Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel k-Means, By All Means: Algorithms and Strong Consistency

Kernel $k$-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power $k$-Means, our algorithm makes use of majorization-minimization (MM) to better solve this non-convex problem. We show the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates, and we rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature non-linear and multi-view separation.
Dirichlet Pruning for Neural Network Compression

We introduce Dirichlet pruning, a novel post-processing technique to transform a large neural network model into a compressed one. Dirichlet pruning is a form of structured pruning which assigns the Dirichlet distribution over each layer's channels in convolutional layers (or neurons in fully-connected layers), and estimates the parameters of the distribution over these units using variational inference. The learned distribution allows us to remove unimportant units, resulting in a compact architecture containing only crucial features for a task at hand. Our method is extremely fast to train. The number of newly introduced Dirichlet parameters is only linear in the number of channels, which allows for rapid training, requiring as little as one epoch to converge. We perform extensive experiments, in particular on larger architectures such as VGG and WideResNet (45% and 52% compression rate, respectively) where our method achieves the state-of-the-art compression performance and provides interpretable features as a by-product.
Involvement of the Wnt/β-Catenin signaling pathway in the heterogenous nuclear ribonucleoprotein K-driven inhibition of proliferation and migration in head and neck squamous cell carcinoma

The abnormal upregulation of heterogeneous nuclear ribonucleoprotein K (hnRNP K) expression levels were reported to be involved in the progression of various types of cancer. Therefore, it is hypothesized that hnRNP K may serve as a useful diagnostic marker and antitumor target; however, only a few studies to date have investigated the exact role of hnRNP K in head and neck squamous cell carcinoma (HNSCC) and the potential downstream signaling pathway involved. The present study aimed to identify the roles of hnRNP K in the proliferation and migration of HNSCC, and the possible signaling pathways hnRNP K may be associated with in HNSCC. hnRNP K expression levels in clinical HNSCC samples were analyzed using the Oncomine and UALCAN databases, and its association with the survival of patients with HNSCC was analyzed using the tumor-immune system interactions database. Short hairpin RNA targeting hnRNP K was transfected into the CAL-27 cell line to establish HNSCC cells with stable hnRNP K-knockdown. Cell viability was analyzed using a Cell Counting Kit-8 assay and an absolute count assay, and cell proliferation was measured using 5-ethynyl-2′-deoxyuridine incorporation and colony formation assays. Migratory ability of cells was analyzed using wound healing assay and transwell assay. The growth of xenografts derived from hnRNP K-knockdown cells was also evaluated, and bioinformatics analyses were performed using the Gene Ontology and Kyoto Encyclopedia for Genes and Genomes databases to determine the possible downstream signaling pathways of hnRNP K. Furthermore, the status of the Wnt/β-Catenin signaling pathway in hnRNP K-knockdown cells mediated by small interfering RNA was determined using reverse transcription-quantitative PCR and western blotting. The results revealed that the expression levels of hnRNP K were upregulated in HNSCC cell lines and tissues. Moreover, the upregulation of hnRNP K expression levels was associated with poor survival of patients with HNSCC. The knockdown of hnRNP K also decreased HNSCC cell proliferation and migration, and inhibited tumor growth in nude mice. Bioinformatics analyses identified the Wnt/β-Catenin signaling pathway as a possible downstream signaling pathway of hnRNP K. Knockdown of hnRNP K significantly downregulated the expression levels of Wnt/β-Catenin signaling pathway-related proteins; while with knockdown of hnRNP K and overexpression of β-Catenin, the expression levels of Wnt/β-Catenin signaling pathway-related proteins were partially rescued. In conclusion, the present findings indicated that hnRNP K may serve as a candidate diagnostic biomarker and a promising therapeutic target for HNSCC.
Performance of Bounded-Rational Agents With the Ability to Self-Modify

Self-modification of agents embedded in complex environments is hard to avoid, whether it happens via direct means (e.g. own code modification) or indirectly (e.g. influencing the operator, exploiting bugs or the environment). While it has been argued that intelligent agents have an incentive to avoid modifying their utility function so that their future instances will work towards the same goals, it is not clear whether this also applies in non-dualistic scenarios, where the agent is embedded in the environment. The problem of self-modification safety is raised by Bostrom in Superintelligence (2014) in the context of safe AGI deployment. In contrast to Everitt et al. (2016), who formally show that providing an option to self-modify is harmless for perfectly rational agents, we show that for agents with bounded rationality, self-modification may cause exponential deterioration in performance and gradual misalignment of a previously aligned agent. We investigate how the size of this effect depends on the type and magnitude of imperfections in the agent's rationality (1-4 below). We also discuss model assumptions and the wider problem and framing space. Specifically, we introduce several types of a bounded-rational agent, which either (1) doesn't always choose the optimal action, (2) is not perfectly aligned with human values, (3) has an innacurate model of the environment, or (4) uses the wrong temporal discounting factor. We show that while in the cases (2)-(4) the misalignment caused by the agent's imperfection does not worsen over time, with (1) the misalignment may grow exponentially.
Clustering of Big Data with Mixed Features

Clustering large, mixed data is a central problem in data mining. Many approaches adopt the idea of k-means, and hence are sensitive to initialisation, detect only spherical clusters, and require a priori the unknown number of clusters. We here develop a new clustering algorithm for large data of mixed type, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to mixed data; (2) the algorithm is capable of detecting outliers and clusters of relatively lower density values; (3) the algorithm is competent at deciding the correct number of clusters. The computational complexity of the algorithm is greatly reduced by applying a fast k-nearest neighbors method and by scaling down to component sets. We present experimental results to verify that our algorithm works well in practice. Keywords: Clustering; Big Data; Mixed Attribute; Density Peaks; Nearest-Neighbor Graph; Conductance.
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting

Deep learning is often criticized by two serious issues that rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labeled data, which has little knowledge behind the instance-label pairs. When a deep network continually learns over time by accommodating new tasks, it usually quickly overwrites the knowledge learned from previous tasks. Referred to as the neural variability, it is well known in neuroscience that human brain reactions exhibit substantial variability even in response to the same stimulus. This mechanism balances accuracy and plasticity/flexibility in the motor learning of natural nervous systems. Thus, it motivates us to design a similar mechanism, named artificial neural variability (ANV), that helps artificial neural networks learn some advantages from “natural” neural networks. We rigorously prove that ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. This result theoretically guarantees ANV a strictly improved generalizability, robustness to label noise, and robustness to catastrophic forgetting. We then devise a neural variable risk minimization (NVRM) framework and neural variable optimizers to achieve ANV for conventional network architectures in practice. The empirical studies demonstrate that NVRM can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs.
EPIDEMIOLOGICAL AND CLINICAL CHARACTERISTICS OF COVID-19 PATIENTS IN KENYA

Abstract Background More than 49,000 cases of infection and 900 deaths from COVID-19 have been recorded in the Kenya. However, the characteristics and risk factors for severe outcomes among hospitalized COVID-19 patients in this setting have not been described. Methods We extracted demographic, laboratory, clinical and outcome data from medical records of RT-PCR confirmed SARS-CoV2 patients admitted in six hospitals in Kenya between March and September, 2020. We used Cox proportional hazards regressions to determine factors related to in-hospital mortality. Results Data from 787 COVID-19 patients was available. The median age was 43 years (IQR 30-53), with 505 (64%) males. At admission, 455 (58%) were symptomatic. The commonest symptoms were cough (337, 43%), loss of taste or smell (279, 35%), and fever (126, 16%). Co-morbidities were reported in 340 (43%), with cardiovascular disease, diabetes and HIV documented in 130 (17%), 116 (15%), 53 (7%) respectively. 90 (11%) were admitted to ICU for a mean of 11 days, 52 (7%) were ventilated with a mean of 10 days, 107 (14%) died. The risk of death increased with age [hazard ratio (HR) 1.57 (95% CI 1.13-2.19)] for persons >60 years compared to those <60 years old; having co-morbidities [HR 2.34 (1.68-3.25)]; and among males [HR 1.76 (1.27, 2.44)] compared to females. Elevated white blood cell count and aspartate aminotransferase were associated with higher risk of death. Conclusions We identify the risk factors for mortality that may guide stratification of high risk patients.
Childhood obesity prediction from nationwide health records

Rapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. Here, we analyzed electronic health records of children from Israel's largest healthcare provider from 2002 to 2018 and developed a model for predicting children's obesity. Data included demographics, anthropometric measurements, medications, diagnoses, and laboratory tests of children and their families. Obesity was defined as body mass index (BMI) [≥]95th percentile for age and sex. To identify the most critical time-window in which the largest annual increases in BMI percentile occurs during early childhood, we first analyzed childhood BMI trajectories among 382,132 adolescents. Next, we devised a prediction model targeted to identify children at high risk of obesity prior to this time-window, predicting obesity at 5-6 years of age based on data from the first 2 years of life of 136,196 children. Retrospective BMI analysis revealed that among obese adolescents, the greatest acceleration in BMI percentiles occured between 2-4 years of age. Our model, validated temporally and geographically, accurately predicted obesity at 5-6 years old (auROC of 0.804). Discrimination results on subpopulations demonstrated its robustness across the pediatric population. The model's most influential features included anthropometric measurements of the child and family. Other impactful features included ancestry and pregnancy glucose. Antibiotics exposure in utero and during the first 2 years of life had no observed impact on obesity prediction. Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems.
Accretion disks around naked singularities

We investigate here the thermal properties of accretion disks in a spacetime for some galactic density profiles in spherical symmetry. The matter distributions have a finite outer radius with a naked central singularity. The luminosities of the accretion disks for some density profile models are found to be higher than those for a Schwarzschild black hole of the same mass. The slopes for the luminosity distributions with respect to frequencies are significantly different, especially at higher frequencies, from that in the Schwarzschild black hole case. Such features may be used to distinguish black holes from naked singularities. The efficiencies for the conversion of mass energy of the accreting gas into radiation and the strength of naked singularities are analyzed. The novel feature that we find is, the strength of the singularity is different depending on the profiles considered, and the stronger the singularity is, the higher is the efficiency for the accretion disk.
Autonomous Obstacle Legipulation with a Hexapod Robot

Legged robots traversing in confined environments could find their only path is blocked by obstacles. In circumstances where the obstacles are movable, a multilegged robot can manipulate the obstacles using its legs to allow it to continue on its path. We present a method for a hexapod robot to autonomously generate manipulation trajectories for detected obstacles. Using a RGB-D sensor as input, the obstacle is extracted from the environment and filtered to provide key contact points for the manipulation algorithm to calculate a trajectory to move the obstacle out of the path. Experiments on a 30 degree of freedom hexapod robot show the effectiveness of the algorithm in manipulating a range of obstacles in a 3D environment using its front legs.
Immediate or Reflective?: Effects of Real-timeFeedback on Group Discussions over Videochat

Having a group discussion with the members holding conflicting viewpoints is difficult. It is especially challenging for machine-mediated discussions in which the subtle social cues are hard to notice. We present a fully automated videochat framework that can automatically analyze audio-video data of the participants and provide real-time feedback on participation, interruption, volume, and facial emotion. In a heated discourse, these features are especially aligned with the undesired characteristics of dominating the conversation without taking turns, interrupting constantly, raising voice, and expressing negative emotion. We conduct a treatment-control user study with 40 participants having 20 sessions in total. We analyze the immediate and the reflective effects of real-time feedback on participants. Our findings show that while real-time feedback can make the ongoing discussion significantly less spontaneous, its effects propagate to successive sessions bringing significantly more expressiveness to the team. Our explorations with instant and propagated impacts of real-time feedback can be useful for developing design strategies for various collaborative environments.
Floquet manipulation of interaction-induced edge states in spin chains

We predict edge defects induced by spin-spin interaction in a paradigmatic Heisenberg spin chain. Such defects proportional to interaction can trap one magnon and magnon pair which are termed as one-magnon edge state and bound-magnon edge states, respectively. Besides, there are modulation-induced defects by introducing a time-periodic tunneling. The interplay between the two types of defects allows us to switch one of the defects and manipulate the interaction-induced bound-magnon edge state into the continuum this http URL the high-frequency regime, we analytically derive an effective model by a multiscale perturbation analysis to interpret the corresponding physical mechanisms for edge physics. Our work makes an important progress in understanding the edge physics of spin chains which has been overlooked for a long time, but also offers a new route to control the quantum dynamics of magnons.
Multi-View Dynamic Heterogeneous Information Network Embedding

Most existing heterogeneous information network (HIN) embedding methods focus on static environments while neglecting the evolving characteristic of real-world networks. Although several dynamic embedding methods have been proposed, they are merely designed for homogeneous networks and cannot be directly applied in heterogeneous environments. To tackle above challenges, we propose a novel framework for incorporating temporal information into HIN embedding, named multi-view dynamic HIN embedding (MDHNE), which can efficiently preserve evolution patterns of implicit relationships from different views in updating node vectors over time. We first transform HIN to a series of homogeneous networks corresponding to different views. Then our proposed MDHNE applies recurrent neural network (RNN) to incorporate evolving pattern of complex network structure and semantic relationships between nodes into latent embedding spaces, and thus the node vectors from multiple views can be learned and updated when HIN evolves over time. Moreover, we come up with an attention-based fusion mechanism, which can automatically infer weights of latent vectors corresponding to different views by minimizing the objective function specific for different mining tasks. Extensive experiments clearly demonstrate that our MDHNE model outperforms state-of-the-art baselines on three real-world dynamic datasets for different network mining tasks.
Melatonin attenuates mitochondrial and metabolic dysfunction caused by leptin deficiency

Leptin, as a nutritional inhibitor by repressing food intake, is critical compromised in the major common forms of obesity. Skeletal muscle is the main effector tissue for energy expenditure modifications by the effect of endocrine axes, such as leptin signaling. Our study has been carried out using skeletal muscle from leptin-deficient animal model, in order to ascertain the importance of this hormone in eating disorders. Here we report that leptin-deficiency stimulates an uncontrolled oxidative phosphorylation metabolism, resulting in an excess of energy production that culminates in mitochondrial dysfunction. Thus, different nutrient sensing pathways are perturbed, loosing proteostasis and promoting lipid anabolism, that induces myofiber degeneration and drives oxidative type I fiber conversion. Melatonin treatment plays a significant role in regulating energy homeostasis and fuel utilization. This study reveals melatonin to be a decisive mitochondrial function-fate regulator, with implications for resembling physiological energy requirements and targeting glycolytic type II fibers recovery.
The Usability of Ownership

Ownership is the concept of tracking aliases and mutations to data, useful for both memory safety and system design. The Rust programming language implements ownership via the borrow checker, a static analyzer that extends the core type system. The borrow checker is a notorious learning barrier for new Rust users. In this paper, I focus on the gap between understanding ownership in theory versus its implementation in the borrow checker. As a sound and incomplete analysis, compiler errors may arise from either ownership-unsound behavior or limitations of the analyzer. Understanding this distinction is essential for fixing ownership errors. But how are users actually supposed to make the correct inference? Drawing on my experience with using and teaching Rust, I explore the many challenges in interpreting and responding to ownership errors. I also suggest educational and automated interventions that could improve the usability of ownership.
Automatic Neural Lyrics and Melody Composition

In this paper, we propose a technique to address the most challenging aspect of algorithmic songwriting process, which enables the human community to discover original lyrics, and melodies suitable for the generated lyrics. The proposed songwriting system, Automatic Neural Lyrics and Melody Composition (AutoNLMC) is an attempt to make the whole process of songwriting automatic using artificial neural networks. Our lyric to vector (lyric2vec) model trained on a large set of lyric-melody pairs dataset parsed at syllable, word and sentence levels are large scale embedding models enable us to train data driven model such as recurrent neural networks for popular English songs. AutoNLMC is a encoder-decoder sequential recurrent neural network model consisting of a lyric generator, a lyric encoder and melody decoder trained end-to-end. AutoNLMC is designed to generate both lyrics and corresponding melody automatically for an amateur or a person without music knowledge. It can also take lyrics from professional lyric writer to generate matching melodies. The qualitative and quantitative evaluation measures revealed that the proposed method is indeed capable of generating original lyrics and corresponding melody for composing new songs.
Policing Chronic and Temporary Hot Spots of Violent Crime: A Controlled Field Experiment

Hot-spot-based policing programs aim to deter crime through increased proactive patrols at high-crime locations. While most hot spot programs target easily identified chronic hot spots, we introduce models for predicting temporary hot spots to address effectiveness and equity objectives for crime prevention, and present findings from a crossover experiment evaluating application of hot spot predictions to prevent serious violent crime in Pittsburgh, PA. Over a 12-month experimental period, the Pittsburgh Bureau of Police assigned uniformed patrol officers to weekly predicted chronic and temporary hot spots of serious violent crimes comprising 0.5 percent of the city's area. We find statistically and practically significant reductions in serious violent crime counts within treatment hot spots as compared to control hot spots, with an overall reduction of 25.3 percent in the FBI-classified Part 1 Violent (P1V) crimes of homicide, rape, robbery, and aggravated assault, and a 39.7 percent reduction of African-American and other non-white victims of P1V crimes. We find that temporary hot spots increase spatial dispersion of patrols and have a greater percentage reduction in P1V crimes than chronic hot spots but fewer total number of crimes prevented. Only foot patrols, not car patrols, had statistically significant crime reductions in hot spots. We find no evidence of crime displacement; instead, we find weakly statistically significant spillover of crime prevention benefits to adjacent areas. In addition, we find no evidence that the community-oriented hot spot patrols produced over-policing arrests of minority or other populations.