Aggregation Scheduling Algorithms in Wireless Sensor Networks

In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure

A Simplified Query-Only Attention for Encoder-Based Transformer Models

Transformer models have revolutionized fields like Natural Language Processing (NLP) by enabling machines to accurately understand and generate human language. However, these models’ inherent complexity and limited interpretability pose barriers to their broader adoption. To address these challenges, we propose a simplified query-only attention mechanism specifically for encoder-based transformer models to reduce complexity and improve interpretability. Unlike conventional attention mechanisms, which rely on query (Q), key (K), and value (V) vectors, our method uses only the Q vector for attention calculation. This approach reduces computational complexity while maintaining the model’s ability to capture essential relationships, enhancing interpretability. We evaluated the proposed query-only attention on an EEG conformer model, a state-of-the-art architecture for EEG signal classification. We demonstrated that it performs comparably to the original QKV attention mechanism, while simplifying the model’s architecture. Our findings suggest that query-only attention offers a promising direction for the development of more efficient and interpretable transformer-based models, with potential applications across various domains beyond NLP

Smartphone Forensic Challenges

Article originally published in Internation Journal of Computer Science and SecurityGlobally, the extensive use of smartphone devices has led to an increase in storage and transmission of enormous volumes of data that could be potentially be used as digital evidence in a forensic investigation. Digital evidence can sometimes be difficult to extract from these devices given the various versions and models of smartphone devices in the market. Forensic analysis of smartphones to extract digital evidence can be carried out in many ways, however, prior knowledge of smartphone forensic tools is paramount to a successful forensic investigation. In this paper, the authors outline challenges, limitations and reliability issues faced when using smartphone device forensic tools and accompanied forensic techniques. The main objective of this paper is intended to be consciousness-raising than suggesting best practices to these forensic work challenges

Direct Preference-based Policy Optimization without Reward Modeling

Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is challenging. Existing PbRL methods generally involve a two-step procedure: they first learn a reward model based on given preference data and then employ off-the-shelf reinforcement learning algorithms using the learned reward model. However, obtaining an accurate reward model solely from preference information, especially when the preference is from human teachers, can be difficult. Instead, we propose a PbRL algorithm that directly learns from preference without requiring any reward modeling. To achieve this, we adopt a contrastive learning framework to design a novel policy scoring metric that assigns a high score to policies that align with the given preferences. We apply our algorithm to offline RL tasks with actual human preference labels and show that our algorithm outperforms or is on par with the existing PbRL methods. Notably, on high-dimensional control tasks, our algorithm surpasses offline RL methods that learn with ground-truth reward information. Finally, we show that our algorithm can be successfully applied to fine-tune large language models.Comment: NeurIPS 202

Antiobesity and lipid-lowering effects of Bifidobacterium spp. in high fat diet-induced obese rats

<p>Abstract</p> <p>Background</p> <p>Recent studies have reported the preventive effects of probiotics on obesity. Among commensal bacteria, bifidobacteria is one of the most numerous probiotics in the mammalian gut and are a type of lactic acid bacteria. The aim of this study was to assess the antiobesity and lipid-lowering effects of <it>Bifidobacterium </it>spp. isolated from healthy Korean on high fat diet-induced obese rats.</p> <p>Methods</p> <p>Thirty-six male Sprague-Dawley rats were divided into three groups as follows: (1) SD group, fed standard diet; (2) HFD group, fed high fat diet; and (3) HFD-LAB group, fed high fat diet supplemented with LAB supplement (<it>B. pseudocatenulatum </it>SPM 1204, <it>B. longum </it>SPM 1205, and <it>B. longum </it>SPM 1207; 10⁸~ 10⁹CFU). After 7 weeks, the body, organ, and fat weights, food intake, blood serum levels, fecal LAB counts, and harmful enzyme activities were measured.</p> <p>Results</p> <p>Administration of LAB reduced body and fat weights, blood serum levels (TC, HDL-C, LDL-C, triglyceride, glucose, leptin, AST, ALT, and lipase levels), and harmful enzyme activities (β-glucosidase, β-glucuronidase, and tryptophanase), and significantly increased fecal LAB counts.</p> <p>Conclusion</p> <p>These data suggest that <it>Bifidobacterium </it>spp. used in this study may have beneficial antiobesity effects.</p

Neural responses to syllable-induced P1m and social impairment in children with autism spectrum disorder and typically developing Peers

In previous magnetoencephalography (MEG) studies, children with autism spectrum disorder (ASD) have been shown to respond differently to speech stimuli than typically developing (TD) children. Quantitative evaluation of this difference in responsiveness may support early diagnosis and intervention for ASD. The objective of this research is to investigate the relationship between syllable-induced P1m and social impairment in children with ASD and TD children. We analyzed 49 children with ASD aged 40–92 months and age-matched 26 TD children. We evaluated their social impairment by means of the Social Responsiveness Scale (SRS) and their intelligence ability using the Kaufman Assessment Battery for Children (K-ABC). Multiple regression analysis with SRS score as the dependent variable and syllable-induced P1m latency or intensity and intelligence ability as explanatory variables revealed that SRS score was associated with syllable-induced P1m latency in the left hemisphere only in the TD group and not in the ASD group. A second finding was that increased leftward-lateralization of intensity was correlated with higher SRS scores only in the ASD group. These results provide valuable insights but also highlight the intricate nature of neural mechanisms and their relationship with autistic traits

Relationships between peak alpha frequency, age, and autistic traits in young children with and without autism spectrum disorder

Background: Atypical peak alpha frequency (PAF) has been reported in children with autism spectrum disorder (ASD); however, the relationships between PAF, age, and autistic traits remain unclear. This study was conducted to investigate and compare the resting-state PAF of young children with ASD and their typically developing (TD) peers using magnetoencephalography (MEG). Methods: Nineteen children with ASD and 24 TD children, aged 5-7 years, underwent MEG under resting-state conditions. The PAFs in ten brain regions were calculated, and the associations between these findings, age, and autistic traits, measured using the Social Responsiveness Scale (SRS), were examined. Results: There were no significant differences in PAF between the children with ASD and the TD children. However, a unique positive association between age and PAF in the cingulate region was observed in the ASD group, suggesting the potential importance of the cingulate regions as a neurophysiological mechanism underlying distinct developmental trajectory of ASD. Furthermore, a higher PAF in the right temporal region was associated with higher SRS scores in TD children, highlighting the potential role of alpha oscillations in social information processing. Conclusions: This study emphasizes the importance of regional specificity and developmental factors when investigating neurophysiological markers of ASD. The distinct age-related PAF patterns in the cingulate regions of children with ASD and the association between right temporal PAF and autistic traits in TD children provide novel insights into the neurobiological underpinnings of ASD. These findings pave the way for future research on the functional implications of these neurophysiological patterns and their potential as biomarkers of ASD across the lifespan

Molecular mechanisms of heptaplatin effective against cisplatin-resistant cancer cell lines: less involvement of metallothionein

BACKGROUND: Heptaplatin is a new platinum derivative with anticancer activity against various cancer cell lines, including cisplatin-resistant cancer cell lines (Cancer Chemother Pharmacol 1995; 35: 441). METHODS: Molecular mechanisms of heptaplatin effective against cisplatin-resistant cancer cell lines has been investigated in connection with metallothionein (MT). Cytotoxicity was determined by an MTT assay. MT mRNA, was determined by RT-PCR assay. Transfection study was carried out to examine the function of MT. RESULTS: Of various gastric cancer cell lines, SNU-638 and SNU-601 showed the highest and lowest levels of MT mRNA, respectively, showing 80-fold difference. The IC(50 )values of SNU-638 to cisplatin, carboplatin and heptaplatin were 11.2-fold, 5.1-fold and 2.0-fold greater than those of SNU-601, respectively. Heptaplatin was more effective against cisplatin-resistant and MT-transfected gastric cancer sublines than cisplatin or carboplatin was. In addition, heptaplatin attenuated cadmium, but not zinc, induction of MT. CONCLUSION: These results indicate that molecular mechanisms of heptaplatin effective against cisplatin-resistant gastric cancer sublines is at least in part due to the less involvement of MT in heptaplatin resistance as well as its attenuation of MT induction

Effect of Combination Therapy with Sodium Ozagrel and Panax Ginseng on Transient Cerebral Ischemia Model in Rats

Sodium ozagrel (SO) prevents platelet aggregation and vasoconstriction in the cerebral ischemia. It plays an important role in the prevention of brain damage induced by cerebral ischemia/reperfusion. Recently, many animal studies have suggested that the Panax ginseng (PG) has neuroprotective effects in the ischemic brain. In this study, we assessed the neuroprotective effects that come from a combination therapy of SO and PG in rat models with middle cerebral artery occlusion (MCAO). Animals with MCAO were assigned randomly to one of the following four groups: (1) control (Con) group, (2) SO group (3 mg/kg, intravenously), (3) PG group (200 mg/kg, oral feeding), and (4) SO + PG group. The rats were subjected to a neurobehavior test including adhesive removal test and rotarod test at 1, 3, 7, 10, and 15 days after MCAO. The cerebral ischemic volume was quantified by Metamorph imaging software after 2-3-5-triphenyltetrazolium (TTC) staining. The neuronal cell survival and astrocytes expansion were assessed by immunohistofluorescence staining. In the adhesive removal test, the rats of PG or SO + PG group showed significantly better performance than those of the control group (Con: 88.1 ± 24.8, PG: 43.6 ± 11, SO + PG: 11.8 ± 7, P < .05). Notably, the combination therapy group (SO + PG) showed better performance than the SO group alone (SO: 56 ± 12, SO + PG: 11.8 ± 7, P < .05). In TTC staining for infarct volume, cerebral ischemic areas were also significantly reduced in the PG group and SO + PG group (Con: 219 ± 32, PG: 117 ± 8, SO + PG: 99 ± 11, P < .05). Immunohistofluorescence staining results showed that the group which received SO + PG group therapy had neuron cells in the normal range. They also had a low number of astrocytes and apoptotic cells compared with the control or SO group in the peri-infarction area. During astrocytes staining, compared to the SO + PG group, the PG group showed only minor differences in the number of NeuN-positive cells and quantitative analysis of infarct volume. In conclusion, these studies showed that in MCAO rat models, the combination therapy with SO and PG may provide better neuroprotective effects such as higher neuronal cell survival and inhibition of astrocytes expansion than monotherapy with SO alone