Miracle-dz/UniFAR-FTUs huggingface dataset:
Random Function Priors for Correlation Modeling

The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,\theta)$, where $\theta\mathrel{\mathop:}=(\theta_k)_{k\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\theta_k$ used to express $X_n$. In this paper, we introduce random function priors for $Z_n$ for modeling correlations among its $K$ dimensions $Z_{n1}$ through $Z_{nK}$, which we call \textit{population random measure embedding} (PRME). Our model can be viewed as a generalized paintbox model~\cite{Broderick13} using random functions, and can be learned efficiently with neural networks via amortized variational inference. We derive our Bayesian nonparametric method by applying a representation theorem on separately exchangeable discrete random measures.
Asymptotics of multivariate sequences in the presence of a lacuna

We explain a discontinuous drop in the exponential growth rate for certain multivariate generating functions at a critical parameter value, in even dimensions $d \geq 4$. This result depends on computations in the homology of the algebraic variety where the generating function has a pole. These computations are similar to, and inspired by, a thread of research in applications of complex algebraic geometry to hyperbolic PDEs, going back to Leray, Petrowski, Atiyah, Bott and Garding. As a consequence, we give a topological explanation for certain asymptotic phenomenon appearing in the combinatorics and number theory literature. Furthermore, we show how to combine topological methods with symbolic algebraic computation to determine explicitly the dominant asymptotics for such multivariate generating functions. This in turn enables the rigorous determination of integer coefficients in the Morse-Smale complex, which are difficult to determine using direct geometric methods.
Comparative analysis of PV module efficiency for different types of silicon materials considering the effects of environmental parameters

To meet the energy demand, solar photovoltaic has already emerged as a convincing renewable energy technology. Solar Photovoltaic (PV) modules are one of the most effective, sustainable, and eco-friendly systems in recent world. Different types of PV solar technologies like Mono crystalline silicon, poly crystalline silicon, amorphous silicon, thin film are the most popular technologies to produce electricity. Their module performance and efficiency depend on the electrical and environment parameters of the PV materials. This paper presents some theoretical evaluations for variation in the efficiency of energy for different types of photovoltaic (PV) module under changes of environmental parameters. A proper comparison is developed among the mono-crystalline, polycrystalline and amorphous material using the model parameters. This work also illustrates the variation of fill factor and efficiency of a specific photovoltaic module under real operating conditions. MATLAB is used to investigate the P-V characteristics to evaluate the maximum power point of solar photovoltaic cell considering the effect of temperature and solar radiation.
Sustainable Computing: Informatics and Systems Leveraging Weather Forecasts in Renewable Energy Systems

Systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since it cannot precisely scale its usage to match its supply. Instead, the system must choose how to satisfy its demands based on its current energy reserves and predictions of its future energy supply. In this paper, we show that prediction strategies that use weather forecasts are more accurate than prediction strategies based on the past, and are capable of improving the performance of a variety of systems. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a solar or wind energy harvesting prediction, and quantify its accuracy. We then compare the performance of three types of energy harvesting systems—a lexicographically fair sensor network, an off-the-grid sensor testbed, and a solar-powered smart home—using prediction models based on both forecasts and the past. In each case, forecast-based predictions significantly improve system performance.
Photoinduced valence dynamics in 
EuNi2(Si0.21Ge0.79)2
 studied via time-resolved x-ray absorption spectroscopy

The photoinduced valence dynamics of ${\mathrm{EuNi}}_{2}{({\mathrm{Si}}_{0.21}{\mathrm{Ge}}_{0.79})}_{2}$ were investigated using time-resolved x-ray absorption spectroscopy for Eu ${M}_{5}$ edge. Through the pump-probe technique with synchrotron x-ray and Ti:sapphire laser pulses, a photoinduced valence transition was observed from ${\mathrm{Eu}}^{3+}$ to ${\mathrm{Eu}}^{2+}$. Because the lifetime of a photoinduced state can be up to 3 ns, a metastable state is considered to be realized. By comparing the experimental results with the theoretical calculations, the photoinduced valence transition between Eu $4f$ and conduction electrons was quantitatively evaluated.
Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation

Knowledge of vehicle dynamic parameters is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. Unfortunately, some fundamental parameters like tire-road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. The ability to accurately estimate lateral tire forces and sideslip angle is a critical determinant in the performances of many vehicle control system and making vehicle's danger indices. To address system nonlinearities and unmodeled dynamics, an observer derived from unscented Kalman filtering technique is proposed. The estimation process method is based on the dynamic response of a vehicle instrumented with easily-available standard sensors. Performances are tested using an experimental car in real driving situations. Experimental results show the potentiel of the estimation method.
A New Method for Locating Feature Points in Pulse Wave Using Wavelet Transform

This paper aims to propose a new method for positioning pulse signal characteristic point automatically by using embedded software with wavelet transform. Wavelet transform that can provide multi-resolution analysis is a powerful method in pulse signal analysis. By calculating the relative displacement between local maximum which is in high-level detail coefficients of wavelet transform, point e is located more effectively than traditional method. New analysis method for biomedical signal analysis such as pulse signal is put forward.
Filtered instanton Floer homology and the homology cobordism group

For any $s \in [-\infty,0]$ and oriented homology $3$-sphere $Y$, we introduce a homology cobordism invariant $r_s(Y )$ whose value is in $(0,\infty]$. The values $\{r_s (Y )\}$ are contained in the critical values of the $SU(2)$-Chern-Simons functional of $Y$, and we have a negative definite cobordism inequality and a connected sum formula. As applications, we have several new results on the homology cobordism group. As one of such results, we give infinitely many homology $3$-spheres which cannot bound any definite $4$-manifold. As another result, we show that if the $1$-surgery of a knot has the Froyshov invariant negative, then all positive $1/n$-surgeries of the knot are linearly independent in the homology cobordism group. In another direction, we use $\{r_s\}$ to define a filtration on the homology cobordism group which is parametrized by $[0,\infty]$. Moreover, as a hyperbolic example, we compute an approximate value of $r_s$ for the $1/2$-surgery along the mirror of the knot $5_2$ in Rolfsen's knot table.
A simple and quantum-mechanically motivated characterization of the formally real Jordan algebras

Quantum theory’s Hilbert space apparatus in its finite-dimensional version is nearly reconstructed from four simple and quantum-mechanically motivated postulates for a quantum logic. The recon- struction process is not complete, since it excludes the two-dimensional Hilbert space and still includes the exceptional Jordan algebras, which are not part of the Hilbert space apparatus. Options for physically meaningful potential generalizations of the apparatus are discussed.
Unified explanation of 
b→sμ+μ−
 anomalies, neutrino masses, and 
B→πK
 puzzle

Anomalies in semi-leptonic $B$ decays could indicate new physics beyond the standard model(SM). There is an older puzzle in non-leptonic $B \to \pi K$ decays. The new particles, leptoquarks and diquarks, required to solve the semi-leptonic and the non-leptonic puzzles can also generate neutrino masses and mixing at loop level. We show that a consistent framework to explain the $B$ anomalies and the neutrino masses is possible and we make predictions for certain rare nonleptonic $B$ decays.
Motion-Based Feature Selection and Adaptive Template Update Strategy for Robust Visual Tracking

The Kanade-Lucas-Tomasi(KLT) method is a classictracking algorithm, which however suffers from a longexisting gradual template drift problem. In this paper, wepresent an improved tracking algorithm which can sustain thetracking performance for a long term against drift. In thisapproach, we formulate the tracking over a state comprising of a template with kinematic motion. Based on the sparsemotion field generated by KLT, a motion consistency basedmethod is applied to filer out the outliers which are the causeof cumulative drift errors. To sustain the tracking performance in a long term, an adaptive template update strategy monitoredby the appearance and motion continuities of the template isproposed. Finally, quantitative testing on several benchmarksequences demonstrate the advances of the proposed methodin long term tracking.
Towards Unsupervised Familiar Scene Recognition in Egocentric Videos

Nowadays, there is an upsurge of interest in using lifelogging devices. Such devices generate huge amounts of image data; consequently, the need for automatic methods for analyzing and summarizing these data is drastically increasing. We present a new method for familiar scene recognition in egocentric videos, based on background pattern detection through automatically configurable COSFIRE filters. We present some experiments over egocentric data acquired with the Narrative Clip.
Hardware/Software Co-monitoring

Hardware/Software (HW/SW) interfaces, mostly implemented as devices and device drivers, are pervasive in various computer systems. Nowadays HW/SW interfaces typically undergo intensive testing and validation before release, but they are still unreliable and insecure when deployed together with computer systems to end users. Escaped logic bugs, hardware transient failures, and malicious exploits are prevalent in HW/SW interactions, making the entire system vulnerable and unstable. We present HW/SW co-monitoring, a runtime co-verification approach to detecting failures and malicious exploits in device/driver interactions. Our approach utilizes a formal device model (FDM), a transaction-level model derived from the device specification, to shadow the real device execution. Based on the co-execution of the device and FDM, HW/SW co-monitoring carries out two-tier runtime checking: (1) device checking checks if the device behaviors conform to the FDM behaviors; (2) property checking detects invalid driver commands issued to the device by verifying system properties against driver/device interactions. We have applied HW/SW co-monitoring to five widely-used devices and their Linux drivers, discovering 9 real bugs and vulnerabilities while introducing modest runtime overhead. The results demonstrate the major potential of HW/SW co-monitoring in improving system reliability and security.
An Analysis of Lemmatization on Topic Models of Morphologically Rich Language

Topic models are typically represented by top-$m$ word lists for human interpretation. The corpus is often pre-processed with lemmatization (or stemming) so that those representations are not undermined by a proliferation of words with similar meanings, but there is little public work on the effects of that pre-processing. Recent work studied the effect of stemming on topic models of English texts and found no supporting evidence for the practice. We study the effect of lemmatization on topic models of Russian Wikipedia articles, finding in one configuration that it significantly improves interpretability according to a word intrusion metric. We conclude that lemmatization may benefit topic models on morphologically rich languages, but that further investigation is needed.
Fast Inhibition Underlies the Transmission of Auditory Information between Cochlear Nuclei

A direct commissural connection between cochlear nuclei provides a pathway by which binaural input can influence the processing of acoustic information through the ventral cochlear nucleus. Despite anatomical evidence to suggest the existence of such a pathway, its nature and behavior have not been investigated previously. This in vivo intracellular electrophysiological study provides direct evidence of monosynaptic (mean latency, 1.43 msec), inhibitory commissural input to T stellate cells. This inhibition is fast acting (duration, <10 msec), occurring with little synaptic delay (∼0.3 msec). Electrical stimulation also revealed the initiation of antidromic responses in the onset chopper population, signifying D stellate neurons as a source of commissural inputs. Activation of the commissural connection was most evident in response to broadband stimuli. These results provide the first compelling evidence of a fast, monosynaptic commissural pathway arising from contralateral D stellate neurons providing broadband inhibitory input to T stellate cells.
Compositionality in Verb-Particle Constructions

We are developing a broad-coverage deep semantic lexicon for a system that parses sentences into a logical form expressed in a rich ontology that supports reasoning. In this paper we look at verb-particle constructions (VPCs), and the extent to which they can be treated compositionally vs idiomatically. First we distinguish between the different types of VPCs based on their compositionality and then present a set of heuristics for classifying specific instances as compositional or not. We then identify a small set of general sense classes for particles when used compositionally and discuss the resulting lexical representations that are being added to the lexicon. By treating VPCs as compositional whenever possible, we attain broad coverage in a compact way, and also enable interpretations of novel VPC usages not explicitly present in the lexicon.
Biodiversity, population regulation, and the stability of coral-reef fish communities

Unprecedented population declines and extinctions because of human activities, combined with a growing recognition that such losses affect the stability of ecosystems, underscore the need to better understand how populations persist naturally. We provide field experimental evidence that high biodiversity—in particular, the combined effects of predators and competitors—acts in a way that regulates the size of local fish populations within their coral-reef community. These results indicate that complex interactions among multiple species are necessary for the stability of a highly diverse community, and so forewarn that overexploiting such species may have cascading negative consequences for the entire system.
Localization of the DMDL gene-encoded dystrophin-related protein using a panel of nineteen monoclonal antibodies: presence at neuromuscular junctions, in the sarcolemma of dystrophic skeletal muscle, in vascular and other smooth muscles, and in proliferating brain cell lines

mAbs have been raised against different epitopes on the protein product of the DMDL gene, which is an autosomal homologue of the X-linked DMD gene for dystrophin. These antibodies provide direct evidence that DMDL protein is localized near acetylcholine receptors at neuromuscular junctions in normal and mdx mouse intercostal muscle. The primary location in tissues other than skeletal muscle is smooth muscle, especially in the vascular system, which may account for the wide tissue distribution previously demonstrated by Western blotting. The DMDL protein was undetectable in the nonjunctional sarcolemma of normal human muscle, but was observed in nonjunctional sarcolemma of Duchenne muscular dystrophy patients, where dystrophin itself is absent or greatly reduced. The expression of DMDL protein is not restricted to smooth and skeletal muscle, however, since relatively large amounts are present in transformed brain cell lines of both glial and Schwann cell origin. This contrasts with the low levels of DMDL protein in adult brain tissue.
A Quick Multi-Objective Evolutionary Algorithm Based on Dominating Tree

To solve the time-consuming problem of the fitness assignment in the multi-objective evolutionary algorithm, this paper proposes a novel fitness assignment—dominating tree. The dominating tree preserves the necessary relationships among individuals, contains the density information implicitly, and reduces the comparisons among individuals distinctly. In addition, a smart eliminating strategy based on the dominating tree maintains the diversity of the population without extra expenses. A new multi-objective evolutionary algorithm based on dominating tree is proposed on these innovations. By examining three performance metrics on six test problems, the new algorithm is found to be competitive with SPEA2 and NSGA-II in terms of converging to the true Pareto front and maintaining the diversity of the population, moreover, it is much faster than other two algorithms.
Disk failures in the real world : What does an MTTF of 1 , 000 , 000 hours mean to you ?

In large-scale systems where number of components c an approach a million a failure is a significant problem. In this paper aut hors have presented and analyzed failure data from different large systems. More than 100,000 disks with different interfaces which come from at least four different vendors have been investigated. According to information which o ne can find in datasheets the annual failure rate should be at most 0.88%, bu t the results show that replacement rate is usually in the range of 2 to 4% and can be up to 13%. Another important outcome is that infant mortality is negligible, but wear-out starts earlier than expected and replacement rate i s constantly increasing over the time. For different disk interfaces replacement rate is at the same level. Exponential distribution does not provide good mode ling for time between
Biodegradability of Dissolved Organic Matter in Forest Throughfall, Soil Solution, and Stream Water

High concentrations of dissolved organic matter (DOM) were leached into rainwater passing through the canopy and forest floor of an oak (Quercus spp.)-hickory (Carya spp.) forest in the southern Appala-chian Mountains. More than 95% of this dissolved organic C (DOC) and N (DON) was removed as water percolated through the soil profile and left the ecosystem in stream water. Our objective was to examine the importance of decomposition in the removal of DOC and DON. Samples of DOM front throughfall, forest floor water, soil water from A and B soil horizons, and stream water were all adjusted to a common initial DOC concentration, inoculated with soil and stream microbes , and incubated in solution for 134 d. In general, only 14 to 33% of the DOC in forest floor, soil solution, and stream samples decomposed during the incubation. The relative order of average decomposition of DOC from the various strata was, from fastest to slowest: throughfall, Oi horizon (forest floor), Oa horizon (forest floor), B horizon, stream, AB horizon, isolated fulvic acid, and upper A horizon. In short, biodegradability of DOM in the ecosystem profile declined vertically from throughfall to the A horizon and then increased with depth. The DON generally did not decay faster than the DOC — results consistent with the idea that hydrolysis of organic N is linked to mineralization of DOC rather than occurring selectively in response to the biochemical need for N. Throughfall DOM could be decomposed during its passage through the upper soil, but decomposition seems too slow to be responsible for the bulk of removal of DON and DOC that occurs in the mineral soil. Adsorption, rather than biodegradation, is more likely responsible for maintaining low DOC substrate concentrations in the mineral soil and preventing its loss into stream water. T HE BIODEGRADABILITY of xenobiotic compounds in soil and water has been the focus of much research. The biodegradability of naturally occurring DOM has received less attention, though there has been considerable research on the fate of fresh autumn leaf litter leachate and on the ability of stream and lake DOM to support the growth of bacteria in those At the same time, there is a widespread belief that dissolved humic substances , which make up much of natural DOM, are quite resistant to decomposition. For obvious reasons, DOM in soil has received much less attention than that in lakes and streams. The …
A Mixed Key Assignment Algorithm for Secure Multicast

Key assignment algorithm plays a very important role in the large and dynamic groups for group key management. This paper investigates detailed and deeply key assignment algorithms and analyses Breadth First Assignment (BFA) and Depth First Assignment (DFA). Combining the advantage and disadvantage of these two kinds of algorithms, we present a new mixed key assignment algorithm (MKA). Our results show that this approach is much better than previous key assignment algorithms for reducing the average number of packets that a user needs to receive and the variance of it.
Power-Delivery Networks Optimization with Thermal Reliability Integrity

With the growing power consumption in modern high performance VLSI designs, nonuniform temperature distribution and limited heat-conduction capability have caused thermal induced performance and reliability degradation. Electromigration is the main reliability concern and will become a more limiting factor of IC designs. It must be addressed together with a thermal reliability modeling. This issue also has been recognized in the International Technology Roadmap for Semiconductors (ITRS) 2002 update as one of the difficult challenges [1]. Although the impacts of thermal effects on transistor and interconnect performance are wellstudied, but still how thermal effects affect the reliability of power delivery is not very clear. As a result, traditional power-delivery designs without thermal consideration may cause soft-error, reliability degradation, and even premature chip failures. In this paper, we propose an algorithm for power-delivery networks optimization with thermal reliability integrity. By considering thermal and power integrity, we are able to achieve high power supply quality and thermal reliability. For a 56×72 mesh, our design shows that the lifetime of the optimized ground network is 9.8 years. Whereas the lifetime of the ground network designed by a traditional method without thermal integrity is only 4.1 years.