Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigorated by the progress in large language models (LLMs) and vision-language models (VLMs), offering the potential for a more nuanced understanding of anomalies. However, online VAD has seldom received attention due to real-time constraints and computational intensity. In this paper, we introduce a novel **M**emory-based online scoring queue scheme for **T**raining-free VAD (MoniTor), to address the inherent complexities in online VAD. Specifically, MoniTor applies a streaming input to VLMs, leveraging the capabilities of pre-trained large-scale models. To capture temporal dependencies more effectively, we incorporate a novel prediction mechanism inspired by Long Short-Term Memory (LSTM) networks. This ensures the model can effectively model past states and leverage previous predictions to identify anomalous behaviors. Thereby, it better understands the current frame. Moreover, we design a scoring queue and an anomaly prior to dynamically store recent scores and cover all anomalies in the monitoring scenario, providing guidance for LLMs to distinguish between normal and abnormal behaviors over time. We evaluate MoniTor on two large datasets (i.e., UCF-Crime and XD-Violence) containing various surveillance and real-world scenarios. The results demonstrate that MoniTor outperforms state-of-the-art methods and is competitive with weakly supervised methods without training. Code is available at https://github.com/YsTvT/MoniTor.

We jointly consider full-duplex operation and network coding in two-hop relay networks to enhance the throughput of the block transmission of packets over erasure channels. Two coded transmission schemes, termed Fewest Broadcast Packet First (FBPF) and Buffer Contents-based Coded Transmission (BCCT), are proposed, where random linear network coding is employed at the Base Station (BS) and the Relay Station (RS), respectively. Both schemes do not rely on users’ Channel State Information (CSI), buffer status, channel parameters, etc., and hence are practically viable. We derive closed-form upper bounds on the throughput of both schemes. We prove that both schemes achieve the optimal throughput when the BS-to-RS channel is perfect. Through extensive simulations, we demonstrate that both schemes incur substantially higher throughput than the traditional uncoded Automatic Repeat-reQuest (ARQ) scheme and perform close to a general upper bound on the system throughput. Furthermore, even with imperfect Self-Interference Cancellation (SIC) at the full-duplex RS, our schemes are shown to be superior to state-of-the-art coded transmission schemes designed for half-duplex relay networks, given that the impact of imperfect SIC on the BS-to-RS channel quality is not high.

Consider the problem of power control for an energy harvesting communication system, where the transmitter is equipped with a finite-sized rechargeable battery and is able to look ahead to observe a fixed number of future energy arrivals. An implicit characterization of the maximum average throughput over an additive white Gaussian noise channel and the associated optimal power control policy is provided via the Bellman equation under the assumption that the energy arrival process is stationary and memoryless. A more explicit characterization is obtained for the case of Bernoulli energy arrivals by means of asymptotically tight upper and lower bounds on both the maximum average throughput and the optimal power control policy. Apart from their pivotal role in deriving the desired analytical results, such bounds are highly valuable from a numerical perspective as they can be efficiently computed using convex optimization solvers.

A new online power control policy is constructed for a discrete-time energy harvesting communication system equipped with a battery of finite capacity. This policy only requires the knowledge of the (effective) mean of the energy arrival process and is optimal in the sense that it maximizes the minimum asymptotic expected average reward for i.i.d. energy arrival processes with the same (effective) mean. We show that the fixed fraction policy by Shaviv and Özgür can be interpreted as a linear approximation of this new policy in the large battery-capacity limit and propose a locally fixed fraction policy as its more accurate approximation. A systematic comparison of these policies is conducted via analytical and numerical performance analysis.

A general theory of online power control for discrete-time battery limited energy harvesting communications is developed, which leads to, among other things, an explicit characterization of a maximin optimal policy. This policy only requires the knowledge of the (effective) mean of the energy arrival process and maximizes the minimum asymptotic expected average reward (with the minimization taken over all energy arrival distributions of a given (effective) mean). Moreover, it is universally near optimal and has a strictly better worst-case performance as well as a strictly improved lower multiplicative factor in comparison with the fixed fraction policy proposed by Shaviv and Özgür when the objective is to maximize the throughput over an additive white Gaussian noise channel. The competitiveness of this maximin optimal policy is also demonstrated via numerical examples.

Every channel can be expressed as a convex combination of deterministic channels with each deterministic channel corresponding to one particular intrinsic state. Such convex combinations are, in general, not unique, each giving rise to a specific intrinsic-state distribution. In this paper, we study the maximum and minimum capacities of a channel when the realization of its intrinsic state is causally available at the encoder and/or the decoder. Several conclusive results are obtained for binary-input channels and binary-output channels. By-products of our investigation include a generalization of the Birkhoff-von Neumann theorem and a condition on the uselessness of causal state information at the encoder.

Tight lower and upper bounds on the ratio of relative entropies of two probability distributions with respect to a common third one are established, where the three distributions are collinear in the standard (n - 1)-simplex. These bounds are leveraged to analyze the capacity of an arbitrary binary-input channel with noisy causal state information (provided by a side channel) at the encoder and perfect state information at the decoder, and in particular to determine the exact universal threshold on the noise measure of the side channel, above which the capacity is the same as that with no encoder side information.

Every channel can be expressed as a convex combination of deterministic channels with each deterministic channel corresponding to one particular intrinsic state. Such convex combinations are in general not unique, each giving rise to a specific intrinsic-state distribution. In this paper we study the maximum and the minimum capacities of a channel when the realization of its intrinsic state is causally available at the encoder and/or the decoder. Several conclusive results are obtained for binary-input channels and binary-output channels. Byproducts of our investigation include a generalization of the Birkhoff-von Neumann theorem and a condition on the uselessness of causal state information at the encoder.

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (Bcont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. Bcont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.

Linear encoders with good joint spectra are suitable candidates for optimal lossless joint source-channel coding (JSCC), where the joint spectrum is a variant of the input-output complete weight distribution and is considered good if it is close to the average joint spectrum of all linear encoders (of the same coding rate). In spite of their existence, little is known on how to construct such encoders in practice. This paper is devoted to their construction. In particular, two families of linear encoders are presented and proved to have good joint spectra. The first family is derived from Gabidulin codes, a class of maximum-rank-distance codes. The second family is constructed using a serial concatenation of an encoder of a low-density parity-check code (as outer encoder) with a low-density generator matrix encoder (as inner encoder). In addition, criteria for good linear encoders are defined for three coding applications: 1) lossless source coding; 2) channel coding; and 3) lossless JSCC. In the framework of the code-spectrum approach, these three scenarios correspond to the problems of constructing linear encoders with good kernel spectra, good image spectra, and good joint spectra, respectively. Good joint spectra imply both good kernel spectra and good image spectra, and for every linear encoder having a good kernel (respectively, image) spectrum, it is proved that there exists a linear encoder not only with the same kernel (respectively, image) but also with a good joint spectrum. Thus, a good joint spectrum is the most important feature of a linear encoder.

Rateless code usually generates a potentially infinite number of coded packets at the encoder and collects enough packets at the decoder to ensure reliable recovery of multiple information packets. The conventional rateless decoder usually works in a parallel manner which needs to initiate a new belief propagation (BP) decoding procedure upon each newly received collection of coded packets, thereby resulting in prohibitive decoding complexity in practice. In this paper, we present a novel serial decoding algorithm, i.e., the serial storage belief propagation (SS BP) algorithm, for rateless codes over noisy channels. Specifically, upon receiving a new group of coded packets, the decoder initiates a new attempt to decode all the packets received so far, using the results of the previous attempt as initial input. Moreover, in each iteration of the new attempt, the decoder serially propagates the messages group by group from the most recent one to the earliest one. In this way, the newly updated messages can be propagated faster, expediting the recovery of information packets. In addition, the proposed serial decoding algorithm has significantly lower complexity than the existing parallel decoding algorithms. Simulation results validate its effectiveness in AWGN, Rayleigh, and Rician fading channels.

A fundamental property of codes, the second-order weight distribution, is proposed to solve the problems such as computing second moments of weight distributions of linear code ensembles. A series of results, parallel to those for weight distributions, is established for second-order weight distributions. In particular, an analogue of MacWilliams identities is proved. The second-order weight distributions of regular LDPC code ensembles are then computed. As easy consequences, the second moments of weight distributions of regular LDPC code ensembles are obtained. Furthermore, the application of second-order weight distributions in random coding approach is discussed. The second-order weight distributions of the ensembles generated by a so-called 2-good random generator or parity-check matrix are computed, where a 2-good random matrix is a kind of generalization of the uniformly distributed random matrix over a finite filed and is very useful for solving problems that involve pairwise or triple-wise properties of sequences. It is shown that the 2-good property is reflected in the second-order weight distribution, which thus plays a fundamental role in some well-known problems in coding theory and combinatorics. An example of linear intersecting codes is finally provided to illustrate this fact.

The average weight distribution of a regular low-density parity-check (LDPC) code ensemble over a finite field is thoroughly analyzed. In particular, a precise asymptotic approximation of the average weight distribution is derived for the small-weight case, and a series of fundamental qualitative properties of the asymptotic growth rate of the average weight distribution are proved. Based on this analysis, a general result, including all previous results as special cases, is established for the minimum distance of individual codes in a regular LDPC code ensemble.

Adsorptive non-point source pollution associated with soil erosion has brought big problems to water environment and land productivity. As the most important water source area of Hainan, the Songtao Reservoir Basin (SRB) has faced with a crisis of water contamination caused by land exploitation. The objectives of this study are: (1) to assess the soil erosion risk and adsorptive non-point source pollution load; (2) to put forward a scheme for land management and planning to control soil erosion and nonpoint source pollution. Taking GIS and remote sensing as main techniques, an analysis was conducted to assess the non-point source pollution load caused by eucalyptus and rubber cultivation. Research findings indicate that soil erosion in the eucalyptus and rubber plantations is much more serious than the natural forest, and causes heavier adsorptive non-point source pollution. Adopting soil and water conservation facilities can effectively control the soil erosion and non-point source pollution which can bring a lasting ecological and economic benefit.

A general lossless joint source-channel coding (JSCC) scheme based on linear codes and random interleavers for multiple-access channels (MACs) is presented and then analyzed in this paper. By the information-spectrum approach and the code-spectrum approach, it is shown that a linear code with a good joint spectrum can be used to establish limit-approaching lossless JSCC schemes for correlated general sources and general MACs, where the joint spectrum is a generalization of the input-output weight distribution. Some properties of linear codes with good joint spectra are investigated. A formula on the ldquodistancerdquo property of linear codes with good joint spectra is derived, based on which, it is further proved that, the rate of any systematic codes with good joint spectra cannot be larger than the reciprocal of the corresponding alphabet cardinality, and any sparse generator matrices cannot yield linear codes with good joint spectra. The problem of designing arbitrary rate coding schemes is also discussed. A novel idea called ldquogeneralized puncturingrdquo is proposed, which makes it possible that one good low-rate linear code is enough for the design of coding schemes with multiple rates. Finally, various coding problems of MACs are reviewed in a unified framework established by the code-spectrum approach, under which, criteria and candidates of good linear codes in terms of spectrum requirements for such problems are clearly presented.

Performance bounds on the average codeword length of Golomb codes for arbitrary probability distributions are derived in terms of the mean. Then based on Golomb codes, a class of extended gamma codes which are the generalizations of Elias gamma code is constructed and proved to be universal. Optimal decision rules for choosing parameters of Golomb codes and extended gamma codes for arbitrary probability distributions are also derived. Finally, the concept of maximum entropy codes is introduced and the existence of such codes is investigated for source classes with linear constraints. Golomb codes with optimal parameters turn out to be maximum entropy codes for sources with a fixed mean.

We derive an upper bound on the average MAP decoding error probability of random linear SW codes for arbitrary correlated stationary memoryless sources defined on Galois fields. By using this tool, we analyze the performance of SW codes based on LDPC codes and random permutations, and show that under some conditions, all but a diminishingly small proportion of LDPC encoders and permutations are good enough for the design of practical SW systems when the coding length is very large.

The Gallager bound is well known in the area of channel coding. However, most discussions about it mainly focus on its applications to memoryless channels. We show in this paper that the bounds obtained by Gallager's method are sufficiently tight even for general sources and channels that are defined in the information-spectrum theory. Our method is mainly based on the estimations of error exponents in those bounds, and by these estimations we proved the direct part of the Slepian-Wolf theorem and channel coding theorem for general sources and channels

A new LAI retrieval method is developed. The algorithm borrows ideas from the principles and methods of ground LAI measurements, and adopts a new frame which differs from traditional remote sensing LAI inversion methods. The ground data acquired from two field experiments are used to validate the algorithm. In order to resolve the scale exchange problem between high resolution ground observation and low resolution remote sensing data, two high resolution remote sensing images almost having the same resolutions with ground measurements are used as transitions.

GuiYang city is located in GuiZhou Province, China. As well as frequent dry and flood, Karst widely spreads in GuiYang, so serious soil erosion is an obvious character of ecosystem there. In the paper, we adopted Remote Sensing Interpretation based on the experts system to monitor and evaluate the soil erosion in Guiyang. The study includes (1) digitizing the influent factors of soil erosion. The influent factors of soil erosion are divided into the unchanged data and the changed data. The unchanged data are Lithological class, DEM and soil class, which are digitized by GIS. The changed data are land cover and vegetation cover, which are interpreted by remote sensing. (2) Making up the knowledge database of soil erosion. We analyzed the interested soil region and recorded the condition of influent factors of soil erosion. After optimizing the records, we made up the knowledge database for soil erosion in GuiYang. (3) Interpreting the soil erosion in GuiYang. After making up the knowledge database, we employed expert system to interpret the soil erosion there in 1994 and in 1998. (4) Getting the deteriorated region of soil erosion by comparing the soil erosion both in 1994 and in 1998 (5) Evaluating the hazard of soil erosion. According to the deteriorated region of soil erosion in GuiYang, we analyzed the condition of soil erosion and evaluate which region is easy for soil erosion. Finally, We concluded the rate of soil erosion changing in GuiYang and the spatial hazard classification of soil erosion.

Soil erosion is a key issue of land degradation and a main phenomenon of environmental deterioration. RS (Remote Sensing) and GIS (Geography Information System) have made great progress in the research on soil erosion and soil and water conservation since the end of 1980s'. All methods applying RS and GIS to study soil erosion are usually divided into Visual interpretation, Spectral Analysis, Parameter Determination and Man-computer Interactive Interpretation. But the model of intelligent interpreting soil erosion based on the geographical knowledge (MIISE) is different from four groups of the methods above. MIISE is developed from Visual Interpretation and Man-computer Interactive Interpretation to survey soil erosion for large scale, by use of a new expert system of spatial feature. In the article, the first part introduces the principle MIISE based on. The second part shows how to employ computer to realize MIISE, and how to make up knowledge database and the structure of the experts system. The third part shows MIISE evaluation. MIISE was applied in the study area to interpret the soil erosion, and the result was compared with original soil erosion map surveyed by field method. Finally, it is concluded that the geographical knowledge is an effectual way to discover the relationship between the soil erosion and its influent factors, and MIISE is more accurate and efficient than usual method such as Visual interpretation, Spectral Analysis and Man-computer Interactive Interpretation.