Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

1 Introduction.- 2 Experiments, Deficiencies, Distances v.- 2.1 Comparing Risk Functions.- 2.2 Deficiency and Distance between Experiments.- 2.3 Likelihood Ratios and Blackwell's Representation.- 2.4 Further Remarks on the Convergence of Distri butions of Likelihood Ratios.- 2.5 Historical Remarks.- 3 Contiguity - Hellinger Transforms.- 3.1 Contiguity.- 3.2 Hellinger Distances, Hellinger Transforms.- 3.3 Historical Remarks.- 4 Gaussian Shift and Poisson Experiments.- 4.1 Introduction.- 4.2 Gaussian Experiments.- 4.3 Poisson Experiments.- 4.4 Historical Remarks.- 5 Limit Laws for Likelihood Ratios.- 5.1 Introduction.- 5.2 Auxiliary Results.- 5.2.1 Lindeberg's Procedure.- 5.2.2 Levy Splittings.- 5.2.3 Paul Levy's Symmetrization Inequalities.- 5.2.4 Conditions for Shift-Compactness.- 5.2.5 A Central Limit Theorem for Infinitesimal Arrays.- 5.2.6 The Special Case of Gaussian Limits.- 5.2.7 Peano Differentiable Functions.- 5.3 Limits for Binary Experiments.- 5.4 Gaussian Limits.- 5.5 Historical Remarks.- 6 Local Asymptotic Normality.- 6.1 Introduction.- 6.2 Locally Asymptotically Quadratic Families.- 6.3 A Method of Construction of Estimates.- 6.4 Some Local Bayes Properties.- 6.5 Invariance and Regularity.- 6.6 The LAMN and LAN Conditions.- 6.7 Additional Remarks on the LAN Conditions.- 6.8 Wald's Tests and Confidence Ellipsoids.- 6.9 Possible Extensions.- 6.10 Historical Remarks.- 7 Independent, Identically Distributed Observations.- 7.1 Introduction.- 7.2 The Standard i.i.d. Case: Differentiability in Quadratic Mean.- 7.3 Some Examples.- 7.4 Some Nonparametric Considerations.- 7.5 Bounds on the Risk of Estimates.- 7.6 Some Cases Where the Number of Observations Is Random.- 7.7 Historical Remarks.- 8 On Bayes Procedures.- 8.1 Introduction.- 8.2 Bayes Procedures Behave Nicely.- 8.3 The Bernstein-von Mises Phenomenon.- 8.4 A Bernstein-von Mises Result for the i.i.d. Case.- 8.5 Bayes Procedures Behave Miserably.- 8.6 Historical Remarks.- Author Index.

Asymptotics in Statistics–Some Basic Concepts

Chemotherapeutic resistance poses a significant challenge in cancer treatment, resulting in the reduced efficacy of standard chemotherapeutic agents. Abnormal metabolism, particularly increased anaerobic glycolysis, has been identified as a major contributing factor to chemotherapeutic resistance. To address this issue, noninvasive imaging techniques capable of visualizing tumor glycolysis are crucial. However, the currently available methods (such as PET, MRI, and fluorescence) possess limitations in terms of sensitivity, safety, dynamic imaging capability, and autofluorescence. Here, we present the de novo design of a unique afterglow molecular scaffold based on hemicyanine and rhodamine dyes, which holds promise for low-background optical imaging. In contrast to previous designs, this scaffold exhibits responsive "OFF-ON" afterglow signals through spirocyclization, thus enabling simultaneous control of photodynamic effects and luminescence efficacy. This leads to a larger dynamic range, broader detection range, higher signal enhancement ratio, and higher sensitivity. Furthermore, the integration of multiple functionalities simplifies probe design, eliminates the need for spectral overlap, and enhances reliability. Moreover, we have expanded the applications of this afterglow molecular scaffold by developing various probes for different molecular targets. Notably, we developed a water-soluble pH-responsive afterglow nanoprobe for visualizing glycolysis in living mice. This nanoprobe monitors the effects of glycolytic inhibitors or oxidative phosphorylation inhibitors on tumor glycolysis, providing a valuable tool for evaluating the tumor cell sensitivity to these inhibitors. Therefore, the new afterglow molecular scaffold presents a promising approach for understanding tumor metabolism, monitoring chemotherapeutic resistance, and guiding precision medicine in the future.

Noninvasive Imaging of Tumor Glycolysis and Chemotherapeutic Resistance via De Novo Design of Molecular Afterglow Scaffold.

Traces derived from real-world traffic show that significant redundancy exists at the packet level in mobile network traffic. This has inspired new solutions to suppress the redundancy present in the packet data to manage the explosive traffic. In this paper, we propose a novel approach to performing redundancy elimination by employing universal compression using memory-enabled overhearing helpers without backhaul connectivity, referred to as wireless network compression. The helpers overhear the data packets previously sent by the wireless gateway to various mobile clients within their coverage and use them as side information to reduce the overall communication cost. We study wireless network compression via overhearing helpers from an information-theoretic point of view and conclude that this approach potentially offers a threefold benefit: 1) offloading the wireless gateway and hence increasing the maximum number of mobile nodes the gateway can reliably serve; 2) reducing the average packet delay; and 3) improving the overall throughput in the network.

/pdf/wireless-network-compression-via-memory-enabled-overhearing-9g5zcf7elg.pdf

Wireless Network Compression Via Memory-Enabled Overhearing Helpers

A systemic treatment strategy is urgently demanded to suppress the rapid growth and easy metastasis characteristics of breast cancer. In this work, a chimeric peptide-engineered self-delivery nanomedicine (designated as ChiP-CeR) for photodynamic-triggered breast cancer immunotherapy by macrophage polarization. Among these, ChiP-CeR is composed of the photosensitizer of chlorine e6 (Ce6) and the TLR7/8 agonist of lmiquimod (R837), which is further modified with tumor matrix targeting peptide (Fmoc-K(Fmoc)-PEG8 -CREKA. ChiP-CeR is preferred to actively accumulate at the tumor site via specific recognition of fibronectin, which can eradicate primary tumor growth through photodynamic therapy (PDT). Meanwhile, the destruction of primary tumors would trigger immunogenic cell death (ICD) effects to release high-mobility group box-1(HMGB1) and expose calreticulin (CRT). Moreover, ChiP-CeR can also polarize M2-type tumor-associated macrophages (TAMs) into M1-type TAMs, which can activate T cell antitumor immunity in combination with ICD. Overall, ChiP-CeR possesses superior antitumor effects against primary and lung metastatic tumors, which provide an applicable nanomedicine and a feasible strategy for the systemic management of metastatic breast cancer.

Chimeric Peptide-Engineered Self-Delivery Nanomedicine for Photodynamic-Triggered Breast Cancer Immunotherapy by Macrophage Polarization.

Dual inhibition of glucose uptake and energy supply synergistically restrains the growth and metastasis of breast cancer

This paper investigates the benefits of the side information on the universal compression of sequences from a mixture of $K$ parametric sources. The output sequence of the mixture source is chosen from the source $i \in \{1,\ldots ,K\}$ with a $d_i$-dimensional parameter vector at random according to probability vector $\mathbf{w} = (w_1,\ldots,w_K)$. The average minimax redundancy of the universal compression of a new random sequence of length $n$ is derived when the encoder and the decoder have a common side information of $T$ sequences generated independently by the mixture source. Necessary and sufficient conditions on the distribution $\mathbf{w}$ and the mixture parameter dimensions $\mathbf{d} = (d_1,\ldots,d_K)$ are determined such that the side information provided by the previous sequences results in a reduction in the first-order term of the average codeword length compared with the universal compression without side information. Further, it is proved that the optimal compression with side information corresponds to the clustering of the side information sequences from the mixture source. Then, a clustering technique is presented to better utilize the side information by classifying the data sequences from a mixture source. Finally, the performance of the clustering on the universal compression with side information is validated using computer simulations on real network data traces.

pdf/universal-compression-of-a-mixture-of-parametric-sources-19lfrieep9.pdf

Universal Compression of a Mixture of Parametric Sources with Side Information

Recently, we proposed a framework called memory-assisted compression that learns the statistical properties of the sequence-generating server at intermediate network nodes and then leverages the learnt models to overcome the inevitable redundancy (overhead) in the universal compression of the payloads of the short-length network packets. In this paper, we prove that when the content-generating server is comprised of a mixture of parametric sources, label-based clustering of the data to their original sequence-generating models from the mixture is optimal almost surely as it achieves the mixture entropy (which is the lower bound on the average codeword length). Motivated by this result, we present a K-means clustering technique as the proof of concept to demonstrate the benefits of memory-assisted compression performance. Simulation results confirm the effectiveness of the proposed approach by matching the expected improvements predicted by theory on man-made mixture sources. Finally, the benefits of the cluster-based memory-assisted compression are validated on real data traffic traces demonstrating more than 50% traffic reduction on average in data gathered from wireless users.

/pdf/on-optimality-of-data-clustering-for-packet-level-memory-579k5alasb.pdf

On optimality of data clustering for packet-level memory-assisted compression of network traffic

Recently , we proposed a framework called memory-assisted compression that learns the statistical proper­ ties of the sequence-generat ing server at intermediate network nodes and then leverages the learnt models to overcome the inevitable redundancy (overhead) in the universal compression of the payloads of the short-length network packets. In this paper, we prove that when the content-generati ng server is comprised of a mixture of parametric sources, label-based clustering of the data to their original sequence-generat ing models from the mixture is optimal almost surely as it achieves the mixture entropy (which is the lower bound on the average codeword length). Motivated by this result, we present a K-means clustering technique as the proof of concept to demonstrate the benefits of memory-assisted compression performance. Simulation results confirm the effectiveness of the proposed approach by matching the expected improvements predicted by theory on man-made mixture sources. Finally, the benefits of the cluster-based memory-assisted compression are validated on real data traflic traces demonstrating more than 50% traffic reduction on average in data gathered from wireless users. Index Terms-Memory-Ass isted Wireless Net­ works; Redundancy Elimination; K-Means Clustering.

Memory-Assisted Compression of Network Traffic

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Dual inhibition of glucose uptake and energy supply synergistically restrains the growth and metastasis of breast cancer

Universal Compression of a Mixture of Parametric Sources with Side Information

On optimality of data clustering for packet-level memory-assisted compression of network traffic

Memory-Assisted Compression of Network Traffic