Miracle-dz/UniFAR-FTUs huggingface dataset:
PolG Inhibits Gastric Cancer Glycolysis and Viability by Suppressing PKM2 Phosphorylation

Purpose Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related death. There is a critical need for the development of novel therapies in GC. DNA polymerase gamma (PolG) has been implicated in mitochondrial homeostasis and affects the development of numerous types of cancer, however, its effects on GC and molecular mechanisms remain to be fully determined. The aim of the present research was to clarify the effects of PolG on GC and its possible molecular mechanism of action. Methods The GSE62254 dataset was used to predict the effect of PolG on prognostic value in GC patients. Lentivirus-mediated transduction was used to silence PolG expression. Western blot analysis evinced the silencing effect. Co-immunoprecipitation (Co-IP) analysis was performed to explore the potential molecular mechanism of action. Analysis of the glycolysis process in GC cells was also undertaken. Cell proliferation was determined using a CCK-8 (Cell Counting Kit-8) proliferation assay. Cell migration was detected using the Transwell device. Animal experiments were used to measure in vivo xenograft tumor growth. Results GC patients with low PolG expression have worse overall survival (OS) and progression-free survival (PFS). PolG binds to PKM2 and affects the activation of Tyr105-site phosphorylation, thus interfering with the glycolysis of GC cells. In vitro tumor formation experiments in mice also confirmed that PolG silencing of GC has a stronger proliferation ability. PolG can suppress GC cell growth both in vivo and in vitro. Conclusion Our study reveals a potential molecular mechanism between PolG and the energy metabolic process of GC tumor cells for the first time, suggesting PolG as an independent novel potential therapeutic target for tumor therapy, and providing new ideas for clinical GC treatment.
The use of gas-liquid chromatography to determine activity coefficients and second virial coefficients of mixtures II. Experimental studies on hydrocarbon solutes

This paper describes apparatus of a new design for measuring accurately gas-liquid retention volumes in packed columns under carrier gas pressures up to 25 atm. It includes a high pressure soap-film flowmeter, a high pressure vapour phase sample injection system and a pressure control system at the column outlet which enables a flame-ionization detector to be operated at atmospheric pressure. Results are reported for a series of hydrocarbon solutes in three different stationary liquids (squalane, dinonylphthalate and n-hexadecane), with nitrogen, hydrogen and argon as carrier gases. Solute activity coefficients at infinite dilution in the stationary liquids, and second virial coefficients (B12) for the solute + carrier gas mixtures have been obtained by analysing the retention volume data by the procedure established in part I. The activity coefficients are compared with values obtained from static vapour pressure measurements made by two widely different methods, and the agreement is satisfactory in both cases. The second virial coefficients (B12) are compared with those values predicted by the principle of corresponding states via the reduced equation of state of McGlashan & Potter (1962). The B12 values given by using the ‘geometric mean’ combining rule for the mixed critical temperature in the reduced equation of state differ from our experimental values by as much as 75 cm3/mole. Using the combining rule of Hudson & McCoubrey (1960) or that of Munn (1961), both of which take account of differences in molal volume and ionization potential between the two components, however, gives B12 values which agree with our experimental values.
STUDY OF VARIOUS HERBICIDES FOR WEED CONTROL IN WHEAT UNDER IRRIGATED CONDITIONS

A field experiment was conducted at Malakandher Farm, NWFP Agricultural University Peshawar during the rabi season 2004- 2005. The experiment was laid out in randomized complete block (RCB) design with four replications. Six treatments were kept in each replication with five post-emergence herbicides including Topik 15 WP @ 0.04 kg, Puma super 75 EW @ 0.75 kg, Buctril super 60 EC @ 0.45 kg, Isoproturon 50 WP @ 1.0 kg, Aim 40 DF @ 0.02 kg a.i. ha -1 and a weedy check. The parameters found significantly affected were weed control efficiency (%), fresh weed biomass (kg ha -1 ), number of tillers m -2 , 1000-grain weight (g) and grain yield (kg ha -1 ). Statistically maximum weed control efficiency (85.4 %) and minimum fresh weed biomass (1015 kg ha -1 ) was observed in plots treated with Isoproturon 50 WP followed by Buctril super 60 EC with values (77.3 %) and (1330 kg ha -1 ) respectively as compared to the fresh weed biomass (3175 kg ha -1 ) in the weedy check. Similarly number of tillers (253 m -2 ), 1000-grain weight (38.9 g), biological yield (13500 kg ha -1 ) and grain yield (3250 kg ha -1 ) were maximum in Isoproturon 50 WP treatments followed by Buctril super 60 EC treatments with values (210 m -2 ), (34.8 g), (11420 kg ha -1 ) and (2883 kg ha -1 ) respectively as compared to the weedy check (136 m -2 ), (23.4 g), (9834 kg ha -1 ) and (1834) respectively. The herbicide Isoproturon 50 WP @ 1.0 kg a.i. ha -1 followed by Buctril super 60 EC @ 0.45 kg a.i. ha -1 both applied as post emergence in wheat performed well in the entire weed and crop data parameters and showed effectively weed control in wheat.
JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction

Abstract Analysis of online reviews has attracted great attention with broad applications. Often times, the textual reviews are coupled with the numerical ratings in the data. In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction. The key of the proposed method is to develop a unified generative model where the topic modeling is constructed based on review texts and the sentiment prediction is obtained by combining review texts and overall ratings. The inference of model parameters are obtained by an efficient Gibbs sampling procedure. The proposed method can enhance the prediction accuracy of review data and achieve an effective detection of interpretable topics and sentiments. The merits of the proposed method are elaborated by the case study from Amazon datasets and simulation studies.
Structural Characteristics of Cerium Oxide Nanocrystals Prepared by the Microemulsion Method

The aim of this work is to investigate the microstructure development of cerium oxide nanocrystal, prepared by the microemulsion process, as a function of annealing temperature in air. Combined with the HRTEM and the thermogravimetric−differential thermal analysis (TG−DTA), the XRD patterns reveal that the sample annealed at 623 K is amorphous, and the formation of cerium oxide nanocrystal occurs above 773 K. The local structural and electronic properties in the nanocrystallization process are probed by X-ray absorption spectra (XAS) at the Ce L3 edge. It is found that the phase structure changes from triclinic to cubic (CeO2), and the electronic structure changes from Ce3+ to Ce4+ upon increasing the annealing temperature.
UPRec: User-Aware Pre-training for Recommender Systems

Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem. To tackle this, the pre-training mechanism has been widely adopted, which attempts to leverage large-scale data to perform self-supervised learning and transfer the pre-trained parameters to downstream tasks. However, previous pre-trained models for recommendation focus on leverage universal sequence patterns from user behaviour sequences and item information, whereas ignore capturing personalized interests with the heterogeneous user information, which has been shown effective in contributing to personalized recommendation. In this paper, we propose a method to enhance pre-trained models with heterogeneous user information, called User-aware Pre-training for Recommendation (UPRec). Specifically, UPRec leverages the user attributes andstructured social graphs to construct self-supervised objectives in the pre-training stage and proposes two user-aware pre-training tasks. Comprehensive experimental results on several real-world large-scale recommendation datasets demonstrate that UPRec can effectively integrate user information into pre-trained models and thus provide more appropriate recommendations for users.
Making an $H$-Free Graph $k$-Colorable

We study the following question: how few edges can we delete from any $H$-free graph on $n$ vertices in order to make the resulting graph $k$-colorable? It turns out that various classical problems in extremal graph theory are special cases of this question. For $H$ any fixed odd cycle, we determine the answer up to a constant factor when $n$ is sufficiently large. We also prove an upper bound when $H$ is a fixed clique that we conjecture is tight up to a constant factor, and prove upper bounds for more general families of graphs. We apply our results to get a new bound on the maximum cut of graphs with a forbidden odd cycle in terms of the number of edges.
General parametrization of wormhole spacetimes and its application to shadows and quasinormal modes

The general parametrization for spacetimes of spherically symmetric Lorentzian, traversable wormholes in an arbitrary metric theory of gravity is presented. The parametrization is similar in spirit to the post-Newtonian parametrized formalism, but with validity that extends beyond the weak field region and covers the whole space. Our method is based on a continued-fraction expansion in terms of a compactified radial coordinate. Calculations of shadows and quasinormal modes for various examples of parametrization of known wormhole metrics that we have performed show that, for most cases, the parametrization provides excellent accuracy already at the first order. Therefore, only a few parameters are dominant and important for finding potentially observable quantities in a wormhole background. We have also extended the analysis to the regime of slow rotation.
Anatomy of a gaussian giant: supercritical level-sets of the free field on random regular graphs

We study the level-set of the zero-average Gaussian Free Field on a uniform random d-regular graph above an arbitrary level h ∈ (−∞, h? ), where h? is the level-set percolation threshold of the GFF on the d-regular tree Td. We prove that w.h. p as the number n of vertices of the graph diverges, the GFF has a unique giant connected component C 1 of size η(h)n+ o(n), where η(h) is the probability that the root percolates in the corresponding GFF level-set on Td. This gives a positive answer to the conjecture of [4] for most regular graphs. We also prove that the second largest component has size Θ(log n). Moreover, we show that C 1 shares the following similarities with the giant component of the supercritical Erdős-Rényi random graph. First, the diameter and the typical distance between vertices are Θ(log n). Second, the 2-core and the kernel encompass a given positive proportion of the vertices. Third, the local structure is a branching process conditioned to survive, namely the level-set percolation cluster of the root in Td (in the Erdős-Rényi case, it is known to be a Galton-Watson tree with a Poisson distribution for the offspring).
Self-Supervised Learning via multi-Transformation Classification for Action Recognition

Self-supervised tasks have been utilized to build useful representations that can be used in downstream tasks when the annotation is unavailable. In this paper, we introduce a self-supervised video representation learning method based on the multi-transformation classification to efficiently classify human actions. Self-supervised learning on various transformations not only provides richer contextual information but also enables the visual representation more robust to the transforms. The spatio-temporal representation of the video is learned in a self-supervised manner by classifying seven different transformations i.e. rotation, clip inversion, permutation, split, join transformation, color switch, frame replacement, noise addition. First, seven different video transformations are applied to video clips. Then the 3D convolutional neural networks are utilized to extract features for clips and these features are processed to classify the pseudo-labels. We use the learned models in pretext tasks as the pre-trained models and fine-tune them to recognize human actions in the downstream task. We have conducted the experiments on UCF101 and HMDB51 datasets together with C3D and 3D Resnet-18 as backbone networks. The experimental results have shown that our proposed framework is outperformed other SOTA self-supervised action recognition approaches. The code will be made publicly available.
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification

With the continued innovations of deep neural networks, spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption. However, for continuous data values, they must employ a coding process to convert the values to spike trains. Thus, they have not yet exceeded the performance of artificial neural networks (ANNs), which handle such values directly. To this end, we combine an ANN and an SNN to build versatile hybrid neural networks (HNNs) that improve the concerned performance. To qualify this performance, MNIST and CIFAR-10 image datasets are used for various classification tasks in which the training and coding methods changes. In addition, we present simultaneous and separate methods to train the artificial and spiking layers, considering the coding methods of each. We find that increasing the number of artificial layers at the expense of spiking layers improves the HNN performance. For straightforward datasets such as MNIST, it is easy to achieve the same performance as ANNs by using duplicate coding and separate learning. However, for more complex tasks, the use of Gaussian coding and simultaneous learning is found to improve the HNN’s accuracy while utilizing a smaller number of artificial layers.
VisualGPT: Data-efficient Image Captioning by Balancing Visual Input and Linguistic Knowledge from Pretraining

The ability to quickly learn from a small quantity of training data widens the range of applications of machine learning. In this paper, we propose a data-efﬁcient image captioning model, VisualGPT, which leverages the linguistic knowledge from a large pretrained language model (LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining. We designed a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the pretrained LM as the language decoder on a small amount of in-domain training data. The proposed self-resurrecting activation unit produces sparse activations but is not susceptible to zero gradients. When trained on 0.1%, 0.5% and 1% of MSCOCO [35] and Conceptual Captions [56], the proposed model, VisualGPT, surpasses strong image captioning baselines. VisualGPT out-performs the best baseline model by up to 10.8% CIDEr on MS COCO and up to 5.4% CIDEr on Conceptual Cap-tions. To the best of our knowledge, this is the ﬁrst work that improves data efﬁciency of image captioning by utilizing LM pretrained on unimodal data. Our code is available at: https://github.com/Vision-CAIR/VisualGPT.
Attention Models for Point Clouds in Deep Learning: A Survey

Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However, creating feature representation of robust, discriminative from unordered and irregular point clouds is challenging. In this paper, our ultimate goal is to provide a comprehensive overview of the point clouds feature representation which uses attention models. More than 75+ key contributions in the recent three years are summarized in this survey, including the 3D objective detection, 3D semantic segmentation, 3D pose estimation, point clouds completion etc. We provide a detailed characterization (1) the role of attention mechanisms, (2) the usability of attention models into different tasks, (3) the development trend of key technology.
The Effects of Image Distribution and Task on Adversarial Robustness

In this paper, we propose an adaptation to the area under the curve (AUC) metric to measure the adversarial robustness of a model over a particular $\epsilon$-interval $[\epsilon_0, \epsilon_1]$ (interval of adversarial perturbation strengths) that facilitates unbiased comparisons across models when they have different initial $\epsilon_0$ performance. This can be used to determine how adversarially robust a model is to different image distributions or task (or some other variable); and/or to measure how robust a model is comparatively to other models. We used this adversarial robustness metric on models of an MNIST, CIFAR-10, and a Fusion dataset (CIFAR-10 + MNIST) where trained models performed either a digit or object recognition task using a LeNet, ResNet50, or a fully connected network (FullyConnectedNet) architecture and found the following: 1) CIFAR-10 models are inherently less adversarially robust than MNIST models; 2) Both the image distribution and task that a model is trained on can affect the adversarial robustness of the resultant model. 3) Pretraining with a different image distribution and task sometimes carries over the adversarial robustness induced by that image distribution and task in the resultant model; Collectively, our results imply non-trivial differences of the learned representation space of one perceptual system over another given its exposure to different image statistics or tasks (mainly objects vs digits). Moreover, these results hold even when model systems are equalized to have the same level of performance, or when exposed to approximately matched image statistics of fusion images but with different tasks.
Adaptive dose‐response studies to establish proof‐of‐concept in learning‐phase clinical trials

In learning‐phase clinical trials in drug development, adaptive designs can be efficient and highly informative when used appropriately. In this article, we extend the multiple comparison procedures with modeling techniques (MCP‐Mod) procedure with generalized multiple contrast tests (GMCTs) to two‐stage adaptive designs for establishing proof‐of‐concept. The results of an interim analysis of first‐stage data are used to adapt the candidate dose‐response models and the dosages studied in the second stage. GMCTs are used in both stages to obtain stage‐wise p ‐values, which are then combined to determine an overall p ‐value. An alternative approach is also considered that combines the t ‐statistics across stages, employing the conditional rejection probability principle to preserve the Type I error probability. Simulation studies demonstrate that the adaptive designs are advantageous compared to the corresponding tests in a nonadaptive design if the selection of the candidate set of dose‐response models is not well informed by evidence from preclinical and early‐phase studies.
Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation

Stochastic root-finding problems are fundamental in the fields of operations research and data science. However, when the root-finding problem involves rare events, crude Monte Carlo can be prohibitively inefficient. Importance sampling (IS) is a commonly used approach, but selecting a good IS parameter requires knowledge of the problem’s solution, which creates a circular challenge. In “Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation,” He, Jiang, Lam, and Fu propose an adaptive IS approach to untie this circularity. The adaptive IS simultaneously estimates the root and the IS parameters, and can be embedded in sample average approximation–type algorithms and stochastic approximation–type algorithms. They provide theoretical analysis on strong consistency and asymptotic normality of the resulting estimators, and show the benefit of adaptivity from a worst-case perspective. They also provide specialized analyses on extreme quantile estimation under milder conditions.
Joint Estimation of Multipath Angles and Delays for Millimeter-Wave Cylindrical Arrays With Hybrid Front-Ends

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.
QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm.