Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast routing table construction using small messages: extended abstract

We describe a distributed randomized algorithm to construct routing tables. Given 0< ε <= 1/2, the algorithm runs in time ~O(n1/2+ε + HD), where n is the number of nodes and HD denotes the diameter of the network in hops (i.e., as if the network is unweighted). The weighted length of the produced routes is at most O(ε-1log ε-1) times the optimal weighted length. This is the first algorithm to break the Omega(n) complexity barrier for computing weighted shortest paths even for a single source. Moreover, the algorithm nearly meets the ~Omega(n1/2 + HD) lower bound for distributed computation of routing tables and approximate distances (with optimality, up to polylog factors, for ε=1/log n). The presented techniques have many applications, including improved distributed approximation algorithms for Generalized Steiner Forest, all-pairs distance estimation, and estimation of the weighted diameter.
Anatomical and ontogenetic reassessment of the Ediacaran frond Arborea arborea and its placement within total group Eumetazoa

Organisms in possession of a frondose body plan are amongst the oldest and most enigmatic members of the soft‐bodied Ediacaran macrobiota. Appraisal of specimens from the late Ediacaran Ediacara Member of South Australia reveals that the frondose taxon Arborea arborea probably possessed a fluid‐filled holdfast disc, the size and form of which could vary within populations. Mouldic preservation of internal anatomical features provides evidence for tissue differentiation, and for bundles of tubular structures within the stalk of the organism. These structures connect in a fascicled arrangement to individual lateral branches, before dividing further into individual units housed on those branches. The observed fascicled branching arrangement, which seemingly connects individual units to the main body of the organism, is consistent with a biologically modular construction for Arborea, and raises the possibility of a colonial organization. In conjunction with morphological characters previously recognized by other authors, including apical‐basal and front‐back differentiation, we propose that to the exclusion of all alternative known possibilities, Arborea can be resolved as a total group eumetazoan.
Growth of Moonshine Yarrow on a Limited Moisture Budget

Abstract Yarrow is an herbaceous perennial that is highly recommended for landscape gardens in many parts of the United States. However, performance data of yarrow produced under limited irrigation conditions is lacking. During 2004 and 2005, we studied the growth and physiology of yarrow (Achillea ‘Moonshine’) maintained as well irrigated controls or irrigated when there was a 30 or 60% depletion of moisture in the growing substrate. Plants irrigated at the 60% moisture level had the lowest predawn water potential, stomatal conductance and transpiration rate. Cell osmotic potential and relative water content data suggest that yarrow might be able to withstand prolonged exposure to drought. In 2005, net assimilation rate of well irrigated plants (0.317 mg/cm2/d) was almost twice as high (0.179 mg/cm2/d) as that of plants maintained at the 60% moisture level. This suggests that a 60% moisture depletion level had a very significant impact on carbon assimilation. In 2005, leaf area of plants irrigated at the.
Technical Note - Preservation of Quasi-K-Concavity and Its Applications

In this paper, we establish a new preservation property of quasi-K-concavity under certain optimization operations. One important application of the result is to analyze joint inventory-pricing models for single-product periodic-review inventory systems with concave ordering costs. At each period, an ordering quantity and a selling price of the product are determined simultaneously. Demand is random but sensitive to the price. The objective is to maximize the total expected discounted profit over a finite planning horizon. Assuming that demand is a deterministic function of the selling price plus a random perturbation with a positive Polya or uniform distribution, we show that a generalized (s, S, p) policy is optimal.
The Holocene Thermal Maximum around Svalbard, Arctic North Atlantic; molluscs show early and exceptional warmth

Shallow marine molluscs that are today extinct close to Svalbard, because of the cold climate, are found in deposits there dating to the early Holocene. The most warmth-demanding species found, Zirfaea crispata, currently has a northern limit 1000 km farther south, indicating that August temperatures on Svalbard were 6°C warmer at around 10.2–9.2 cal. ka BP, when this species lived there. The blue mussel, Mytilus edulis, returned to Svalbard in 2004 following recent warming, and after almost 4000 years of absence, excluding a short re-appearance during the Medieval Warm Period 900 years ago. Mytilus first arrived in Svalbard at 11 cal. ka BP, indicating that the climate was then as least as warm as present. This first warm period lasted from 11 to 9 cal. ka BP and was followed by a period of lower temperatures 9–8.2 cal. ka BP. After 8.2 cal. ka, the climate around Svalbard warmed again, and although it did not reach the same peak in temperatures as prior to 9 ka, it was nevertheless some 4°C warmer than present between 8.2 and 6 cal. ka BP. Thereafter, a gradual cooling brought temperatures to the present level at about 4.5 cal. ka BP. The warm early-Holocene climate around Svalbard was driven primarily by higher insolation and greater influx of warm Atlantic Water, but feedback processes further influenced the regional climate.
Grasp Recognition for Uncalibrated Data Gloves: A Machine Learning Approach

This paper presents a comparison of various machine learning methods applied to the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove. Instead, raw sensor readings are used as input features that are directly mapped to different categories of hand shapes. An experiment was carried out in which test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was comprehensively analyzed using numerous classification techniques provided in an open-source machine learning toolbox. Evaluated machine learning methods are composed of (a) 38 classifiers including different types of function learners, decision trees, rule-based learners, Bayes nets, and lazy learners; (b) data preprocessing using principal component analysis (PCA) with varying degrees of dimensionality reduction; and (c) five meta-learning algorithms under various configurations where selection of suitable base classifier combinations was informed by the results of the foregoing classifier evaluation. Classification performance was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably good to highly reliable recognition of grasp types can be achieveddepending on whether or not the glove user is among those training the classifiereven with uncalibrated data gloves. (2) We identify the best performing classification methods for the recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.
Support vector machine-based expert system for reliable heartbeat recognition

This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.
Social registries for social assistance and beyond : a guidance note and assessment tool

This paper makes several contributions. First, it presents a ‘guidance note’ on the framework for Social Registries, anchoring the definition of these systems in their functions along the Delivery Chain and their social policy role as inclusion systems, while clarifying terminology in a manner that is consistent with IT standards in the discussion of their architecture as information systems. Second, it illustrates the diverse typologies and trajectories of country experiences with Social Registries with respect to their (a) institutional arrangements (central and local); (b) use as inclusion systems (coverage, single or multi-program use, static or dynamic intake and registration); and (c) structure as information systems (structure of data management; degree and us of interoperability with other systems). These patterns primarily derive from a review of Social Registries in a sample of 20 countries), (Azerbaijan, Brazil, Chile, China, Colombia, the Dominican Republic, Djibouti, Georgia, Indonesia, Macedonia, Mali, Mauritius, Mexico, Montenegro, Pakistan, the Philippines, Senegal, Sierra Leone, Turkey, and Yemen). The paper also draws on experience in other countries (Kenya, Rwanda, Nigeria, Egypt, Jordan, Vietnam, India, Estonia, Belgium, the US, Canada, Australia, and others) to illustrate specific points. Third, this paper develops a basic ‘Assessment Tool’ covering the core building blocks of Social Registries using a ‘checklist’ style of questions. Given the wide diversity of Social Registries in both their role in social policy and in their architecture, the approach is not prescriptive: it does not advocate for any specific model or blueprint for Social Registries. Any diagnostics or recommendations that emerge from use of this Guidance Note and Assessment Tool will be country specific. Some key take-away messages include: (a) the importance of recognizing both the role of the ‘front lines’ for outreach, intake and registration (Social Registries as inclusion systems) and the ‘back office’ functions of Social Registries as information systems; (b) the potential power of Social Registries as integrated and dynamic gateways for inclusion; (c) the recognition that Social Registries are generally part of end-to-end systems for specific programs, integrated social protection information systems, and/or even ‘whole-of-government’ approaches; and (d) there is significant diversity in the typology and trajectories of Social Registries across countries and over time.
Observed Covariations of Aerosol Optical Depth and Cloud Cover in Extratropical Cyclones

Using NASA Moderate Resolution Imaging Spectroradiometer aerosol optical depth and total cloud cover retrievals, CloudSat‐CALIPSO cloud profiles, and a database of extratropical cyclones and frontal boundary locations, relationships between changes in aerosol optical depth and cloud cover in extratropical cyclones occurring over Northern Hemisphere oceans are examined. A reanalysis data set is used to constrain column water vapor and ascent strength in the cyclones in an attempt to distinguish their impact on cloud cover from the effect of changes in aerosol loading. The results suggest that high aerosol optical depth cyclones exhibit larger middle‐ and high‐level cloud cover than their low aerosol optical depth counterparts. However, the opposite behavior is found for low‐level cloud cover. These relationships are found to depend on the large‐scale environment, in particular the column water vapor and vertical motion. Despite the inability of the observations to provide a causal physical link between aerosol load and cloud cover, these results can help to constrain and evaluate model simulations.
Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression

Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behavior, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.
Human Adenovirus 52 Uses Sialic Acid-containing Glycoproteins and the Coxsackie and Adenovirus Receptor for Binding to Target Cells

Most adenoviruses attach to host cells by means of the protruding fiber protein that binds to host cells via the coxsackievirus and adenovirus receptor (CAR) protein. Human adenovirus type 52 (HAdV-52) is one of only three gastroenteritis-causing HAdVs that are equipped with two different fiber proteins, one long and one short. Here we show, by means of virion-cell binding and infection experiments, that HAdV-52 can also attach to host cells via CAR, but most of the binding depends on sialylated glycoproteins. Glycan microarray, flow cytometry, surface plasmon resonance and ELISA analyses reveal that the terminal knob domain of the long fiber (52LFK) binds to CAR, and the knob domain of the short fiber (52SFK) binds to sialylated glycoproteins. X-ray crystallographic analysis of 52SFK in complex with 2-O-methylated sialic acid combined with functional studies of knob mutants revealed a new sialic acid binding site compared to other, known adenovirus:glycan interactions. Our findings shed light on adenovirus biology and may help to improve targeting of adenovirus-based vectors for gene therapy.
Differential interactions of two introduced piscivorous salmonids with a native cyprinid in lentic systems: implications for conservation of roundtail chub.

Abstract The effects of multiple nonnative piscivore species on native prey species in lentic systems are poorly understood. We studied the relative predation risks posed by two piscivorous salmonids (brown trout Salmo trutta and lake trout Salvelinus namaycush) to endemic roundtail chub Gila robusta in two lakes within the upper Colorado River basin. Gill nets were set in various habitat types to study habitat use by the three species before and after the onset of summer stratification. Roundtail chub and brown trout were mainly associated with shallow-water habitats, and this association did not change with thermal period. By contrast, lake trout habitat use changed with thermal period as fish moved from shallow areas in the spring to deepwater habitat after stratification. These habitat use patterns indicate that roundtail chub are more susceptible to predation by brown trout than by lake trout because both roundtail chub and brown trout occupy the littoral zone for a prolonged period. Diet data indica.
Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games

Integrating LLM and reinforcement learning (RL) agent effectively to achieve complementary performance is critical in high stake tasks like cybersecurity operations. In this study, we introduce SecurityBot, a LLM agent mentored by pre-trained RL agents, to support cybersecurity operations. In particularly, the LLM agent is supported with a profile module to generated behavior guidelines, a memory module to accumulate local experiences, a reflection module to re-evaluate choices, and an action module to reduce action space. Additionally, it adopts the collaboration mechanism to take suggestions from pre-trained RL agents, including a cursor for dynamic suggestion taken, an aggregator for multiple mentors' suggestions ranking and a caller for proactive suggestion asking. Building on the CybORG experiment framework, our experiences show that SecurityBot demonstrates significant performance improvement compared with LLM or RL standalone, achieving the complementary performance in the cybersecurity games.
The cognitive niche: Coevolution of intelligence, sociality, and language

Although Darwin insisted that human intelligence could be fully explained by the theory of evolution, the codiscoverer of natural selection, Alfred Russel Wallace, claimed that abstract intelligence was of no use to ancestral humans and could only be explained by intelligent design. Wallace's apparent paradox can be dissolved with two hypotheses about human cognition. One is that intelligence is an adaptation to a knowledge-using, socially interdependent lifestyle, the “cognitive niche. ” This embraces the ability to overcome the evolutionary fixed defenses of plants and animals by applications of reasoning, including weapons, traps, coordinated driving of game, and detoxification of plants. Such reasoning exploits intuitive theories about different aspects of the world, such as objects, forces, paths, places, states, substances, and other people's beliefs and desires. The theory explains many zoologically unusual traits in Homo sapiens, including our complex toolkit, wide range of habitats and diets, extended childhoods and long lives, hypersociality, complex mating, division into cultures, and language (which multiplies the benefit of knowledge because know-how is useful not only for its practical benefits but as a trade good with others, enhancing the evolution of cooperation). The second hypothesis is that humans possess an ability of metaphorical abstraction, which allows them to coopt faculties that originally evolved for physical problem-solving and social coordination, apply them to abstract subject matter, and combine them productively. These abilities can help explain the emergence of abstract cognition without supernatural or exotic evolutionary forces and are in principle testable by analyses of statistical signs of selection in the human genome.
ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching

The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT, which have come to dominate a broad range of NLP tasks. Despite their superior accuracy, LLMs present unique challenges in practical inference, concerning the compute and memory-intensive nature. Thanks to the autoregressive characteristic of LLM inference, KV caching for the attention layers in Transformers can effectively accelerate LLM inference by substituting quadratic-complexity computation with linear-complexity memory accesses. Yet, this approach requires increasing memory as demand grows for processing longer sequences. The overhead leads to reduced throughput due to I/O bottlenecks and even out-of-memory errors, particularly on resource-constrained systems like a single commodity GPU. In this paper, we propose ALISA, a novel algorithm-system co-design solution to address the challenges imposed by KV caching. On the algorithm level, ALISA prioritizes tokens that are most important in generating a new token via a Sparse Window Attention (SWA) algorithm. SWA introduces high sparsity in attention layers and reduces the memory footprint of KV caching at negligible accuracy loss. On the system level, ALISA employs three-phase token-level dynamical scheduling and optimizes the trade-off between caching and recomputation, thus maximizing the overall performance in resource-constrained systems. In a single GPU-CPU system, we demonstrate that under varying workloads, ALISA improves the throughput of baseline systems such as FlexGen and vLLM by up to 3X and 1.9X, respectively.
Inequality of Opportunities Among Ethnic Groups in the Philippines

This paper contributes to the scant body of literature on inequalities among and within ethnic groups in the Philippines by examining both the vertical and horizontal measures in terms of opportunities in accessing basic services such as education, electricity, safe water, and sanitation. The study also provides a glimpse of the patterns of inequality in Mindanao. The results show that there are significant inequalities in opportunities in accessing basic services within and among ethnic groups in the Philippines. Muslims (particularly indigenous people) are the worst-off ethnic groups while the non-indigenous/non-Muslim groups are the better-off groups. Disparities in terms of literacy rate and access to electricity and sanitation between ethnic groups, however, appear to be narrowing between 2000 and 2010.
Asymptotic Stability Region of Slotted Aloha

We analyze the stability of standard, buffered, slotted-Aloha systems. Specifically, we consider a set of <formula formulatype="inline"><tex Notation="TeX">$N$</tex></formula> users, each equipped with an infinite buffer. Packets arrive into user <formula formulatype="inline"><tex Notation="TeX">$i$</tex> </formula>'s buffer according to some stationary ergodic Markovian process of intensity <formula formulatype="inline"> <tex Notation="TeX">$\lambda_{i}$</tex></formula>. At the beginning of each slot, if user <formula formulatype="inline"> <tex Notation="TeX">$i$</tex></formula> has packets in its buffer, it attempts to transmit a packet with fixed probability <formula formulatype="inline"><tex Notation="TeX">$p_{i}$</tex></formula> over a shared resource/channel. The transmission is successful only when no other user attempts to use the channel. The stability of such systems has been open since their very first analysis in 1979 by Tsybakov and Mikhailov. In this paper, we propose an approximate stability condition that is provably exact when the number of users <formula formulatype="inline"> <tex Notation="TeX">$N$</tex></formula> grows large. We provide theoretical evidence and numerical experiments to explain why the proposed approximate stability condition is extremely accurate even for systems with a restricted number of users (even two or three).