Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Prediction of Aging-Related Genes From a Context-Specific Protein Interaction Subnetwork

Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes’ protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.
Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines

Deep reinforcement learning excels in various domains but lacks generalizability and interoperability. Programmatic RL methods [56, 39] reformulate solving RL tasks as synthesizing interpretable programs that can be executed in the environments. Despite encouraging results, these methods are limited to short-horizon tasks. On the other hand, representing RL policies using state machines [27] can inductively generalize to long-horizon tasks; however, it struggles to scale up to acquire diverse and complex behaviors. This work proposes Program Machine Policies (POMPs), which bridge the advantages of programmatic RL and state machine policies, allowing for the representation of complex behaviors and the address of long-term tasks. Specifically, we introduce a method that can retrieve a set of effective, diverse, compatible programs. Then, we use these programs as modes of a state machine and learn a transition function to transition among mode programs, allowing for capturing long-horizon repetitive behaviors. Our proposed framework outperforms programmatic RL and deep RL baselines on various tasks and demonstrates the ability to generalize to even longer horizons without any fine-tuning inductively. Ablation studies justify the effectiveness of our proposed search algorithm for retrieving a set of programs as modes.
Classification and tracking of items on a moving conveyor belt using convolutional networks and image processing

The prolonged waiting time at supermarket checkout lines poses a significant challenge to the shopping experience, impacting customer satisfaction and operational efficiency. This paper presents a prototype that addresses this issue by utilizing computer vision and deep learning. The model, incorporating convolutional neural networks such as YOLO v4 tiny and YOLO v5 small, along with tools like OpenCV and Roboflow for dataset management, achieves a remarkable 98% mean average precision for two-class detection. It efficiently detects, classifies, tracks, and counts items on a mobile supermarket conveyor belt. Additionally, we introduce a versatile framework designed for seamless integration into real-world applications. It comprises a customizable monitoring application and simulator that facilitates synthetic image data generation. Managing diverse items in a supermarket presents a major challenge for data gathering, labeling, and training. In that sense, the importance of customizable monitoring and simulation tools is highlighted, emphasizing their practical role. Our findings demonstrate the feasibility of maintaining a minimal 0% to 2.85% precision tradeoff while using half of the data as synthetic for two-class detection, indicating potential practicality in supermarkets with proper scaling. In summary, this study brings tangible benefits to both customers and retailers, offering a potential to streamline, speed up, and cut costs in the supermarket checkout process.
Efficient Test-Time Adaptation of Vision-Language Models

Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time. Though prior studies have achieved very promising performance, they involve intensive computation which is severely unaligned with test-time adaptation. We design TDA, a training-free dynamic adapter that enables effective and efficient test-time adaptation with vision-language models. TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys. Leveraging the key-value cache, TDA allows adapting to test data gradually via progressive pseudo label refinement which is super-efficient without incurring any backpropagation. In addition, we introduce negative pseudo labeling that alleviates the adverse impact of pseudo label noises by assigning pseudo labels to certain negative classes when the model is uncertain about its pseudo label predictions. Extensive experiments over two benchmarks demonstrate TDA's superior effectiveness and efficiency as compared with the state-of-the-art. The code has been released in \url{https://kdiaaa.github. io/tda/}.
Local inverses of shift maps along orbits of flows

Let $M$ be a smooth manifold and $F$ be a vector field on $M$. My article ["Smooth shifts along trajectories of flows", Topol. Appl. 130 (2003) 183-204, arXiv:math/0106199] concerning the homotopy types of the group of diffeomorphisms preserving orbits of $F$ contains two errors. They imply that the principal statement of that paper holds under additional assumptions on $F$. Unfortunately this result was essentially used in another paper of mine ["Homotopy types of stabilizers and orbits of Morse functions on surfaces" Ann. Glob. Anal. Geom. , 29 no. 3, (2006) 241-285, arXiv:math/0310067]. The aim of this article is to expose the results of the first paper in a right way, extend them to a larger class of flows with degenerate singularities, and show that the results of the second paper remain true.
Holocene Hydrological Changes Inferred from Alluvial Stream Entrenchment in North Tian Shan (Northwestern China)

We analyze the possible contribution of climate change or tectonics on fluvial incision from the study of a case example along the northern flank of Tian Shan. The rivers that exit the high range fed large alluvial fans by the end of the last glacial period. They have since deeply entrenched the piedmont by as much as 300 m. We have surveyed several terraces that were cut and abandoned during river entrenchment, providing information on intermediate positions of the riverbed during downcutting. They suggest a gradual decline in river slope during a major phase of incision throughout the Holocene. Tectonic uplift affects only a zone about 5 km wide, corresponding to a growing anticline, and is shown to account for about 10% of total incision. Incision was therefore most probably driven by climate change. From observed fluvial incision, we estimate the water discharge in excess of that needed to carry the sediments supplied by hillslope erosion in the headwaters. We used a model based on a transport‐limited erosion law. The model predicts relaxation process with entrenchment in the upper reach, downstream progradation of the incision‐sedimentation line, and a progressive decrease of river slope during incision consistent with our observations. According to this model, river slope might be used as a proxy for specific discharge and then for volumetric discharge, provided that an assumption is made about river width variations. We conclude that river incision in the study area has resulted from dynamic adjustment of the hydrological system to the settlement of wetter conditions in the early Holocene, when water discharge might have been about three times as high as at present. Then, a rather arid climate with enhanced seasonality has likely prevailed from the mid‐Holocene (∼6 ka B.P.) until now.
Gastrointestinal hormones in short bowel syndrome. Peptide YY may be the 'colonic brake' to gastric emptying.

BACKGROUND: Short bowel patients with a jejunostomy have large volume stomal outputs, which may in part be due to rapid gastric emptying of liquid. Short bowel patients with a preserved colon do not have such a high stool output and gastric emptying of liquid is normal. AIMS: To determine if differences in the gastric emptying rate between short bowel patients with and without a colon can be related to gastrointestinal hormone changes after a meal. SUBJECTS: Seven short bowel patients with no remaining colon (jejunal length 30-160 cm) and six with jejunum in continuity with a colon (jejunal length 25-75 cm), and 12 normal subjects. METHODS: The subjects all consumed a 640 kcal meal; blood samples were taken for 180 minutes for measurement of gastrointestinal hormones. RESULTS: Patients with a colon had high fasting peptide YY values (median 71 pmol/l with a colon; 11 pmol/l normal subjects, p < 0.005) with a normal postprandial rise, but those without a colon had a low fasting (median 7 pmol/l, p = 0.076) and a reduced postprandial peptide YY response (p < 0.050). Motilin values were high in some patients without a colon. In both patient groups fasting and postprandial gastrin and cholecystokinin values were high while neurotensin values were low. There were no differences between patient groups and normal subjects in enteroglucagon, pancreatic polypeptide, or somatostatin values. CONCLUSIONS: Low peptide YY values in short bowel patients without a colon may cause rapid gastric emptying of liquid. High values of peptide YY in short bowel patients with a retained colon may slow gastric emptying of liquid and contribute to the "colonic brake'.
Risk and Determinants of Dementia in Patients with Mild Cognitive Impairment and Brain Subcortical Vascular Changes: A Study of Clinical, Neuroimaging, and Biological Markers—The VMCI-Tuscany Study: Rationale, Design, and Methodology

Dementia is one of the most disabling conditions. Alzheimer's disease and vascular dementia (VaD) are the most frequent causes. Subcortical VaD is consequent to deep-brain small vessel disease (SVD) and is the most frequent form of VaD. Its pathological hallmarks are ischemic white matter changes and lacunar infarcts. Degenerative and vascular changes often coexist, but mechanisms of interaction are incompletely understood. The term mild cognitive impairment defines a transitional state between normal ageing and dementia. Pre-dementia stages of VaD are also acknowledged (vascular mild cognitive impairment, VMCI). Progression relates mostly to the subcortical VaD type, but determinants of such transition are unknown. Variability of phenotypic expression is not fully explained by severity grade of lesions, as depicted by conventional MRI that is not sensitive to microstructural and metabolic alterations. Advanced neuroimaging techniques seem able to achieve this. Beside hypoperfusion, blood-brain-barrier dysfunction has been also demonstrated in subcortical VaD. The aim of the Vascular Mild Cognitive Impairment Tuscany Study is to expand knowledge about determinants of transition from mild cognitive impairment to dementia in patients with cerebral SVD. This paper summarizes the main aims and methodological aspects of this multicenter, ongoing, observational study enrolling patients affected by VMCI with SVD.
Opportunistic Routing and Scheduling for Wireless Networks

In spatial time division multiple access wireless mesh networks, not all links can be activated simultaneously, as links scheduled for transmission must satisfy the specified SINR requirements. Previously, slot assignment has been done on a link basis, where a set of links is selected for transmission in a given slot. However, if selected links are in deep fade or have no traffic to transmit, the slot is wasted. Thus, a node-based scheme was proposed, where a set of nodes is selected for transmission. Which link to be used by a node depends on the links’ instantaneous traffic load. Although this allows us to exploit multi-user diversity, it creates a planning discrepancy: slot assignment is designed based on long-term channel statistics, but scheduling on short-term channel fading conditions. Consequently, the performance gain of the node-based scheme is not consistent: it is marginal under certain scenarios. To avoid the design discrepancy, we develop a new slot-assignment and routing framework in this paper. The new approach incorporates short-term channel fading statistics to optimize the long term slot assignment, routing and scheduling simultaneously. Hence, multi-user diversity can be exploited more efficiently. Not only is the performance gain of the resulting system significant (can be as much as 64% higher throughput than the scheme introduced by Chen and Lea), it is also less topology dependent compared with the one by Chen and Lea.
Blood Group‐Active Surface Molecules of the Human Red Blood Cell

Abstract. The surface of the human red blood cell is dominated by a small number of abundant blood group active proteins. The major proteins are the anion transport protein (band 3) which has AB(H) activity, and Glycophorin A which has MN activity. Band 3 and Glycophorin A are of equal abundance in the normal red cell membrane (approximately 106 copies of each) and the two proteins may associate together as a complex. The glucose transporter (band 4.5) has AB(H) activity and there are about 5 times 105 copies/red cell. Several polypeptides associate together to form the Rh complex. The major components of this complex (abundance 1–2 times 105 copies/red cell) are polypeptides of Mr 30,000, polypeptides of Mr 45,000–100,000 and Glycophorin B. The antigens of the Rh blood group system appear to be associated with the polypeptides of Mr 30,000 and those of Mr 45,000–100,000 (the latter also express AB(H) activity). Glycophorin B expresses the blood group ‘N’ antigen and the Ss antigens. Glycophorins C and D carry the Gerbich antigens and, together, these polypeptides comprise approximately 105 copies/red cell. The complete protein sequence of all the above‐mentioned proteins is known, except for the Mr 30,000 and Mr 45,000–100,000 polypeptides of the Rh complex for which only partial sequences are available, and Glycophorin D, the sequence of which can be inferred from that of Glycophorin C. Several of the minor blood group active proteins at the red cell surface (abundance <1.2 times 104/red cell) have been the subject of recent studies. The polypeptide expressing Cromer‐related blood group antigens has been identified as decay‐accelerating factor and that carrying the Ina/Inb antigens as CD44. The protein sequence of both of these proteins has been deduced form nucleotide sequencing. The polypeptides expressing Kell antigens, Lutheran antigens, Fy antigens, and LW antigens have also been identified and partially characterised.
Factors affecting the tendency to leave the organization using an intelligent model based on multi-objective neural network and genetic algorithms

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Modeling of Sigma-Delta Modulator Non-idealities with Two Step Quantization in MATLAB/SIMULINK

An architecture to simplify the circuit implementation of analog-to-digital (A/D) converter in a sigma-delta (S? ) modulator is proposed. The two-step quantization technique is utilized to design architecture of S? modulator. The architecture is based on dividing the A/D conversion into two time steps for achieving resolution improvement without decreasing speed. The novel architecture is designed to obtain high dynamic range of input signal, high signal-to-noise ratio and high reliability. Switched capacitor (SC) modulator performance is prone to various nonidealities, which affects overall circuit performance. In this paper a set of models are proposed which takes into account SC S? modulator nonidealities, such as sampling jitter, kT/C noise, and operational amplifier parameters (noise, finite dc gain, finite bandwidth, slew-rate and saturation voltages). Each nonidealities are modelled mathematically and their behaviour is verified using different analysis in MATLAB Simulink. Simulation results on a second-order SC S? modulator with two step quantization demonstrate the validity of the models proposed.
An Indigenous Perspective on Chinese Depression

Heinrich, Norenzayan & Heine (2010) published an article named “The weirdest people in the world? ” in the Journal of Behavioral and Brain Sciences in 2010. They made strong critique of the “weird” phenomenon in psychology research area. They claimed that 96% samples of psychological research published 2003 to 2007 were drawn from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies which house just 12% of the world’s population. More and more psychologists became aware of the cultural bias of the western psychology and called for more cultural indigenous perspective to do research and clinical interventions.
Towards mobility-aware predictive radio access: modeling; simulation; and evaluation in LTE networks

Novel radio access techniques that leverage mobility predictions are receiving increasing interest in recent literature. The essence of these schemes is to lookahead at the future rates users will experience, and then devise long-term resource allocation strategies. For instance, a YouTube video user moving towards the cell edge can be prioritized to pre-buffer additional video content before poor coverage commences. While the potential of mobility-aware resource allocation has recently been demonstrated, several practical design aspects and evaluation approaches have not yet been addressed due to the complexity of the problem. Furthermore, since prior works have focused on specific applications there is also a strong need for a unified framework that can support different user and network requirements. For this purpose, we present a novel two-stage Predictive Radio Access Network (P-RAN) framework that can efficiently leverage both future data rate predictions in the order of tens of seconds, and instantaneous fast fading at the millisecond level. We also show how the framework can be implemented within the open source Network Simulator 3 (ns-3) LTE module, and apply it to optimize stored video delivery. A thorough set of performance tests are then conducted to assess the performance gains and investigate sensitivity to various prediction errors. Our results indicate that P-RANs can jointly improve both service quality and transmission efficiency. Additionally, we also observe that P-RAN performance can be further improved by modeling prediction uncertainty and developing robust allocation techniques.
Mining Frequent Itemsets from Uncertain Databases using probabilistic support

Mining of frequent itemsets is one of the popular knowledge discovery and data mining tasks. The frequent itemset mining algorithms find itemsets from traditional transaction databases, in which the content of each transaction i.e. items is definitely known and precise. There are many real-life applications like location-based services, sensor monitoring systems in which the content of transactions is uncertain. This initiates the requirement of uncertain data mining. The frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard certain transaction databases. The consideration of existential uncertainty of itemsets, indicating the probability that an itemset occurs in a transaction, makes the traditional techniques inapplicable. Hence the mining methods like the Apriori and the tree based mining needs to be modified for handling the uncertain data. The uncertain data has attribute as well as tuple uncertainty. This paper introduces the techniques for mining frequent itemsets from uncertain databases that makes use of the probabilistic support concept which considers the aspects of uncertain data completely.
Photovoltaic module reliability model based on field degradation studies

Crystalline silicon photovoltaic (PV) modules are often stated as being the most reliable element in PV systems. This presumable high reliability is reflected by their long power warranty periods. In agreement with these long warranty times, PV modules have a very low total number of returns, the exceptions usually being the result of catastrophic failures. Up to now, failures resulting from degradation are not typically taken into consideration because of the difficulties in measuring the power of an individual module in a system. However, lasting recent years PV systems are changing from small isolated systems to large grid‐connected power stations. In this new scenario, customers will become more sensitive to power losses and the need for a reliability model based on degradation may become of utmost importance. In this paper, a PV module reliability model based on degradation studies is presented. The main analytical functions of reliability engineering are evaluated using this model and applied to a practical case, based on state‐of‐the‐art parameters of crystalline silicon PV technology. Relevant and defensible power warranties and other reliability data are obtained with this model based on measured degradation rates and time‐dependent power variability. In the derivation of the model some assumptions are made about the future behaviour of the products—i.e. linear degradation rates—although the approach can be used for other assumed functional profiles as well. The method documented in this paper explicitly shows manufacturers how to make reasonable and sensible warranty projections. Copyright © 2008 John Wiley & Sons, Ltd.
Capacity of a Modulo-Sum Simple Relay Network

This paper presents the capacity of a modulo-sum simple relay network. In previous work related to this paper, capacity was characterized for the case where the noise was transmitted to the relay. And the closed-form capacity was derived only for the noise with a Bernoulli-( distribution. However, in this paper, the source is transmitted to the relay, and a more general case of noise with an arbitrary Bernoulli-( distribution, , is considered. The relay observes a corrupted version of the source, uses a quantize-and-forward strategy, and transmits the encoded codeword through a separate dedicated channel to the destination. The destination receives both from the relay and source. This paper assumes that the channel is discrete and memoryless. After deriving the achievable capacity theorem (i.e., the forward theorem) for the binary symmetric simple relay network, this paper proves that the capacity is strictly below the cut- set bound. In addition, this paper presents the proof of the converse theorem. Finally, this paper extends the capacity of the binary symmetric simple relay network to that of an m-ary modulo-sum relay network.