groups during training whose contribution decays to zero as the training progresses to convergence. This allows the CCA block to be discarded during inference, thus enabling enhanced performance with no additional computational cost. The resulting $\textit{Scalable CHannEl MixEr}$ (SCHEME) can be plugged into any ViT architecture to obtain a gamut of models with different trade-offs between complexity and performance by controlling the block diagonal structure size in the MLP. This is shown by the introduction of a new family of SCHEMEformer models that is shown to establish new Pareto frontiers for accuracy vs FLOPS, accuracy vs model size, and accuracy vs throughput, especially for fast transformers of small model size. For example, the SCHEMEformer establishes a new SOTA of 79.7% accuracy for ViTs using pure attention mixers on ImageNet-1K at 1.77G FLOPs.
Deep Equilibrium Based Neural Operators for Steady-State PDEs

Data-driven machine learning approaches are being increasingly used to solve partial differential equations (PDEs). They have shown particularly striking successes when training an operator, which takes as input a PDE in some family, and outputs its solution. However, the architectural design space, especially given structural knowledge of the PDE family of interest, is still poorly understood. We seek to remedy this gap by studying the benefits of weight-tied neural network architectures for steady-state PDEs. To achieve this, we first demonstrate that the solution of most steady-state PDEs can be expressed as a fixed point of a non-linear operator. Motivated by this observation, we propose FNO-DEQ, a deep equilibrium variant of the FNO architecture that directly solves for the solution of a steady-state PDE as the infinite-depth fixed point of an implicit operator layer using a black-box root solver and differentiates analytically through this fixed point resulting in $\mathcal{O}(1)$ training memory. Our experiments indicate that FNO-DEQ-based architectures outperform FNO-based baselines with $4\times$ the number of parameters in predicting the solution to steady-state PDEs such as Darcy Flow and steady-state incompressible Navier-Stokes. Finally, we show FNO-DEQ is more robust when trained with datasets with more noisy observations than the FNO-based baselines, demonstrating the benefits of using appropriate inductive biases in architectural design for different neural network based PDE solvers. Further, we show a universal approximation result that demonstrates that FNO-DEQ can approximate the solution to any steady-state PDE that can be written as a fixed point equation.
Subsystem eigenstate thermalization hypothesis for translation invariant systems

The eigenstate thermalization hypothesis for translation invariant quantum spin systems has been proved recently by using random matrices. In this paper, we study the subsystem version of eigenstate thermalization hypothesis for translation invariant quantum systems without referring to random matrices. By showing the small upper bounds on the quantum variance or the Belavkin-Staszewski relative entropy, we prove the subsystem eigenstate thermalization hypothesis for translation invariant quantum systems with an algebraic speed of convergence in an elementary way.
Explainable Fraud Detection with Deep Symbolic Classification

There is a growing demand for explainable, transparent, and data-driven models within the domain of fraud detection. Decisions made by fraud detection models need to be explainable in the event of a customer dispute. Additionally, the decision-making process in the model must be transparent to win the trust of regulators and business stakeholders. At the same time, fraud detection solutions can benefit from data due to the noisy, dynamic nature of fraud and the availability of large historical data sets. Finally, fraud detection is notorious for its class imbalance: there are typically several orders of magnitude more legitimate transactions than fraudulent ones. In this paper, we present Deep Symbolic Classification (DSC), an extension of the Deep Symbolic Regression framework to classification problems. DSC casts classification as a search problem in the space of all analytic functions composed of a vocabulary of variables, constants, and operations and optimizes for an arbitrary evaluation metric directly. The search is guided by a deep neural network trained with reinforcement learning. Because the functions are mathematical expressions that are in closed-form and concise, the model is inherently explainable both at the level of a single classification decision and the model's decision process. Furthermore, the class imbalance problem is successfully addressed by optimizing for metrics that are robust to class imbalance such as the F1 score. This eliminates the need for oversampling and undersampling techniques that plague traditional approaches. Finally, the model allows to explicitly balance between the prediction accuracy and the explainability. An evaluation on the PaySim data set demonstrates competitive predictive performance with state-of-the-art models, while surpassing them in terms of explainability. This establishes DSC as a promising model for fraud detection systems.