Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis and Evaluation of Wireless Mobile Networks using the Power Saving Class Type 111 in IEEE 802.16e with Self-similar Th affic

In mobile networks ， how to control energy consum p-tion is one of the most important issues for the battery-powered mobile stations. The standard energy-saving mechanism for conserving power of the mobile stations is “ sleep mode". Based on the operating mechanism of the sleep mode in the conventional power saving class (PSC) type III ， and taking into account the self-similar nature of a massiv e- scale wireless multimedia service ， in this paper ， we build a batch arrival queueing model as a Geom X /G/1 queuing model to suppose the batch size 出 arandom variable following a Pareto( c ， α) distribution. By using a discrete-time imbedded Markov chain ， we analyze the probability generating functions of the queueing length and the waiting time of the system. Then we give the formula for performance measures in terms of the response time of data frames and the energy saving ratio. Moreover ， we consider a slee p- delay into the system to consider how the slee p- delay impacts on the network performance. In numerical results ， we evaluate the system performance when considered with different degrees of self-similarity and different sleep window sizes. We also compare the average performance measures for systems both with slee p- delay and without slee p- delay by using numerical results.
Semistability of Nonlinear Systems Having a Continuum of Equilibria and Time-Varying Delays

In this paper, we develop a semistability analysis framework for nonlinear systems with time-varying delays with applications to stability analysis of multiage nt dynamic networks with consensus protocols in the presence of unknown heterogeneous time-varying delays along the communication links. We show that for such a nonlinear system having a continuum of equilibria, if the system asymptotically converges to a constant time-delay system and this new system is semistable, then the original time-varying delay system is semistable, provided that the delays are just bounded, not necessarily differentiable. In proving our results, we extend the limiting equation approach to the time-varying delay systems and also develop some new convergence results for functional differential equations.
Sialyl Lewis" Antigen as Defined by Monoclonal Antibody AM-3 Is a Marker of Dysplasia in the Colonie Adenoma-Carcinoma Sequence

Monoclonal antibody AM-3 (MAb AM-3) raised against a sialomucin from human colorectal carcinoma has previously been shown to define a carbohydrate epitope, which is detectable by immunocytochemistry on all investigated colonie carcinomas and is expressed in correlation with the grade of dysplasia in colonie adenomas (Hanski et al., J. Clin. Pathol., 43:379-385, 1990). Epitope analyses in solid-phase enzyme immunoas- says revealed that AM-3 antibody recognizes the sialylated Lewis" se quence on a branched O-linked glycan and its reductively cleaved alditol from human amniotic mucins. In comparative binding and binding inhi bition studies MAbs AM-3 and CSLEX1 displayed reciprocal affinities to mucins versus gangliosides. Correspondingly, the weaker binding ac tivities of AM-3 versus CSLEX to III'-aFuc-IV'-aNeuAc-nLcOse^Cer or to monogangliosides from human granulocytes were measured. Gan gliosides from a human colon carcinoma were recognized by MAb CSLEX1 exhibiting a broader specificity to various sialyl-Lewis" antigens and by MAb FH6 reactive to sialyl-dimeric Lewis' antigen, but not by MAb AM-3. In conclusion, MAb AM-3 is distinguished from other sialyl Lewisx-specific MAbs by its selective reactivity to mucin-carried epitopes on the immollimi- antigen.
Signaling pathway via TNF- (cid:1) /NF- (cid:2) B in intestinal epithelial cells may be directly involved in colitis-associated carcinogenesis

M. Signaling pathway via TNF- (cid:1) /NF- (cid:2) B in intestinal epithelial cells may be directly involved in colitis-associated carcinogenesis. Am J Physiol Gastrointest Liver Physiol 296: G850–G859, 2009. doi:10.1152/ajpgi.00071. 2008. —Treat-ment with anti-TNF- (cid:1) MAb has been accepted as a successful maintenance therapy for patients with inﬂammatory bowel diseases (IBD). Moreover, it has been recently reported that blockade of TNF receptor (TNFR) 1 signaling in inﬁltrating hematopoietic cells may prevent the development of colitis-associated cancer (CAC). How-ever, it remains unclear whether the TNF- (cid:1) signaling in epithelial cells is involved in the development of CAC. To investigate this, we studied the effects of anti-TNF- (cid:1) MAb in an animal model of CAC by administration of azoxymethane (AOM) followed by sequential dextran sodium sulfate (DSS) ingestion. We observed that the NF- (cid:2) B pathway is activated in colonic epithelia from DSS-administered mice in association with upregulation of TNFR2 rather than TNFR1. Immunoblot analysis also revealed that the TNFR2 upregulation accompanied by the NF- (cid:2) B activation is further complicated in CAC tissues induced in AOM/DSS-administered mice compared with the nontumor area. Such NF- (cid:2) B activity in the epithelial cells is signiﬁcantly suppressed by the treatment of MP6-XT22, an anti-TNF- (cid:1) MAb. Despite inability to reduce the severity of colitis, sequential administration of MP6-XT22 reduced the numbers and size of tumors in association with the NF- (cid:2) B inactivation. Taken together, present studies suggest that the TNFR2 signaling in intestinal epithelial cells may be directly involved in the development of CAC with persistent colitis and imply that the maintenance therapy with anti-TNF- (cid:1) MAb may prevent the development of CAC in patients with long-standing IBD.
ACELL December 46/6

JOANNE E. RACE,1 FADI N. MAKHLOUF,1,2 PAUL J. LOGUE,2 FREDERICK H. WILSON,3 PHILIP B. DUNHAM,2 AND ELI J. HOLTZMAN1,4 1Renal Division, Department of Medicine, and 4Department of Biochemistry and Molecular Biology, State University of New York Health Science Center, Syracuse 13210; 2Department of Biology, Syracuse University, Syracuse, New York 13244; and 3Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520
Time-harmonic finite element simulation of a shaded-pole induction machine

Elliptical air gap fields are decomposed by Fourier transforms into forward and backward rotating components. By applying these to two distinct rotor models, it is possible to account for motional eddy current effects by slip transformation. External circuit coupling and ferromagnetic saturation are considered. The overall approach simulates efficiently the steady-state behaviour of rotating shaded-pole induction machines without time stepping. Introduction To simulate a three-phase induction machine at steady-state, a time-harmonic finite element (FE) method can be used [1]. Motional eddy current effects are incorporated by slip transformation, i.e., by multiplying the rotor bar conductivities by the slip. The main assumption thereby is that the air gap field is a rotating, sinusoidal wave. In single-phase induction machines, however, the air gap field is alternating or, if an auxiliary winding or shading rings are applied, of an elliptical wave form (Fig. 1) [2]. Hence, time-harmonic simulation with slip transformation is no longer applicable. Existing simulation techniques are based on transient FE simulation [3], suffering from huge simulation times, or lumped parameter extraction methods, which may be inaccurate [4]. In this paper, a novel time-harmonic FE method with air gap flux splitting is presented and applied to a shaded-pole machine. Single-Phase Induction Machine Model The 2D time-harmonic magnetodynamic formulation is V A s j y A y x A x z z z z ∆ σ = σ ω +       ∂ ∂ ν ∂ ∂ −       ∂ ∂ ν ∂ ∂ − l (1) with ν the reluctivity, σ the conductivity, ω the electrical pulsation and z l the axial length of the 2D model [5]. The phasor z A corresponds to the z -component of the magnetic vector potential. The phasor V ∆ is the voltage drop between the front and rear ends of the model. The slip is defined by
Modeling suicide mortality in US counties using population socioeconomic indicators

Background: Suicide is one of the leading causes of death in the United States and population risk prediction models can inform the type, location, and timing of public health interventions. Here, we report the development of a prediction model of suicide risk using population characteristics. Methods: All suicide deaths reported to the Nation Vital Statistics System between 2005-2019 were identified, and age, sex, race, and county-of-residence of the decedents were extracted to calculate baseline risk. County-wise annual measures of socioeconomic predictors of suicide risk (unemployment, weekly wage, poverty prevalence, median household income, and population density), along with two state-wise measures of prevalence of major depressive disorder and firearm ownership were compiled from public sources. Conditional autoregressive (CAR) models, which account for spatiotemporal autocorrelation in response and predictors, were used to estimate county-level risk. Results: Estimates derived from CAR models were more accurate than from models not adjusted for spatiotemporal autocorrelation. Inclusion of suicide risk/protective covariates further reduced errors. Suicide risk was estimated to increase with each standard deviation increase in firearm ownership (2.8%), prevalence of major depressive episode (1%) and unemployment (2.8%). Conversely, risk was estimated to decrease by 4.3% for each standard deviation increase in both median household income and population density. Increased heterogeneity of risk across counties was also noted. Conclusions: Area-level characteristics and the CAR model structure can estimate population-level suicide risk and thus inform decisions on resource allocation and focused interventions during outbreaks.
Transcriptomics Meta-Analysis Predicts Two Robust Human Biomarkers for Severe Infection with SARS-CoV-2

Defining the human factors associated with severe vs mild SARS-CoV-2 infection has become of increasing interest. Mining large numbers of public gene expression datasets is an effective way to identify genes that contribute to a given phenotype. Combining RNA-sequencing data with the associated clinical metadata describing disease severity can enable earlier identification of patients who are at higher risk of developing severe COVID-19 disease. We consequently identified 356 public RNA-seq human transcriptome samples from the Gene Expression Omnibus database that had disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to quantify gene expression in each patient. This process involved using Salmon to map the reads to the reference transcriptomes, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then applied a machine learning algorithm to the read counts data to identify features that best differentiated samples based on COVID-19 severity phenotype. Ultimately, we produced a ranked list of genes based on their Gini importance values that includes GIMAP7 and S1PR2, which are associated with immunity and inflammation (respectively). Our results show that these two genes can potentially predict people with severe COVID-19 at up to ~90% accuracy. We expect that our findings can help contribute to the development of improved prognostics for severe COVID-19.
Parallel K-clique counting on GPUs

Counting k-cliques in a graph is an important problem in graph analysis with many applications such as community detection and graph partitioning. Counting k-cliques is typically done by traversing search trees starting at each vertex in the graph. Parallelizing k-clique counting has been well-studied on CPUs and many solutions exist. However, there are no performant solutions for k-clique counting on GPUs. Parallelizing k-clique counting on GPUs comes with numerous challenges such as the need for extracting fine-grain multi-level parallelism, sensitivity to load imbalance, and constrained physical memory capacity. While there has been work on related problems such as finding maximal cliques and generalized sub-graph matching on GPUs, k-clique counting in particular has yet to be explored in depth. In this paper, we present the first parallel GPU solution specialized for the k-clique counting problem. Our solution supports both graph orientation and pivoting for eliminating redundant clique discovery. It incorporates both vertex-centric and edge-centric parallelization schemes for distributing work across thread blocks, and further partitions work within each thread block to extract fine-grain multi-level parallelism while tolerating load imbalance. It also includes optimizations such as binary encoding of induced sub-graphs and sub-warp partitioning to limit memory consumption and improve the utilization of execution resources. Our evaluation shows that our best GPU implementation outperforms the best state-of-the-art parallel CPU implementation by a geometric mean of 12.39X, 6.21X, and 18.99X for k = 4, 7, and 10, respectively. We also perform a detailed evaluation of the trade-offs involved in the choice of parallelization scheme, and the incremental speedup of each optimization to provide an in-depth understanding of the optimization space. The insights from our optimization flow can be useful for optimizing other clique finding and graph mining solutions on GPUs. Our code will be open-sourced to enable further research on GPU parallelization of k-clique counting and other similar graph mining algorithms.
Conceptual Design of the Memory System of the Robot Cognitive Architecture ArmarX

We consider the memory system as a key component of any technical cognitive system that can play a central role in bridging the gap between high-level symbolic discrete representations used for reasoning, planning and semantic scene understanding and low-level sensorimotor continuous representations used for control. In this work we described conceptual and technical characteristics such a memory system has to fulﬁll, together with the underlying data representation. We identify these characteristics based on the experience we gained in developing our ARMAR humanoid robot systems and discuss practical examples that demonstrate what a memory system of a humanoid robot performing tasks in human-centered environments should support, such as multi-modality, introspectability, hetero-associativity, predictability or an inherently episodic structure. Based on these characteristics, we extended our robot software framework ArmarX into a uniﬁed cognitive architecture that is used in robots of the ARMAR humanoid robot family. Further, we describe, how the development of robot software led us to this novel memory-enabled cognitive architecture and we show how the memory is used by the robots to implement memory-driven behaviors.
Whale: Efficient Giant Model Training over Heterogeneous GPUs

The scaling up of deep neural networks has been demonstrated to be effective in improving model quality, but also encompasses several training challenges in terms of training efficiency, programmability, and resource adaptability. We present Whale, a general and efficient distributed training framework for giant models. To support various parallel strategies and their hybrids, Whale generalizes the programming interface by defining two new primitives in the form of model annotations, allowing for incorporating user hints. The Whale runtime utilizes those annotations and performs graph optimizations to transform a local deep learning DAG graph for distributed multi-GPU execution. Whale further introduces a novel hardware-aware parallel strategy, which improves the performance of model training on heterogeneous GPUs in a balanced manner. Deployed in a production cluster with 512 GPUs, Whale successfully trains an industry-scale multimodal model with over ten trillion model parameters, named M6, demonstrating great scalability and efficiency.
New kernel-based change-point detection

Change-point analysis plays a signiﬁcant role in various ﬁelds to reveal discrepancies in distribution in a sequence of observations. While a number of algorithms have been proposed for high-dimensional data, kernel-based methods have not been well explored due to difﬁculties in controlling false discoveries and mediocre performance. In this paper, we propose a new kernel-based framework that makes use of an important pattern of data in high dimensions to boost power. Analytic approximations to the signiﬁcance of the new statistics are derived and fast tests based on the asymptotic results are proposed, offering easy off-the-shelf tools for large datasets. The new tests show superior performance for a wide range of alternatives when compared with other state-of-the-art methods. We illustrate these new approaches through an analysis of a phone-call network data.
PROFILE OF PATIENTS WITH OSTEOPOROTIC FRACTURES AND FACTORS THAT DECREASE PREVENTION

ABSTRACT Objective: To evaluate the epidemiological profile of patients with osteoporotic fractures compared to patients with osteoarthritis (OA) and identify factors that diminish adherence to secondary prevention. Methods: A total of 108 patients with osteoporotic fractures (OF) were compared to 86 patients with OA. Results: Patients in the OF group were older (p < 0.001); had a lower body mass index (p < 0.001); were less literate (p = 0.012); were more frequently Caucasian (p = 0.003); were less frequently married (p < 0.001); experienced more falls, cognitive deficiency, previous fractures, old fracture, falls in the last year, and fall fractures; needed more help and took more medicine for osteoporosis (p < 0.05); and showed less pathology in the feet, muscle weakness, less vitamin D intake, and lower Katz & Lawton scores (p < 0.001). Factors that increased the chance of nonadherence included older age (p = 0.020), falls (p = 0.035), cognitive deficiency (p = 0.044), and presence of depression/apathy/confusion (p < 0.001). Conclusion: Patient age, ethnicity, marital status, previous falls, foot pathologies, muscle weakness, previous fractures, use of vitamin D, use of osteoporosis drugs, and lower Katz & Lawton scale score defined the OF group. Factors that increased the chance of nonadherence included older age, sedative use, cognitive disorders, and symptoms of depression/apathy/confusion. Level of Evidence III, Case-control.
Exploring the Jarzynski Equality for the Harmonic Oscillator

Twenty-five years ago [1], Christopher Jarzynski published a paper in which he asserted: the work done, W, in driving a system from state A to state B, characterized by the Helmholtz free energies F and F, satisfies the equality: < exp[−βW] > = exp[−β(F − F)] in which < . > denotes an average over an ensemble of realizations of the stochastic W along the path from A to B. The equality is significant and unexpected. So is the statement that the equality is independent of the rate of change from state A to state B. Almost ten years ago, I presented two papers in which the contraction of the description from full phase space to coordinate space was made. This was motivated by the large difference in time scales for momenta relaxation and coordinate relaxation at temperatures near 300 K and processes at the nanoscale in space and time. The Jarzynski equality (JE) can be reconsidered in this contracted picture. The example to be considered here is a macromolecular harmonic oscillator with a Hooke's spring constant, k, that during the time interval (0, τ) linearly changes from k1to k2. When the transition is performed sufficiently slowly, we obtain JE in the form: < exp[−βW] > = √k1 k2 ⁄ However, when the transition is performed more rapidly the equality is lost.
Structure-Aware Image Segmentation with Homotopy Warping

Besides per-pixel accuracy, topological correctness is also crucial for the segmentation of images with fine-scale structures, e.g., satellite images and biomedical images. In this paper, by leveraging the theory of digital topology, we identify pixels in an image that are critical for topology. By focusing on these critical pixels, we propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy. To efficiently identify these topologically critical pixels, we propose a new algorithm exploiting the distance transform. The proposed algorithm, as well as the loss function, naturally generalize to different topological structures in both 2D and 3D settings. The proposed loss function helps deep nets achieve better performance in terms of topology-aware metrics, outperforming state-of-the-art structure/topology-aware segmentation methods.
MSR: Making Self-supervised learning Robust to Aggressive Augmentations

Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augmentations have been introduced to further improve the diversity of training pairs. However, aggressive augmentations may distort images' structures leading to a severe semantic shift problem that augmented views of the same image may not share the same semantics, thus degrading the transfer performance. To address this problem, we propose a new SSL paradigm, which counteracts the impact of semantic shift by balancing the role of weak and aggressively augmented pairs. Specifically, semantically inconsistent pairs are of minority and we treat them as noisy pairs. Note that deep neural networks (DNNs) have a crucial memorization effect that DNNs tend to first memorize clean (majority) examples before overfitting to noisy (minority) examples. Therefore, we set a relatively large weight for aggressively augmented data pairs at the early learning stage. With the training going on, the model begins to overfit noisy pairs. Accordingly, we gradually reduce the weights of aggressively augmented pairs. In doing so, our method can better embrace the aggressive augmentations and neutralize the semantic shift problem. Experiments show that our model achieves 73.1% top-1 accuracy on ImageNet-1K with ResNet-50 for 200 epochs, which is a 2.5% improvement over BYOL. Moreover, experiments also demonstrate that the learned representations can transfer well for various downstream tasks.
What do tokens know about their characters and how do they know it?

Pre-trained language models (PLMs) that use subword tokenization schemes can succeed at a variety of language tasks that require character-level information, despite lacking explicit access to the character composition of tokens. Here, studying a range of models (e.g., GPT- J, BERT, RoBERTa, GloVe), we probe what word pieces encode about character-level information by training classifiers to predict the presence or absence of a particular alphabetical character in a token, based on its embedding (e.g., probing whether the model embedding for “cat” encodes that it contains the character “a”). We find that these models robustly encode character-level information and, in general, larger models perform better at the task. We show that these results generalize to characters from non-Latin alphabets (Arabic, Devanagari, and Cyrillic). Then, through a series of experiments and analyses, we investigate the mechanisms through which PLMs acquire English-language character information during training and argue that this knowledge is acquired through multiple phenomena, including a systematic relationship between particular characters and particular parts of speech, as well as natural variability in the tokenization of related strings.