234 research outputs found
Selective AP-sequence Based Indoor Localization without Site Survey
In this paper, we propose an indoor localization system employing ordered
sequence of access points (APs) based on received signal strength (RSS). Unlike
existing indoor localization systems, our approach does not require any
time-consuming and laborious site survey phase to characterize the radio
signals in the environment. To be precise, we construct the fingerprint map by
cutting the layouts of the interested area into regions with only the knowledge
of positions of APs. This can be done offline within a second and has a
potential for practical use. The localization is then achieved by matching the
ordered AP-sequence to the ones in the fingerprint map. Different from
traditional fingerprinting that employing all APs information, we use only
selected APs to perform localization, due to the fact that, without site
survey, the possibility in obtaining the correct AP sequence is lower if it
involves more APs. Experimental results show that, the proposed system achieves
localization accuracy < 5m with an accumulative density function (CDF) of 50%
to 60% depending on the density of APs. Furthermore, we observe that, using all
APs for localization might not achieve the best localization accuracy, e.g. in
our case, 4 APs out of total 7 APs achieves the best performance. In practice,
the number of APs used to perform localization should be a design parameter
based on the placement of APs.Comment: VTC2016-Spring, 15-18 May 2016, Nanjing, Chin
Influencer Backdoor Attack on Semantic Segmentation
When a small number of poisoned samples are injected into the training
dataset of a deep neural network, the network can be induced to exhibit
malicious behavior during inferences, which poses potential threats to
real-world applications. While they have been intensively studied in
classification, backdoor attacks on semantic segmentation have been largely
overlooked. Unlike classification, semantic segmentation aims to classify every
pixel within a given image. In this work, we explore backdoor attacks on
segmentation models to misclassify all pixels of a victim class by injecting a
specific trigger on non-victim pixels during inferences, which is dubbed
Influencer Backdoor Attack (IBA). IBA is expected to maintain the
classification accuracy of non-victim pixels and misleads classifications of
all victim pixels in every single inference. Specifically, we consider two
types of IBA scenarios, i.e., 1) Free-position IBA: the trigger can be
positioned freely except for pixels of the victim class, and 2) Long-distance
IBA: the trigger can only be positioned somewhere far from victim pixels, given
the possible practical constraint. Based on the context aggregation ability of
segmentation models, we propose techniques to improve IBA for the scenarios.
Concretely, for free-position IBA, we propose a simple, yet effective Nearest
Neighbor trigger injection strategy for poisoned sample creation. For
long-distance IBA, we propose a novel Pixel Random Labeling strategy. Our
extensive experiments reveal that current segmentation models do suffer from
backdoor attacks, and verify that our proposed techniques can further increase
attack performance
Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor
According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve
FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator
Spiking Neural Networks (SNNs) are expected to be a promising alternative to
Artificial Neural Networks (ANNs) due to their strong biological
interpretability and high energy efficiency. Specialized SNN hardware offers
clear advantages over general-purpose devices in terms of power and
performance. However, there's still room to advance hardware support for
state-of-the-art (SOTA) SNN algorithms and improve computation and memory
efficiency. As a further step in supporting high-performance SNNs on
specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can
address the issue of non-spike operation in current SOTA SNN algorithms, which
presents an obstacle in the end-to-end deployment onto existing SNN hardware.
To more effectively align with the SNN characteristics, we design a
spatiotemporal dataflow that allows four dimensions of parallelism and
eliminates the need for membrane potential storage, enabling on-the-fly spike
processing and spike generation. To further improve hardware acceleration
performance, we develop a high-performance spike computing engine as a backend
based on a systolic array operating at 500-600MHz. To the best of our
knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based
implementations. Furthermore, it stands as the first SNN accelerator capable of
supporting non-spike operations, which are commonly used in advanced SNN
algorithms. FireFly v2 has doubled the throughput and DSP efficiency when
compared to our previous version of FireFly and it exhibits 1.33 times the DSP
efficiency and 1.42 times the power efficiency compared to the current most
advanced FPGA accelerators
Is Conventional SNN Really Efficient? A Perspective from Network Quantization
Spiking Neural Networks (SNNs) have been widely praised for their high energy
efficiency and immense potential. However, comprehensive research that
critically contrasts and correlates SNNs with quantized Artificial Neural
Networks (ANNs) remains scant, often leading to skewed comparisons lacking
fairness towards ANNs. This paper introduces a unified perspective,
illustrating that the time steps in SNNs and quantized bit-widths of activation
values present analogous representations. Building on this, we present a more
pragmatic and rational approach to estimating the energy consumption of SNNs.
Diverging from the conventional Synaptic Operations (SynOps), we champion the
"Bit Budget" concept. This notion permits an intricate discourse on
strategically allocating computational and storage resources between weights,
activation values, and temporal steps under stringent hardware constraints.
Guided by the Bit Budget paradigm, we discern that pivoting efforts towards
spike patterns and weight quantization, rather than temporal attributes,
elicits profound implications for model performance. Utilizing the Bit Budget
for holistic design consideration of SNNs elevates model performance across
diverse data types, encompassing static imagery and neuromorphic datasets. Our
revelations bridge the theoretical chasm between SNNs and quantized ANNs and
illuminate a pragmatic trajectory for future endeavors in energy-efficient
neural computations
FireFly: A High-Throughput and Reconfigurable Hardware Accelerator for Spiking Neural Networks
Spiking neural networks (SNNs) have been widely used due to their strong
biological interpretability and high energy efficiency. With the introduction
of the backpropagation algorithm and surrogate gradient, the structure of
spiking neural networks has become more complex, and the performance gap with
artificial neural networks has gradually decreased. However, most SNN hardware
implementations for field-programmable gate arrays (FPGAs) cannot meet
arithmetic or memory efficiency requirements, which significantly restricts the
development of SNNs. They do not delve into the arithmetic operations between
the binary spikes and synaptic weights or assume unlimited on-chip RAM
resources by using overly expensive devices on small tasks. To improve
arithmetic efficiency, we analyze the neural dynamics of spiking neurons,
generalize the SNN arithmetic operation to the multiplex-accumulate operation,
and propose a high-performance implementation of such operation by utilizing
the DSP48E2 hard block in Xilinx Ultrascale FPGAs. To improve memory
efficiency, we design a memory system to enable efficient synaptic weights and
membrane voltage memory access with reasonable on-chip RAM consumption.
Combining the above two improvements, we propose an FPGA accelerator that can
process spikes generated by the firing neuron on-the-fly (FireFly). FireFly is
implemented on several FPGA edge devices with limited resources but still
guarantees a peak performance of 5.53TSOP/s at 300MHz. As a lightweight
accelerator, FireFly achieves the highest computational density efficiency
compared with existing research using large FPGA devices
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
Large language models (LLMs) have been widely used as agents to complete
different tasks, such as personal assistance or event planning. While most work
has focused on cooperation and collaboration between agents, little work
explores competition, another important mechanism that fosters the development
of society and economy. In this paper, we seek to examine the competition
behaviors in LLM-based agents. We first propose a general framework to study
the competition between agents. Then, we implement a practical competitive
environment using GPT-4 to simulate a virtual town with two types of agents,
including restaurant agents and customer agents. Specifically, restaurant
agents compete with each other to attract more customers, where the competition
fosters them to transform, such as cultivating new operating strategies. The
results of our experiments reveal several interesting findings ranging from
social learning to Matthew Effect, which aligns well with existing sociological
and economic theories. We believe that competition between agents deserves
further investigation to help us understand society better. The code will be
released soon.Comment: Technical report; 21 page
Enhanced baseline activity in the left ventromedial putamen predicts individual treatment response in drug-naive, first-episode schizophrenia: Results from two independent study samples
BACKGROUND: Antipsychotic medications are the common treatment for schizophrenia. However, reliable biomarkers that can predict individual treatment response are still lacking. The present study aimed to examine whether baseline putamen activity can predict individual treatment response in schizophrenia.
METHODS: Two independent samples of patients with drug-naive, first-episode schizophrenia (32 patients in sample 1 and 44 in sample 2) and matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) at baseline. Patients were treated with olanzapine for 8 weeks; symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and week 8. Fractional amplitude of low frequency fluctuation (fALFF) and pattern classification techniques were used to analyze the data.
FINDINGS: Univariate analysis shows an elevated pre-treatment fALFF in the left ventromedial putamen in both patient samples compared to healthy controls (p\u27s \u3c 0.001). The support vector regression (SVR) analysis suggests a positive relationship between baseline pre-treatment fALFF in the left ventromedial putamen and improvement in positive symptom at week 8 in each patient group using a cross-validated method (r=0.452, p=.002; r=0.511, p=.003, respectively).
INTERPRETATION: Our study suggests that elevated pre-treatment mean fALFF in the left ventromedial putamen may predict individual therapeutic response to olanzapine treatment in drug-naive, first-episode patients with schizophrenia. Future studies are needed to confirm whether this finding is generalizable to patients with schizophrenia treated with other antipsychotic medications.
FUND: The National Key RandD Program of China and the National Natural Science Foundation of China
Reduced Brain Activity in the Right Putamen as an Early Predictor for Treatment Response in Drug-Naive, First-Episode Schizophrenia
Antipsychotic medications can have a significant effect on brain function after only several days of treatment. It is unclear whether such an acute effect can serve as an early predictor for treatment response in schizophrenia. Thirty-two patients with drug-naive, first-episode schizophrenia and 32 healthy controls underwent resting-state functional magnetic resonance imaging. Patients were treated with olanzapine and were scanned at baseline and 1 week of treatment. Healthy controls were scanned once at baseline. Symptom severity was assessed within the patient group using the Positive and Negative Syndrome Scale (PANSS) at three time points (baseline, 1 week of treatment, and 8 weeks of treatment). The fractional amplitude of low frequency fluctuation (fALFF) and support vector regression (SVR) methods were used to analyze the data. Compared with the control group, the patient group showed increased levels of fALFF in the bilateral putamen at baseline. After 1 week of olanzapine treatment, the patient group showed decreased levels of fALFF in the right putamen relative to those at baseline. The SVR analysis found a significantly positive relationship between the reduction in fALFF after 1 week of treatment and the improvement in positive symptoms after 8 weeks of treatment (r = 0.431, p = 0.014). The present study provides evidence that early reduction and normalization of fALFF in the right putamen may serve as a predictor for treatment response in patients with schizophrenia
Osteoporosis Associated with Antipsychotic Treatment in Schizophrenia
Schizophrenia is one of the most common global mental diseases, with prevalence of 1%. Patients with schizophrenia are predisposed to diabetes, coronary heart disease, hypertension, and osteoporosis, than the normal. In comparison with the metabolic syndrome, for instance, there are little reports about osteoporosis which occurs secondary to antipsychoticinduced hyperprolactinaemia. There are extensive recent works of literature indicating that osteoporosis is associated with schizophrenia particularly in patients under psychotropic medication therapy. As osteoporotic fractures cause significantly increased morbidity and mortality, it is quite necessary to raise the awareness and understanding of the impact of antipsychoticinduced hyperprolactinaemia on physical health in schizophrenia. In this paper, we will review the relationship between schizophrenia, antipsychotic medication, hyperprolactinaemia, and osteoporosis
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