Miracle-dz/UniFAR-FTUs huggingface dataset:
FGFR2::TACC2 fusion as a novel KIT‐independent mechanism of targeted therapy failure in a multidrug‐resistant gastrointestinal stromal tumor

Genetic alterations in FGF/FGFR pathway are infrequent in gastrointestinal stromal tumors (GIST), with rare cases of quadruple wildtype GISTs harboring FGFR1 gene fusions and mutations. Additionally, FGF/FGFR overexpression was shown to promote drug resistance to kinase inhibitors in GISTs. However, FGFR gene fusions have not been directly implicated as a mechanism of drug resistance in GISTs. Herein, we report a patient presenting with a primary small bowel spindle cell GIST and concurrent peritoneal and liver metastases displaying an imatinib‐sensitive KIT exon 11 in‐frame deletion. After an initial 9‐month benefit to imatinib, the patient experienced intraabdominal peritoneal recurrence owing to secondary KIT exon 13 missense mutation and FGFR4 amplification. Despite several additional rounds of tyrosine kinase inhibitors (TKI), the patient's disease progressed after 2 years and presented with multiple peritoneal and liver metastases, including one pericolonic mass harboring secondary KIT exon 18 missense mutation, and a concurrent transverse colonic mass with a FGFR2::TACC2 fusion and AKT2 amplification. All tumors, including primary and recurrent masses, harbored an MGA c.7272 T > G (p.Y2424*) nonsense mutation and CDKN2A/CDKN2B/MTAP deletions. The transcolonic mass showed elevated mitotic count (18/10 HPF), as well as significant decrease in CD117 and DOG1 expression, in contrast to all the other resistant nodules that displayed diffuse and strong CD117 and DOG1 immunostaining. The FGFR2::TACC2 fusion resulted from a 742 kb intrachromosomal inversion at the chr10q26.3 locus, leading to a fusion between exons 1–17 of FGFR2 and exons 7–17 TACC2, which preserves the extracellular and protein tyrosine kinase domains of FGFR2. We present the first report of a multidrug‐resistant GIST patient who developed an FGFR2 gene fusion as a secondary genetic event to the selective pressure of various TKIs. This case also highlights the heterogeneous escape mechanisms to targeted therapy across various tumor nodules, spanning from both KIT‐dependent and KIT‐independent off‐target activation pathways.
Spreading Advantages of Coresident Plasmids blaCTX-M-Bearing IncFII and mcr-1-Bearing IncI2 in Escherichia coli

More and more Enterobacteriaceae carry both blaCTX-M and mcr-1, which are usually located on IncFII-type and IncI2-type plasmids in the same bacterial host, respectively. However, the study on advantages of coresident plasmids in bacterial host is still sparse. ABSTRACT Two diverse conjugative plasmids can interact within bacterial cells. However, to the best of our knowledge, the interaction between blaCTX-M-bearing IncFII plasmid and mcr-1-carrying IncI2 plasmid colocated on the same bacterial host has not been reported. This study was initiated to explore the interaction and to analyze the reasons that these two plasmids are often coresident in multidrug-resistant Escherichia coli. To assess the interactions on plasmid stabilities, fitness costs, and transfer rates, we constructed two groups of isogenic derivatives, C600FII, C600I2, and C600FII+I2 of E. coli C600 and J53FII, J53I2, and J53FII+I2 of E. coli J53, respectively. We found that carriage of FII and I2 plasmids, independently and together, had not impaired the growth of the bacterial host. It was difficult for the single plasmid FII or I2 in E. coli C600 to reach stable persistence for a long time in an antibiotic-free environment, while the stability would be striking improved when they coresided. Meanwhile, plasmids FII and I2, whether together or apart, could notably enhance the fitness advantage of the host; moreover, E. coli coharboring plasmids FII and I2 presented more obvious fitness advantage than that carrying single plasmid FII. Coresident plasmids FII and I2 could accelerate horizontal cotransfer by conjugation. The transfer rates from a strain carrying coresident FII and I2 plasmids increased significantly when it mated with a recipient cell carrying one of them. Our findings highlight the advantages of coinhabitant FII and I2 plasmids in E. coli to drive the persistence and spread of plasmid-carried blaCTX-M and mcr-1 genes, although the molecular mechanisms of their coresidence warrant further study. IMPORTANCE More and more Enterobacteriaceae carry both blaCTX-M and mcr-1, which are usually located on IncFII-type and IncI2-type plasmids in the same bacterial host, respectively. However, the study on advantages of coresident plasmids in bacterial host is still sparse. Here, we investigated the stability, fitness cost, and cotransfer traits associated with coresident IncFII-type and IncI2-type plasmids in E. coli. Our results show that coinhabitant plasmids in E. coli are more stable, confer more fitness advantages, and are easier to transfer and cotransfer than a single plasmid IncFII or IncI2. Our findings confirm the advantages of coresident plasmids of blaCTX-M-bearing IncFII and mcr-1-bearing IncI2 in clinical E. coli, which will pose a serious threat to clinical therapy and public health.
On Measuring Excess Capacity in Neural Networks

We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class - in our case, empirical Rademacher complexity - to what extent can we (a priori) constrain this class while retaining an empirical error on a par with the unconstrained regime? To assess excess capacity in modern architectures (such as residual networks), we extend and unify prior Rademacher complexity bounds to accommodate function composition and addition, as well as the structure of convolutions. The capacity-driving terms in our bounds are the Lipschitz constants of the layers and an (2, 1) group norm distance to the initializations of the convolution weights. Experiments on benchmark datasets of varying task difficulty indicate that (1) there is a substantial amount of excess capacity per task, and (2) capacity can be kept at a surprisingly similar level across tasks. Overall, this suggests a notion of compressibility with respect to weight norms, complementary to classic compression via weight pruning. Source code is available at https://github.com/rkwitt/excess_capacity.
Decoding the Bifurcated Red Giant Branch as a Tracer of Multiple Stellar Populations in the Young Large Magellanic Cloud Cluster NGC 2173

Multiple stellar populations (MPs) representing star-to-star light-element abundance variations are common in nearly all ancient Galactic globular clusters (GCs). Here we provide the strongest evidence yet that the populous, ∼1.7 Gyr old Large Magellanic Cloud cluster NGC 2173 also exhibits light-element abundance variations. Thus, our results suggest that NGC 2173 is the youngest cluster for which MPs have been confirmed to date. Our conclusion is based on the distinct bifurcation at the tip of its red giant branch in high-quality color–magnitude diagrams generated from Hubble Space Telescope imaging observations. Our results are further supported by a detailed analysis of “pseudo-UBI” maps, which reveal clear evidence of a bimodality in the cluster’s red giant branch color distribution. Young clusters in the Magellanic Clouds can provide critical insights into galaxy evolution histories. Our discovery of MPs in NGC 2173 suggests that ancient Galactic GCs and young massive clusters might share a common formation process.
Speech Denoising in the Waveform Domain With Self-Attention

In this work, we present CleanUNet, a causal speech denoising model on the raw waveform. The proposed model is based on an encoder-decoder architecture combined with several self-attention blocks to refine its bottleneck representations, which is crucial to obtain good results. The model is optimized through a set of losses defined over both waveform and multi-resolution spectrograms. The proposed method outperforms the state-of-the-art models in terms of denoised speech quality from various objective and subjective evaluation metrics.1
Quantum games and interactive tools for quantum technologies outreach and education

Abstract. We provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the quantum community in recent years. We present selected tools as described by their developers, including “Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and the Virtual Quantum Optics Laboratory. ” In addition, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population.
Privacy Preserving Visual Question Answering

We introduce a novel privacy-preserving methodology for performing Visual Question Answering on the edge. Our method constructs a symbolic representation of the visual scene, using a low-complexity computer vision model that jointly predicts classes, attributes and predicates. This symbolic representation is non-differentiable, which means it cannot be used to recover the original image, thereby keeping the original image private. Our proposed hybrid solution uses a vision model which is more than 25 times smaller than the current state-of-the-art (SOTA) vision models, and 100 times smaller than end-to-end SOTA VQA models. We report detailed error analysis and discuss the trade-offs of using a distilled vision model and a symbolic representation of the visual scene.
Meta Knowledge Distillation

Recent studies pointed out that knowledge distillation (KD) suffers from two degradation problems, the teacher-student gap and the incompatibility with strong data augmentations, making it not applicable to training state-of-the-art models, which are trained with advanced augmentations. However, we observe that a key factor, i.e., the temperatures in the softmax functions for generating probabilities of both the teacher and student models, was mostly overlooked in previous methods. With properly tuned temperatures, such degradation problems of KD can be much mitigated. However, instead of relying on a naive grid search, which shows poor transferability, we propose Meta Knowledge Distillation (MKD) to meta-learn the distillation with learnable meta temperature parameters. The meta parameters are adaptively adjusted during training according to the gradients of the learning objective. We validate that MKD is robust to different dataset scales, different teacher/student architectures, and different types of data augmentation. With MKD, we achieve the best performance with popular ViT architectures among compared methods that use only ImageNet-1K as training data, ranging from tiny to large models. With ViT-L, we achieve 86.5% with 600 epochs of training, 0.6% better than MAE that trains for 1,650 epochs.
ZeroGen: Efficient Zero-shot Learning via Dataset Generation

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient zero-short learning method, ZeroGen.Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset. This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free and efficient, we argue that ZeroGen can also provide useful insights from the perspective of data-free model-agnostic knowledge distillation, and unreferenced text generation evaluation. Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference, show the effectiveness of ZeroGen.
An Rkhs Approach for Pivotal Inference in Functional Linear Regression

We develop methodology for testing hypotheses regarding the slope function in functional linear regression for time series via a reproducing kernel Hilbert space approach. In contrast to most of the literature, which considers tests for the exact nullity of the slope function, we are interested in the null hypothesis that the slope function vanishes only approximately, where deviations are measured with respect to the $L^2$-norm. An asymptotically pivotal test is proposed, which does not require the estimation of nuisance parameters and long-run covariances. The key technical tools to prove the validity of our approach include a uniform Bahadur representation and a weak invariance principle for a sequential process of estimates of the slope function. Both scalar-on-function and function-on-function linear regression are considered and finite-sample methods for implementing our methodology are provided. We also illustrate the potential of our methods by means of a small simulation study and a data example.
Planckian jitter: enhancing the color quality of self-supervised visual representations

Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The set of used data augmentations is of crucial importance for the quality of the learned feature representation. We analyze how the traditionally used color jitter negatively impacts the quality of the color features in the learned feature representation. To address this problem, we replace this module with physics-based color augmentation, called Planckian jitter, which creates realistic variations in chromaticity, producing a model robust to llumination changes that can be commonly observed in real life, while maintaining the ability to discriminate the image content based on color information. We further improve the performance by introducing a latent space combination of color-sensitive and non-color-sensitive features. These are found to be complementary and the combination leads to large absolute performance gains over the default data augmentation on color classiﬁ-cation tasks, including on Flowers-102 (+15%), Cub200 (+11%), VegFru (+15%), and T1K+ (+12%). Finally, we present a color sensitivity analysis to document the impact of different training methods on the model neurons and we show that the performance of the learned features is robust with respect to illuminant variations.
CIS2: A Simplified Commonsense Inference Evaluation for Story Prose

Transformers have been showing near-human performance on a variety of tasks, but they are not without their limitations. We discuss the issue of conflating results of transformers that are instructed to do multiple tasks simultaneously. In particular, we focus on the domain of commonsense reasoning within story prose, which we call contextual commonsense inference (CCI). We look at the GLUCOSE (Mostafazadeh et al. 2020) dataset and task for predicting implicit commonsense inferences between story sentences. Since the GLUCOSE task simultaneously generates sentences and predicts the CCI relation, there is a conflation in the results. Is the model really measuring CCI or is its ability to generate grammatical text carrying the results? In this paper, we introduce the task contextual commonsense inference in sentence selection ($\rm{C {\small IS}}^2$), a simplified task that avoids conflation by eliminating language generation altogether. Our findings emphasize the necessity of future work to disentangle language generation from the desired NLP tasks at hand.
Generically supercompact cardinals by forcing with chain conditions

A ccc-generically supercompact cardinal κ can be smaller than or equal to the continuum. On the other hand, such a cardinal κ still satisfies diverse largeness properties, like that it is a stationary limit of ccc-generically measurable cardinals (Theorem 4.1). This is in a strong contrast to P-generically supercompact cardinals for the class P of all σ-closed posets, which can be אn for any n > 1.
Aryl: An Elastic Cluster Scheduler for Deep Learning

Companies build separate training and inference GPU clusters for deep learning, and use separate schedulers to manage them. This leads to problems for both training and inference: inference clusters have low GPU utilization when the traffic load is low; training jobs often experience long queueing time due to lack of resources. We introduce Aryl, a new cluster scheduler to address these problems. Aryl introduces capacity loaning to loan idle inference GPU servers for training jobs. It further exploits elastic scaling that scales a training job's GPU allocation to better utilize loaned resources. Capacity loaning and elastic scaling create new challenges to cluster management. When the loaned servers need to be returned, we need to minimize the number of job preemptions; when more GPUs become available, we need to allocate them to elastic jobs and minimize the job completion time (JCT). Aryl addresses these combinatorial problems using principled heuristics. It introduces the notion of server preemption cost which it greedily reduces during server reclaiming. It further relies on the JCT reduction value defined for each additional worker for an elastic job to solve the scheduling problem as a multiple-choice knapsack problem. Prototype implementation on a 64-GPU testbed and large-scale simulation with 15-day traces of over 50,000 production jobs show that Aryl brings 1.53x and 1.50x reductions in average queuing time and JCT, and improves cluster usage by up to 26.9% over the cluster scheduler without capacity loaning or elastic scaling.
Integrated Microsimulation Modelling of Crowd and Subway Network Dynamics For Disruption Management Support

Integrated Microsimulation Modelling of Crowd and Subway Network Dynamics For Disruption Management Support Sivakrishna Srikukenthiran Doctor of Philosophy Graduate Department of Civil Engineering University of Toronto 2015 In many large cities around the world, public transit networks have been carrying an ever-increasing burden of commuters. This has resulted in large movements of crowds in major transit hubs, and high levels of congestion in systems running near or at capacity. In such situations, service disruptions can negatively impact service and transit users well after they are resolved. Currently, transit agencies handle these disruption situations in an ad-hoc fashion. This is due to the lack of a tool capable of analyzing the network-level impacts of response strategies, especially with performance sufficient to handle large-scale systems. Existing tools are limited in their ability to accurately simulate all dynamic facets of the transit network, or model the behaviour of passengers following disruption events. As a result, they do not adequately capture the two-way interaction between crowds and mass transit service, or the complexities of train operation. This dissertation presents Nexus, an innovative crowd dynamics and transit network simulation platform, that enables full simulation of all actors in the transit system and integrated dynamic transit assignment, while being both flexible and scalable to handle large-scale networks. Instead of developing new simulators for surface transit, trains and stations, Nexus enables interfacing of existing simulators together to form a network. An agent-based framework was overlaid to allow for responsive agents, while a virtual communication system permitted on-the-fly modifications to transit service operation. With crowd behaviour in stations key to network performance, new models were constructed to better explain passenger behaviour at two critical locations. First, discrete choice models were developed of passenger choice of stairs versus escalators. Second, a simulation-based model of passenger behaviour on train platforms was developed using a diffusion-inspired approach and accounting for preferred waiting locations. Field data collected across several Toronto subway stations informed both models. Finally, a proof-of-concept case study was conducted on the Toronto transit network. The impact of disruptions of various lengths at a key platform in the network was examined, and an illustrative example was conducted to show how the system could be used to test strategies to reduce impact on
Experimental study of heat transfer enhancement in a concentric double pipe heat exchanger with different axial pitch ratio of perforated twisted tape inserts

In this study, an experimental investigation has been carried out for heat transfer and pressure drop characteristics of a concentric double pipe heat exchanger using the perforated twisted (PT) tape inserts with various axial pitch ratios. The experiments were performed using PT tape inserts with tape-twist ratio of 3.97 and the three axial pitch ratio (Sx/W = 0.56, 0.87 and 1.19) and constant the perforation hole diameter ratio (d/W = 0.16). In the experiments, hot water and cold water flowed through the inner pipe and annulus, respectively. The experiments were performed for counter current flow mode of the fluids in a turbulent flow regime with Reynolds number ranging from 5400 to 17,500. A tube with typical twisted (TT) tape insert and a plain tube were also tested for comparison. The experimental results revealed that both heat transfer rate and friction factor of the heat exchanger equipped with PT tape inserts were significantly higher than those of the plain tube and with TT tape insert. The resu.
Direct Estimate of the Post-Newtonian Parameter and Cosmic Curvature from Galaxy-scale Strong Gravitational Lensing

Einstein’s theory of general relativity (GR) has been precisely tested on solar system scales, but extragalactic tests are still poorly performed. In this work, we use a newly compiled sample of galaxy-scale strong gravitational lenses to test the validity of GR on kiloparsec scales. In order to solve the circularity problem caused by the presumption of a specific cosmological model based on GR, we employ the distance sum rule in the Friedmann–Lemaître–Robertson–Walker metric to directly estimate the parameterized post-Newtonian (PPN) parameter γ PPN and the cosmic curvature Ω k by combining observations of strong lensing and Type Ia supernovae. This is the first simultaneous measurement of γ PPN and Ω k without any assumptions about the contents of the universe or the theory of gravity. Our results show that γPPN=1.11−0.09+0.11 and Ωk=0.48−0.71+1.09 , indicating a strong degeneracy between the two quantities. The measured γ PPN, which is consistent with the prediction of 1 from GR, provides a precise extragalactic test of GR with a fractional accuracy better than 9.0%. If a prior of the spatial flatness (i.e., Ω k = 0) is adopted, the PPN parameter constraint can be further improved to γPPN=1.07−0.07+0.07 , representing a precision of 6.5%. On the other hand, in the framework of GR (i.e., γ PPN = 1), our results are still marginally compatible with zero curvature ( Ωk=−0.12−0.36+0.48 ), supporting no significant deviation from a flat universe.