Online Influence Maximization (Extended Version)

Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.Comment: 13 pages. To appear in KDD 2015. Extended versio

Cleaning algorithms for novel applications

The information managed in emerging applications, such as location-based service, sensor network, and crowdsourcing system, is usually imperfect. In many situations, data can be cleaned (e.g., removed or reduced) by performing appropriate operations. In this thesis, we study the cleaning problem under limited resources for two novel applications: querying probabilistic data, and collecting data from human intelligence tasks in crowdsourcing environments. Probabilistic databases have been developed to handle uncertain data recently. For example, the temperature readings in a sensor network may be uncertain due to the lack of latest readings from sensors at every moment. A probabilistic database is able to capture the real value distributions of the readings, and enables evaluation of probabilistic queries on the data. However, data uncertainty may lead to ambiguous query results. By performing cleaning operations on the data, for example, probing some sensors for their latest readings, the ambiguity in query results can be reduced. In this thesis, we first study how to quantify the ambiguity of query results returned by a probabilistic top-k query. We develop efficient algorithms to compute the quality of this query under the possible world semantics. We further address the cleaning of a probabilistic database in order to improve top-k query quality. Specifically, we consider the facts that cleaning may involve a cost and fail. We propose optimal cleaning algorithms as well as several heuristics to select the data to clean under a limited budget. In a crowdsourcing system, Human Intelligence Tasks (HITs) (e.g., translating sentences, matching photos, tagging videos with keywords) can be conveniently specified to collect data. HITs are made available to a large pool of workers, who are paid upon completing the HITs they have selected. Since workers may be casual Internet users, their answers are hardly perfect. If more workers are employed to perform a HIT, the quality of the HIT’s answer could be statistically improved. Hence, assigning the number of workers (or plurality) of each HIT is an effective way to reduce (or clean) the imperfectness of the collected data (i.e., HITs answers). In this thesis, we address the important problem of determining the plurality of each HIT so that the overall answer quality is optimized. We propose a dynamic programming (DP) algorithm for solving the plurality assignment problem (PAP). We identify two interesting properties, namely, monotonicity and diminishing return, which are satisfied by a HIT if the quality of the HIT’s answer increases monotonically at a decreasing rate with its plurality. We show for HITs that satisfy the two properties (e.g., multiple-choice-question HITs), the PAP is approximable. We propose an efficient greedy algorithm for such case.published_or_final_versionComputer ScienceDoctoralDoctor of Philosoph

On incentive-based tagging

Abstract—A social tagging system, such as del.icio.us and Flickr, allows users to annotate resources (e.g., web pages and photos) with text descriptions called tags. Tags have proven to be invaluable information for searching, mining, and recommending resources. In practice, however, not all resources receive the same attention from users. As a result, while some highly-popular resources are over-tagged, most of the resources are under-tagged. Incomplete tagging on resources severely affects the effectiveness of all tag-based techniques and applications. We address an interesting question: if users are paid to tag specific resources, how can we allocate incentives to resources in a crowd-sourcing environment so as to maximize the tagging quality of resources? We address this question by observing that the tagging quality of a resource becomes stable after it has been tagged a sufficient number of times. We formalize the concepts of tagging quality (TQ) and tagging stability (TS) in measuring the quality of a resource’s tag description. We propose a theoretically optimal algorithm given a fixed “budget ” (i.e., the amount of money paid for tagging resources). This solution decides the amount of rewards that should be invested on each resource in order to maximize tagging stability. We further propose a few simple, practical, and efficient incentive allocation strategies. On a dataset from del.icio.us, our best strategy provides resources with a close-to-optimal gain in tagging stability. I

Microelectrode Arrays for Detection of Neural Activity in Depressed Rats: Enhanced Theta Activity in the Basolateral Amygdala

Depression is a common and severely debilitating neuropsychiatric disorder. Multiple studies indicate a strong correlation between the occurrence of immunological inflammation and the presence of depression. The basolateral amygdala (BLA) is crucial in the cognitive and physiological processing and control of emotion. However, due to the lack of detection tools, the neural activity of the BLA during depression is not well understood. In this study, a microelectrode array (MEA) based on the shape and anatomical location of the BLA in the brain was designed and manufactured. Rats were injected with lipopolysaccharide (LPS) for 7 consecutive days to induce depressive behavior. We used the MEA to detect neural activity in the BLA before modeling, during modeling, and after LPS administration on 7 consecutive days. The results showed that after LPS treatment, the spike firing of neurons in the BLA region of rats gradually became more intense, and the local field potential power also increased progressively. Further analysis revealed that after LPS administration, the spike firing of BLA neurons was predominantly in the theta rhythm, with obvious periodic firing characteristics appearing after the 7 d of LPS administration, and the relative power of the local field potential in the theta band also significantly increased. In summary, our results suggest that the enhanced activity of BLA neurons in the theta band is related to the depressive state of rats, providing valuable guidance for research into the neural mechanisms of depression

High-Throughput Microelectrode Arrays for Precise Functional Localization of the Globus Pallidus Internus

The globus pallidus internus (GPi) was considered a common target for stimulation in Parkinson’s disease (PD). Located deep in the brain and of small size, pinpointing it during surgery is challenging. Multi-channel microelectrode arrays (MEAs) can provide micrometer-level precision functional localization, which can maximize the surgical outcome. In this paper, a 64-channel MEA modified by platinum nanoparticles with a detection site impedance of 61.1 kΩ was designed and prepared, and multiple channels could be synchronized to cover the target brain region and its neighboring regions so that the GPi could be identified quickly and accurately. The results of the implant trajectory indicate that, compared to the control side, there is a reduction in local field potential (LFP) power in multiple subregions of the upper central thalamus on the PD-induced side, while the remaining brain regions exhibit an increasing trend. When the MEA tip was positioned at 8,700 μm deep in the brain, the various characterizations of the spike signals, combined with the electrophysiological characteristics of the β-segmental oscillations in PD, enabled MEAs to localize the GPi at the single-cell level. More precise localization could be achieved by utilizing the distinct characteristics of the internal capsule (ic), the thalamic reticular nucleus (Rt), and the peduncular part of the lateral hypothalamus (PLH) brain regions, as well as the relative positions of these brain structures. The MEAs designed in this study provide a new detection method and tool for functional localization of PD targets and PD pathogenesis at the cellular level

SWIR Fluorescence Imaging In Vivo Monitoring and Evaluating Implanted M2 Macrophages in Skeletal Muscle Regeneration

Skeletal muscle has a robust regeneration ability that is impaired by severe injury, disease, and aging, resulting in a decline in skeletal muscle function. Therefore, improving skeletal muscle regeneration is a key challenge in treating skeletal muscle-related disorders. Owing to their significant role in tissue regeneration, implantation of M2 macrophages (M2Mø) has great potential for improving skeletal muscle regeneration. Here, we present a short-wave infrared (SWIR) fluorescence imaging technique to obtain more in vivo information for an in-depth evaluation of the skeletal muscle regeneration effect after M2Mø transplantation. SWIR fluorescence imaging was employed to track implanted M2Mø in the injured skeletal muscle of mouse models. It is found that the implanted M2Mø accumulated at the injury site for two weeks. Then, SWIR fluorescence imaging of blood vessels showed that M2Mø implantation could improve the relative perfusion ratio on day 5 (1.09 ± 0.09 vs 0.85 ± 0.05; p = 0.01) and day 9 (1.38 ± 0.16 vs 0.95 ± 0.03; p = 0.01) post-injury, as well as augment the degree of skeletal muscle regeneration on day 13 post-injury. Finally, multiple linear regression analyses determined that post-injury time and relative perfusion ratio could be used as predictive indicators to evaluate skeletal muscle regeneration. These results provide more in vivo details about M2Mø in skeletal muscle regeneration and confirm that M2Mø could promote angiogenesis and improve the degree of skeletal muscle repair, which will guide the research and development of M2Mø implantation to improve skeletal muscle regeneration

Impaired Spatial Firing Representations of Neurons in the Medial Entorhinal Cortex of the Epileptic Rat Using Microelectrode Arrays

Epilepsy severely impairs the cognitive behavior of patients. It remains unclear whether epilepsy-induced cognitive impairment is associated with neuronal activities in the medial entorhinal cortex (MEC), a region known for its involvement in spatial cognition. To explore this neural mechanism, we recorded the spikes and local field potentials from MEC neurons in lithium–pilocarpine-induced epileptic rats using self-designed microelectrode arrays. Through the open field test, we identified spatial cells exhibiting spatially selective firing properties and assessed their spatial representations in relation to the progression of epilepsy. Meanwhile, we analyzed theta oscillations and theta modulation in both excitatory and inhibitory neurons. Furthermore, we used a novel object recognition test to evaluate changes in spatial cognitive ability of epileptic rats. After the epilepsy modeling, the spatial tuning of various types of spatial cells had suffered a rapid and pronounced damage during the latent period (1 to 5 d). Subsequently, the firing characteristics and theta oscillations were impaired. In the chronic period (>10 d), the performance in the novel object experiment deteriorated. In conclusion, our study demonstrates the detrimental effect on spatial representations and electrophysiological properties of MEC neurons in the epileptic latency, suggesting the potential use of these changes as a “functional biomarker” for predicting cognitive impairment caused by epilepsy

DataSheet1_Utilizing GO/PEDOT:PSS/PtNPs-enhanced high-stability microelectrode arrays for investigating epilepsy-induced striatal electrophysiology alterations.PDF

The striatum plays a crucial role in studying epilepsy, as it is involved in seizure generation and modulation of brain activity. To explore the complex interplay between the striatum and epilepsy, we engineered advanced microelectrode arrays (MEAs) specifically designed for precise monitoring of striatal electrophysiological activities in rats. These observations were made during and following seizure induction, particularly three and 7 days post-initial modeling. The modification of graphene oxide (GO)/poly (3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS)/platinu-m nanoparticles (PtNPs) demonstrated a marked reduction in impedance (10.5 ± 1.1 kΩ), and maintained exceptional stability, with impedance levels remaining consistently low (23 kΩ) even 14 days post-implantation. As seizure intensity escalated, we observed a corresponding increase in neuronal firing rates and local field potential power, with a notable shift towards higher frequency peaks and augmented inter-channel correlation. Significantly, during the grand mal seizures, theta and alpha bands became the dominant frequencies in the local field potential. Compared to the normal group, the spike firing rates on day 3 and 7 post-modeling were significantly higher, accompanied by a decreased firing interval. Power in both delta and theta bands exhibited an increasing trend, correlating with the duration of epilepsy. These findings offer valuable insights into the dynamic processes of striatal neural activity during the initial and latent phases of temporal lobe epilepsy and contribute to our understanding of the neural mechanisms underpinning epilepsy.</p