Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for achieving state-of-the-art results. In turn, the efficacy of supervised learning may be limited by the size of the human annotated dataset. This limitation is particularly notable for image segmentation tasks, where the expense of human annotation is especially large, yet large amounts of unlabeled data may exist. In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation. The goal of this work is to avoid the construction of sophisticated, learned architectures specific to label propagation (e.g., patch matching and optical flow). Instead, we simply predict pseudo-labels for the unlabeled data and train subsequent models with both human-annotated and pseudo-labeled data. The procedure is iterated for several times. As a result, our Naive-Student model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks, reaching the performance of 67.8% PQ, 42.6% AP, and 85.2% mIOU on the test set. We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
Galactic inflow and wind recycling rates in the eagle simulations

The role of galactic wind recycling represents one of the largest unknowns in galaxy evolution, as any contribution of recycling to galaxy growth is largely degenerate with the inflow rates of first-time infalling material, and the rates with which outflowing gas and metals are driven from galaxies. We present measurements of the efficiency of wind recycling from the eagle cosmological simulation project, leveraging the statistical power of large-volume simulations that reproduce a realistic galaxy population. We study wind recycling at the halo scale, i.e. gas that has been ejected beyond the halo virial radius, and at the galaxy scale, i.e. gas that has been ejected from the interstellar medium to at least $\approx 10 \, {{\ \rm per\ cent}}$ of the virial radius. Galaxy-scale wind recycling is generally inefficient, with a characteristic return time-scale that is comparable to or longer than a Hubble time, and with an efficiency that clearly peaks at the characteristic halo mass of $M_{200} = 10^{12} \, \mathrm{M_\odot }$. Correspondingly, the majority of gas being accreted on to galaxies in eagle is infalling for the first time. Recycling is more efficient at the halo scale, with values that differ by orders of magnitude from those assumed by semi-analytical galaxy formation models. Differences in the efficiency of wind recycling with other hydrodynamical simulations are currently difficult to assess, but are likely smaller. We find that cumulative first-time gas accretion rates at the virial radius are reduced relative to the expectation from dark matter accretion for haloes with mass $M_{200} \lt 10^{12} \, \mathrm{M_\odot }$, indicating efficient preventative feedback on halo scales.
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting

In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many cases similar problems may have already been solved elsewhere—but the accumulated analytical knowledge cannot be tapped to solve a new problem, e.g., because of privacy barriers.For the particular purpose of sales forecasting for similar entities, we propose a transfer machine learning approach based on additive regression models that lets new entities benefit from models of existing entities. We evaluate the approach on a rich, multi-year dataset of multiple restaurant branches. We differentiate the options to simply transfer models from one branch to another (“zero shot”) or to transfer and adapt them. We analyze feasibility and performance against several forecasting benchmarks. The results show the potential of the approach to exploit the collectively available analytical knowledge.Thus, we contribute an approach that is generalizable beyond sales forecasting and the specific use case in particular. In addition, we demonstrate its feasibility for a typical use case as well as the potential for improving forecasting quality. These results should inform academia, as they help to leverage knowledge across various entities, and have immediate practical application in industry.
Profiles of serum IGF-I concentrations in calves from birth to eighteen months of age and in cows throughout the lactation cycle

Blood samples were obtained from 16 female and 7 male holstein calves at birth and at 3-mo intervals until 15 to 18 mo of age. Monthly blood samples were also obtained from 34 holstein cows beginning 1 mo prepartum and continuing until the subsequent dry period. Serum concentrations of IGF-I were determined by RIA after serum-binding proteins had been removed by acid-gel chromatography. In calves there was an age-dependent increase in serum IGF-I concentrations, which reached a plateau after 9 mo of age. From 3 mo of age, these concentrations were greater in male animals. Analysis of data after retrospective grouping of female calves into fast, medium and slow growth rate groups indicated different (P < 0.05) IGF-I concentrations between groups; levels were greatest in the fastest-growing animals and lowest in the slow growth rate group. However, only weak relationships existed between serum IGF-I levels at 3 or 6 mo of age and body weight at 15 or 18 mo of age. In lactating animals, serum IGF-I concentra.
Principal Fairness for Human and Algorithmic Decision-Making

Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making. The key idea is that one should not discriminate among individuals who would be similarly affected by the decision. Unlike the existing statistical definitions of fairness, principal fairness explicitly accounts for the fact that individuals can be impacted by the decision. We propose an axiomatic assumption that all groups are created equal. This assumption is motivated by a belief that protected attributes such as race and gender should have no direct causal effects on potential outcomes. Under this assumption, we show that principal fairness implies all three existing statistical fairness criteria once we account for relevant covariates. This result also highlights the essential role of conditioning covariates in resolving the previously recognized tradeoffs between the existing statistical fairness criteria. Finally, we discuss how to empirically choose conditioning covariates and then evaluate the principal fairness of a particular decision.
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments

Reinforcement learning (RL) algorithms find applications in inventory control, recommender systems, vehicular traffic management, cloud computing, and robotics. The real-world complications arising in these domains makes them difficult to solve with the basic assumptions underlying classical RL algorithms. RL agents in these applications often need to react and adapt to changing operating conditions. A significant part of research on single-agent RL techniques focuses on developing algorithms when the underlying assumption of stationary environment model is relaxed. This article provides a survey of RL methods developed for handling dynamically varying environment models. The goal of methods not limited by the stationarity assumption is to help autonomous agents adapt to varying operating conditions. This is possible either by minimizing the rewards lost during learning by RL agent or by finding a suitable policy for the RL agent that leads to efficient operation of the underlying system. A representative collection of these algorithms is discussed in detail in this work along with their categorization and their relative merits and demerits. Additionally, we also review works that are tailored to application domains. Finally, we discuss future enhancements for this field.
CPOT: Channel Pruning via Optimal Transport

Recent advances in deep neural networks (DNNs) lead to tremendously growing network parameters, making the deployments of DNNs on platforms with limited resources extremely difficult. Therefore, various pruning methods have been developed to compress the deep network architectures and accelerate the inference process. Most of the existing channel pruning methods discard the less important filters according to well-designed filter ranking criteria. However, due to the limited interpretability of deep learning models, designing an appropriate ranking criterion to distinguish redundant filters is difficult. To address such a challenging issue, we propose a new technique of Channel Pruning via Optimal Transport, dubbed CPOT. Specifically, we locate the Wasserstein barycenter for channels of each layer in the deep models, which is the mean of a set of probability distributions under the optimal transport metric. Then, we prune the redundant information located by Wasserstein barycenters. At last, we empirically demonstrate that, for classification tasks, CPOT outperforms the state-of-the-art methods on pruning ResNet-20, ResNet-32, ResNet-56, and ResNet-110. Furthermore, we show that the proposed CPOT technique is good at compressing the StarGAN models by pruning in the more difficult case of image-to-image translation tasks.
Settling the Complexity of Arrow-Debreu Markets under Leontief and PLC Utilities, using the Classes FIXP and \Exists-R.

This paper resolves two of the handful of remaining questions on the computability of market equilibria, a central theme within algorithmic game theory (AGT). Our results are as follows: 1. We show FIXP-hardness of computing equilibria in Arrow-Debreu markets under Leontief utility functions, and Arrow-Debreu markets under linear utility functions and Leontief production sets. We note that these are the first FIXP-hardness results ever since the introduction of the class FIXP and the hardness of 3-Nash established therein. 2. We note that for the problems stated above, the corresponding results showing membership in FIXP were established after imposing suitable sufficiency conditions to render the problems total, as is customary in economics. However, if all instances are under consideration, then in both cases we prove that the problem of deciding if a given instance admits an equilibrium is \Exists-R-complete, where \Exists-R is the class of decision problems which can be reduced in polynomial time to Existential Theory of the Reals. 3. For Arrow-Debreu markets under Leontief utility functions and a constant number of agents, we give a polynomial-time algorithm for computing an equilibrium. This settles part of an open problem of Devanur and Kannan (FOCS'08). We note that PLC utilities are about the most general utilities of interest in economics and several fundamental utility functions studied within AGT are special cases of it. Several important problems, which have been shown to be in FIXP, are waiting for proofs of FIXP-hardness. In this context, our technique of reducing from 3-Nash to Multivariate Polynomial Equations and then to the problem is likely to be useful in the future.
Oceanographic Considerations for Exoplanet Life Detection

Liquid water oceans are at the center of our search for life on exoplanets because water is a strict requirement for life as we know it. However, oceans are dynamic habitats—and some oceans may be better hosts for life than others. In Earth’s ocean, circulation transports essential nutrients such as phosphate and is a first-order control on the distribution and productivity of life. Of particular importance is upward flow from the dark depths of the ocean in response to wind-driven divergence in surface layers. This “upwelling” returns essential nutrients that tend to accumulate at depth via sinking of organic particulates back to the sunlit regions where photosynthetic life thrives. Ocean dynamics are likely to impose constraints on the activity and atmospheric expression of photosynthetic life in exo-oceans as well, but we lack an understanding of how ocean dynamics may differ on other planets. We address this issue by exploring the sensitivity of ocean dynamics to a suite of planetary parameters using ROCKE-3D, a fully coupled ocean–atmosphere general circulation model. Our results suggest that planets that rotate slower and have higher surface pressure than Earth may be the most attractive targets for remote life detection because upwelling is enhanced under these conditions, resulting in greater nutrient supply to the surface biosphere. Seasonal deepening of the mixed layer on high-obliquity planets may also enhance nutrient replenishment from depth into the surface mixed layer. Efficient nutrient recycling favors greater biological activity, more biosignature production, and thus more detectable life. More generally, our results demonstrate the importance of considering oceanographic phenomena for exoplanet life detection and motivate future interdisciplinary contributions to the emerging field of exo-oceanography.
Simplified Self-Attention for Transformer-Based end-to-end Speech Recognition

Transformer models have been introduced into end-to-end speech recognition with state-of-the-art performance on various tasks owing to their superiority in modeling long-term dependencies. However, such improvements are usually obtained through the use of very large neural networks. Transformer models mainly include two submodules – position-wise feedforward layers and self-attention (SAN) layers. In this paper, to reduce the model complexity while maintaining good performance, we propose a simplified self-attention (SSAN) layer which employs FSMN memory blocks instead of projection layers to form query and key vectors for transformer-based end-to-end speech recognition. We evaluate the SSAN-based and the conventional SAN-based transformers on the public AISHELL-1, internal 1000-hour and 20,000-hour large-scale Mandarin tasks. Results show that our proposed SSAN-based transformer model can achieve over 20% reduction in model parameters and 6.7% relative CER reduction on the AISHELL-1 task. With impressively 20% parameter reduction, our model shows no loss of recognition performance on the 20,000-hour large-scale task.
Chronology of the appearance of β, A, and α mitochondria‐rich cells in the gill epithelium during ontogenesis of the brown trout (Salmo trutta)

Three types of mitochondria‐rich (MR) cells, the α, β, and accessory cells, are observed in the gill epithelium of juvenile and adult freshwater teleosts. In addition to numerous mitochondria, their cytoplasm contains a network of membranous tubules, the tubular system, connected to the laterobasal plasma membrane. Because they are believed to play a role in ionic regulation, it is of interest to examine the order of appearance and the ultrastructural characteristics of such cells during the embryogenesis and larval life of the brown trout.
Larval morphology of Thermonectus succinctus (Aubé 1838) (Coleoptera: Dytiscidae: Dytiscinae), with biological notes and chaetotaxic analysis

Abstract The three larval instars of Thermonectus succinctus (Aubé 1838) are described and illustrated for the first time, emphasizing the morphometry and chaetotaxy. Biological notes are included. Thermonectus Dejean is distinguished from other Aciliini genera by its slightly bifid median process of the prementum, which bears two spine-like setae (LA8). First instars of T. succinctus differ from those of Acilius Leach and Graphoderus Dejean in the lower number of additional anterodistal setae on the femur. The shape of seta TA1 and the presence of an additional posterior pore on the tibia separate first instars of Graphoderus from those of Acilius and T. succinctus. Regarding the mature larva, T. succinctus differs from T. alfredi Griffini 1898 in its smaller size, from T. basillaris (Harris 1829) in the lack of a row of minute hairs along the internal margin of the galea, and from T. nigrofasciatus ornaticollis (Aubé 1838) in the shorter length of seta LA8.
Waldindikatoren zur Artenvielfalt – Erkenntnisse aus LFI und BDM Schweiz | Forest structures and species diversity – Findings from the NFI and BDM

In order to monitor species diversity, surveying indicators in habitats has often been recommended as more cost efficient than assessing species directly. In this study data from the Swiss National Forest Inventory (NFI) and the Biodiversity Monitoring Program (BDM) were used to verify the correlation of species density for vascular plants, mosses, and molluscs with 58 variables of forest structure, site conditions and forest management. The analyses show that site factors, in particular the biogeographic regions, the altitude, slope and the soil acidity, explain 18 to 49% of the observed variance in species density, depending on the species group (taxon). Of all the factors influenced by management, only the availability of light (stand density) was found to play an important role primarily on vascular plants. In addition the density of molluscs is positively correlated to shrub cover. However, none of the regression models tested explains more than 54% of the variance of species density. Therefore, the .
The RNA binding protein CPEB2 regulates hormone sensing in mammary gland development and luminal breast cancer

The RNA binding protein CPEB2 defines a new posttranscriptional layer in the hormone-driven gene-expression reprogramming. Organogenesis is directed by coordinated cell proliferation and differentiation programs. The hierarchical networks of transcription factors driving mammary gland development and function have been widely studied. However, the contribution of posttranscriptional gene expression reprogramming remains largely unexplored. The 3′ untranslated regions of messenger RNAs (mRNAs) contain combinatorial ensembles of cis-regulatory elements that define transcript-specific regulation of protein synthesis through their cognate RNA binding proteins. We analyze the contribution of the RNA binding cytoplasmic polyadenylation element–binding (CPEB) protein family, which collectively regulate mRNA translation for about 30% of the genome. We find that CPEB2 is required for the integration of hormonal signaling by controlling the protein expression from a subset of ER/PR- regulated transcripts. Furthermore, CPEB2 is critical for the development of ER-positive breast tumors. This work uncovers a previously unknown gene expression regulation level in breast morphogenesis and tumorigenesis, coordinating sequential transcriptional and posttranscriptional layers of gene expression regulation.
Resistance of Streptococcus Pneumoniae to Macrolides in Iran

Background: Antimicrobial resistance of Streptococcus pneumoniae (S. pneumoniae) has shown major changes in recent years. On the other hand, macrolide antibiotics are being increasingly used in clinical practice. Several studies have reported increased resistance to this group of antibiotics, while there is no comprehensive information in this area. Accordingly, the present study was designed to estimate the resistance of S. pneumoniae to macrolides in Iran. Materials and Methods: In this review, articles (2000–2017), evaluating the level and type of S. pneumoniae resistance to macrolides in Iran, were extracted by searching different databases, and the results were analyzed. Results: A total of 25 relevant articles were retrieved and analyzed. Overall, 2723 cases had been recruited in these studies. The mean percentage of resistance to macrolides was estimated at 48.43% (CI, 38.8–57.9%). In the majority of reported cases, the resistance mechanisms included ribosomal methylation (i.e., ermB mutation), dual resistance, and efflux-mediated resistance. Conclusion: Based on the findings, the resistance rates are considerable in different cities of Iran. Therefore, without determining the type of drug resistance in clinical samples, use of macrolides is not recommended for treatment purposes. In addition, considering the type of resistance mechanisms in Iran, use of higher drug doses is probably ineffective.
Sanguinarine suppresses migration and metastasis in colorectal carcinoma associated with the inversion of EMT through the Wnt/β‐catenin signaling

Abstract Background Unresectable lung or liver organ metastases of colorectal carcinoma (CRC) remain a major obstacle in clinical therapeutics. Epithelial to mesenchymal transition (EMT), a major cause of highly frequent metastasis in tumor, can be promoted by the Wnt/β‐catenin pathway that is aberrantly activated in approximately 90% of CRC. This research aimed to elucidate the antimetastatic potential of sanguinarine (SG) in CRC and the underlying molecular mechanism. Methods The in vitro anticancer effect of SG was determined via cell viability experiment and colony formation assay. Xenograft model of nude mice was used to confirm the antitumor effect of SG in vivo. The antimetastatic potential of SG was investigated by the metastasis model of nude mice, hematoxylin and eosin (H&E) staining, migration assay, and wound‐healing analysis. Immunoblotting analysis, immunofluorescence staining, and immunohistochemistry assay were conducted to elucidate the molecular mechanism. Results In this study, we reported that SG has a selective inhibitory effect on LoVo cells with metastatic characteristics. Furthermore, our results showed attenuation in the migration and metastatic ability of SG‐treated LoVo cells and also decreased metastatic nodules of liver and lung in mice metastasis model. This was also confirmed at the molecular level via H&E staining. Further study revealed that SG had negative impacts on the Wnt/β‐catenin pathway and EMT markers in LoVo cells both in vitro and in vivo. Conclusions Taken together, the antimetastatic potential of SG attributed to the suppression of the Wnt/β‐catenin signaling, which further prevented EMT progression. SG may be of value in a potential therapy for the management of metastasis CRC.