The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms

Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. Existing AHC methods, which are based on a distance measure, have one key issue: it has difficulty in identifying adjacent clusters with varied densities, regardless of the cluster extraction methods applied on the resultant dendrogram. In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it. We analyse the condition under which existing AHC methods fail to extract clusters effectively; and the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms such as existing traditional AHC algorithms, HDBSCAN, GDL and PHA. In each of these algorithms, our empirical evaluation shows that a recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel

Few-Shot Learning with a Strong Teacher

Few-shot learning (FSL) aims to train a strong classifier using limited labeled examples. Many existing works take the meta-learning approach, sampling few-shot tasks in turn and optimizing the few-shot learner's performance on classifying the query examples. In this paper, we point out two potential weaknesses of this approach. First, the sampled query examples may not provide sufficient supervision for the few-shot learner. Second, the effectiveness of meta-learning diminishes sharply with increasing shots (i.e., the number of training examples per class). To resolve these issues, we propose a novel objective to directly train the few-shot learner to perform like a strong classifier. Concretely, we associate each sampled few-shot task with a strong classifier, which is learned with ample labeled examples. The strong classifier has a better generalization ability and we use it to supervise the few-shot learner. We present an efficient way to construct the strong classifier, making our proposed objective an easily plug-and-play term to existing meta-learning based FSL methods. We validate our approach in combinations with many representative meta-learning methods. On several benchmark datasets including miniImageNet and tiredImageNet, our approach leads to a notable improvement across a variety of tasks. More importantly, with our approach, meta-learning based FSL methods can consistently outperform non-meta-learning based ones, even in a many-shot setting, greatly strengthening their applicability

Temozolomide and Pazopanib Combined with FOLFOX Regressed a Primary Colorectal Cancer in a Patient-derived Orthotopic Xenograft Mouse Model.

PurposeThe goal of the present study was to determine the efficacy of temozolomide (TEM) and pazopanib (PAZ) combined with FOLFOX (oxaliplatin, leucovorin and 5-fluorouracil) on a colorectal cancer patient-derived orthotopic xenograft (PDOX) mouse model.Materials and methodsA colorectal cancer tumor from a patient previously established in non-transgenic nude mice was implanted subcutaneously in transgenic green fluorescence protein (GFP)-expressing nude mice in order to label the tumor stromal cells with GFP. Then labeled tumors were orthotopically implanted into the cecum of nude mice. Mice were randomized into four groups: Group 1, untreated control; group 2, TEM + PAZ; group 3, FOLFOX; group 4, TEM + PAZ plus FOLFOX. Tumor width, length, and mouse body weight were measured weekly. The Fluor Vivo imaging System was used to image the GFP-lableled tumor stromal cells in vivo. H&E staining and immunohistochemical staining were used for histological analysis.ResultsAll three treatments inhibited tumor growth as compared to the untreated control group. The combination of TEM + PAZ + FOLFOX regressed tumor growth significantly more effectively than TEM + PAZ or FOLFOX. Only the combination of TEM + PAZ + FOLFOX group caused a decrease in body weight. PAZ suppressed lymph vessels density in the colorectal cancer PDOX mouse model suggesting inhibition of lymphangiogenesis.ConclusionOur results suggest that the combination of TEM + PAZ + FOLFOX has clinical potential for colorectal cancer patient

Study on Characteristic of Overburden Movement in Unsymmetrical Isolated Longwall Mining Using Microseismic Technique

AbstractBased on the key stratum theory, overlying strata structures above a typical unsymmetrical isolated working face (LW10302) was analyzed, and a microseismic monitoring was also applied to characterize the fracturing propagations associated with overburden movement in mining progress. The results show that the overlying strata above LW10302 can be divided into key strata of different grades, and the formed “O-X” fracturing structure have the main and inferior “O-X” ones. The spatial evolution of seismic events demonstrated that seismic activities fits very well with the overburden fracturing patterns and stress manifestation around the longwall face. In the mining process, most of the events located within the surrounding strata of LW10301 and 10302 while low energy events distributed mainly in multiple roof and floor strata, and the strong tremors occurred almost within the super-thick primary key strata and appeared to be related to shear fracturing of large-scale overburden movement. Additionally, seismic signals corresponding to different failure mechanisms show different characteristics in waveform features. The study in this paper indicates that microseismic monitoring can provide invaluable information to characterize the mining-induced seismicity and reveal the failure patterns within strata associated with mining, which will greatly benefit the alleviation and prevention of rock burst hazards in mine

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems

The Nematic Energy Scale and the Missing Electron Pocket in FeSe

Superconductivity emerges in proximity to a nematic phase in most iron-based superconductors. It is therefore important to understand the impact of nematicity on the electronic structure. Orbital assignment and tracking across the nematic phase transition prove to be challenging due to the multiband nature of iron-based superconductors and twinning effects. Here, we report a detailed study of the electronic structure of fully detwinned FeSe across the nematic phase transition using angle-resolved photoemission spectroscopy. We clearly observe a nematicity-driven band reconstruction involving dxz, dyz, and dxy orbitals. The nematic energy scale between dxz and dyz bands reaches a maximum of 50 meV at the Brillouin zone corner. We are also able to track the dxz electron pocket across the nematic transition and explain its absence in the nematic state. Our comprehensive data of the electronic structure provide an accurate basis for theoretical models of the superconducting pairing in FeSe